NURSING INFORMATICS and the Foundation of Knowledge

The Pedagogy Nursing Informatics and the Foundation of Knowledge, Fourth Edition drives comprehension through a variety of strategies geared toward meeting the learning needs of students, while also generating enthusiasm about the topic. This interactive approach addresses diverse learning styles, making this the ideal text to ensure mastery of key concepts. The pedagogical aids that appear in most chapters include the following:

Key Terms » Accessibility » Cognitive activity » Data » Data gatherer » Enumerative

approach » Expert systems

» Industrial Age » Information » Information Age » Information user » International

Classification of Nursing Practice

» Knowledge » Knowledge

builder » Knowledge user » Knowledge worker » Ontological


» Reusability » Standardized Nurs-

ing Terminology » Technologist » Terminology » Ubiquity » Wisdom

1. Trace the evolution of nursing informatics from concept to specialty practice.

2. Relate nursing informatics metastructures, con- cepts, and tools to the knowledge work of nursing.

3. Explore the quest for consistent terminology in nursing and describe terminology approaches that

accurately capture and codify the contributions of nursing to health care.

4. Explore the concept of nurses as knowledge workers.

5. Explore how nurses can create and derive clinical knowledge from information systems.


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Introduction Those who followed the actual events of Apollo 13, or who were enter- tained by the movie (Howard, 1995), watched the astronauts strive against all odds to bring their crippled spaceship back to Earth. The speed of their travel was incomprehensible to most viewers, and the task of bringing the spaceship back to Earth seemed nearly impossible. They were experienc- ing a crisis never imagined by the experts at NASA, and they made up their survival plan moment by moment. What brought them back to Earth safely? Surely, credit must be given to the technology and the spaceship’s ability to withstand the trauma it experienced. Most amazing, however, were the traditional nontechnological tools, skills, and supplies that were used in new and different ways to stabilize the spacecraft’s environment and keep the astronauts safe while traveling toward their uncertain future.

This sense of constancy in the midst of change serves to stabilize experi- ence in many different life events and contributes to the survival of crisis and change. This rhythmic process is also vital to the healthcare system’s stability and survival in the presence of the rapidly changing events of the Knowledge Age. No one can dispute the fact that the Knowledge Age is changing health care in ways that will not be fully recognized and under- stood for years. The change is paradigmatic, and every expert who ad- dresses this change reminds healthcare professionals of the need to go with the fl ow of rapid change or be left behind.

As with any paradigm shift, a new way of viewing the world brings with it some of the enduring values of the previous worldview. As health care continues its journey into digital communications, telehealth, and wearable technologies, it brings some familiar tools and skills recognized in the form of values, such as privacy, confi dentiality, autonomy, and nonma- lefi cence. Although these basic values remain unchanged, the standards for living out these values will take on new meaning as health professionals confront new and different moral dilemmas brought on by the adoption

Ethical applications of Informatics Dee McGonigle, Kathleen Mastrian, and Nedra Farcus


ChapTEr 5

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Key Terms Found in a list at the beginning of each chapter, studying these terms will create an expanded vocabulary.

Objectives Providing a snapshot of the key information encountered in each chapter, the objectives serve as a checklist to help guide and focus study. Objectives can also be found within the text’s online resources.

Introductions Found at the beginning of each chapter, the introductions provide an overview highlighting the importance of the chapter’s topic. They also help keep students focused as they read.

Key Terms » Artificial

intelligence » Brain » Cognitive

informatics » Cognitive science » Computer science

» Connectionism » Decision making » Empiricism » Epistemology » Human Mental

Workload (MWL) » Intelligence

» Intuition » Knowledge » Logic » Memory » Mind » Neuroscience » Perception

» Problem solving » Psychology » Rationalism » Reasoning » Wisdom

1. Describe cognitive science. 2. Assess how the human mind processes and gener-

ates information and knowledge.

3. Explore cognitive informatics. 4. Examine artificial intelligence and its relationship

to cognitive science and computer science.


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Summaries Summaries are included at the end of each chapter to provide a concise review of the material covered, highlighting the most important points and describing what the future holds.

uncertainty to the situational factors and personal beliefs that must be considered cre- ates a need for an ethical decision-making model to help one choose the best action.

Ethical Decision Making Ethical decision making refers to the process of making informed choices about ethical dilemmas based on a set of standards differentiating right from wrong. This type of decision making reflects an understanding of the principles and standards of ethical decision making, as well as the philosophic approaches to ethical decision making, and it requires a systematic framework for addressing the complex and often contro- versial moral questions.

As the high-speed era of digital communications evolves, the rights and the needs of individuals and groups will be of the utmost concern to all healthcare profession- als. The changing meaning of communication, for example, will bring with it new concerns among healthcare professionals about protecting patients’ rights of confi- dentiality, privacy, and autonomy. Systematic and flexible ethical decision-making abilities will be essential for all healthcare professionals.

Notably, the concept of nonmaleficence (“do no harm”) will be broadened to include those individuals and groups whom one may never see in person, but with whom one will enter into a professional relationship of trust and care. Mack (2000)

82 ChapTEr 5 Ethical Applications of Informatics


Using an online survey of 1,227 randomly selected respondents, Bodkin and Miaoulis (2007) sought to describe the characteristics of information seekers on e-health websites, the types of information they seek, and their perceptions of the quality and ethics of the websites. Of the respondents, 74% had sought health in- formation on the Web, with women accounting for 55.8% of the health informa- tion seekers. A total of 50% of the seekers were between 35 and 54 years of age. Nearly two thirds of the users began their searches using a general search engine rather than a health-specific site, unless they were seeking information related to symptoms or diseases. Top reasons for seeking information were related to dis- eases or symptoms of medical conditions, medication information, health news, health insurance, locating a doctor, and Medicare or Medicaid information. The level of education of information seekers was related to the ratings of website quality, in that more educated seekers found health information websites more understandable, but were more likely to perceive bias in the website information. The researchers also found that the ethical codes for e-health websites seem to be increasing consumers’ trust in the safety and quality of information found on the Web, but that most consumers are not comfortable purchasing health products or services online.

The full article appears in Bodkin, C., & Miaoulis, G. (2007). eHealth information quality and ethics issues: An exploratory study of consumer perceptions. International Journal of Pharmaceuti- cal and Healthcare Marketing, 1(1), 27–42. Retrieved from ABI/INFORM Global (Document ID: 1515583081).

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practices are sometimes more harmful than beneficial). A case in point is the long-standing practice of instilling endotracheal tubes with normal saline before suctioning (O’Neal, Grap, Thompson, & Dudley, 2001). Based on the evidence gathered through several studies, the potentially deleterious effects of this practice have become widely recognized. Conceivably, a meta-analysis approach to clinical studies will be expedited by convergence of large clinical data repositories across care settings, thereby making available to practitioners the collective contribu- tions of health professionals and longitudinal outcomes for individuals, families, and populations.

Nurses need to be engaged in the design of CIS tools that support access to and the generation of nursing knowledge. As we have emphasized, the adoption of clini- cal data standards is of particular importance to the future design of CIS tools. We are also beginning to see the development and use of expert systems that implement knowledge automatically without human intervention. For example, an insulin pump that senses the patient’s blood glucose level and administers insulin based on those data is a form of expert system. The expert system differs from decision support tools in that the decision support tools require the human to act on the information pro- vided, whereas the expert system intervenes automatically based on an algorithm that directs the intervention. Consider that as CISs are widely implemented, as standards for nursing documentation and reporting are adopted, and as healthcare IT solutions continue to evolve, the synthesis of findings from a variety of methods and world- views becomes much more feasible.


In the year 2025, nursing practice enabled by technology has created a profes- sional culture of reflection, critical inquiry, and interprofessional collaboration. Nurses use technology at the point of care in all clinical settings (e.g., primary care, acute care, community, and long-term care) to inform their clinical deci- sions and effect the best possible outcomes for their clients. Information is gath- ered and retrieved via human–technology biometric interfaces including voice, visual, sensory, gustatory, and auditory interfaces, which continuously monitor physiologic parameters for potentially harmful imbalances. Longitudinal records are maintained for all citizens from their initial prenatal assessment to death; all lifelong records are aggregated into the knowledge bases of expert systems. These systems provide the basis of the artificial intelligence being embedded in emerging technologies. Smart technologies and invisible computing are ubiqui- tous in all sectors where care is delivered. Clients and families are empowered to review and contribute actively to their record of health and wellness. Invasive diagnostic techniques are obsolete, nanotechnology therapeutics are the norm, and robotics supplement or replace much of the traditional work of all health professions. Nurses provide expertise to citizens to help them effectively manage their health and wellness life plans, and navigate access to appropriate informa- tion and services.

122 ChaPEr 6 History and Evolution of Nursing Informatics

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The Future The future landscape is yet to be fully understood, as technology continues to evolve with a rapidity and unfolding that is rich with promise and potential peril. Box 6-3 helps us to imagine what future practice might entail. It is anticipated that computing power will be capable of aggregating and transforming additional multidimensional data and information sources (e.g., historical, multisensory, experiential, and genetic sources) into CIS. With the availability of such rich repositories, further opportunities will open up to enhance the training of health professionals, advance the design and application of CDSs, deliver care that is informed by the most current evidence, and engage with individuals and families in ways yet unimagined.

The basic education of all health professions will evolve over the next decade to incorporate core informatics competencies. In general, the clinical care environments will be connected, and information will be integrated across disciplines to the benefit of care providers and citizens alike. The future of health care will be highly dependent on the use of CISs and CDSs to achieve the global aspiration of safer, quality care for all citizens.

The ideal is a nursing practice that has wholly integrated informatics and nursing education and that is driven by the use of information and knowledge from a myriad of sources, creating practitioners whose way of being is grounded in informatics. Nursing research is dynamic and an enterprise in which all nurses are engaged by virtue of their use of technologies to gather and analyze findings that inform specific clinical situations. In every practice setting, the contributions of nurses to health and well-being of citizens will be highly respected and parallel, if not exceed, the preemi- nence granted physicians.

Summary In this chapter, we have traced the development of informatics as a specialty, defined nursing informatics, and explored the DIKW paradigm central to informatics. We also explored the need for and the development of standardized terminologies to capture and codify the work of nursing and how informatics supports the knowledge work of nursing. This chapter advanced the view that every nurse’s practice will make contributions to new nursing knowledge in dynamically interactive CIS environ- ments. The core concepts associated with informatics will become embedded in the practice of every nurse, whether administrator, researcher, educator, or practitioner. Informatics will be prominent in the knowledge work of nurses, yet it will be a sub- tlety because of its eventual fulsome integration with clinical care processes. Clinical care will be substantially supported by the capacity and promise of technology today and tomorrow.

Most importantly, readers need to contemplate a future without being limited by the world of practice as it is known today. Information technology is not a panacea for all of the challenges found in health care, but it will provide the nursing profes- sion with an unprecedented capacity to generate and disseminate new knowledge at rapid speed. Realizing these possibilities necessitates that all nurses understand and leverage the informatician within and contribute to the future.

Summary 123

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This text is designed to include the necessary content to prepare nurses for prac- tice in the ever-changing and technology-laden healthcare environments. Informatics competence has been recognized as necessary in order to enhance clinical decision making and improve patient care for many years. This is evidenced by Goossen (2000), who reflected on the need for research in this area and believed that the focus of nursing informatics research should be on the structuring and processing of patient information and the ways that these endeavors inform nursing decision mak- ing in clinical practice. The increased use of technology to enhance nursing practice, nursing education, and nursing research will open new avenues for acquiring, pro- cessing, generating, and disseminating knowledge.

In the future, nursing research will make significant contributions to the devel- opment of nursing science. Technologies and translational research will abound, and clinical practices will continue to be evidence based, thereby improving patient outcomes and decreasing safety concerns. Schools of nursing will embrace nursing science as they strive to meet the needs of changing student populations and the increasing complexity of healthcare environments.

Summary Nursing science influences all areas of nursing practice. This chapter provided an overview of nursing science and considered how nursing science relates to typical nursing practice roles, nursing education, informatics, and nursing research. The Foundation of Knowledge model was introduced as the organizing conceptual framework for this text. Finally, the relationship of nursing science to nursing informatics was discussed. In subsequent chapters the reader will learn more about how nursing informatics supports nurses in their many and varied roles. In  an ideal world, nurses would embrace nursing science as knowledge users, knowledge managers, knowledge developers, knowledge engineers, and knowl- edge workers.


1. Imagine you are in a social situation and someone asks you, “What does a nurse do?” Think about how you will capture and convey the richness that is nursing science in your answer.

2. Choose a clinical scenario from your recent experience and analyze it using the Foundation of Knowledge model. How did you acquire knowledge? How did you process knowledge? How did you generate knowledge? How did you dis- seminate knowledge? How did you use feedback, and what was the effect of the feedback on the foundation of your knowledge?

18 ChapTer 1 Nursing Science and the Foundation of Knowledge

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Research Briefs These summaries encourage students to access current research in the field.

Thought-Provoking Questions Students can work on these critical thinking assign­ ments individually or in a group. In addition, students can delve deeper into concepts by completing these exercises online.

Case Studies Case studies encourage active learning and promote critical think­ ing skills. Students can ask questions, analyze situations, and solve problems in a real­world context.


Dee McGonigle, PhD, RN, CNE, FAAN, ANEF Director, Virtual Learning Experiences (VLE) and Professor Graduate Program, Chamberlain College of Nursing Member, Informatics and Technology Expert Panel (ITEP) for the American Academy of Nursing

Kathleen Mastrian, PhD, RN Associate Professor and Program Coordinator for Nursing Pennsylvania State University, Shenango Sr. Managing Editor, Online Journal of Nursing Informatics (OJNI)

NURSING INFORMATICS and the Foundation of Knowledge

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Library of Congress Cataloging-in-Publication Data Names: McGonigle, Dee, editor. | Mastrian, Kathleen Garver, editor. Title: Nursing informatics and the foundation of knowledge/[edited by] Dee McGonigle, Kathleen Mastrian. Description: Fourth edition. | Burlington, MA: Jones & Bartlett Learning, [2018] | Includes bibliographical references and index. Identifiers: LCCN 2016043838 | ISBN 9781284121247 (pbk.) Subjects: | MESH: Nursing Informatics | Knowledge Classification: LCC RT50.5 | NLM WY 26.5 | DDC 651.5/04261--dc23

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Special Acknowledgments

We want to express our sincere appreciation to the staff at Jones & Bartlett Learning, especially Amanda, Christina, and Carolyn, for their continued encouragement, assistance, and support during the writing process and publication of our book.


Preface xvi Acknowledgments xix Contributors xxi


1 Nursing Science and the Foundation of Knowledge 7 Dee McGonigle and Kathleen Mastrian Introduction 7 Quality and Safety Education for Nurses 16 Summary 18 References 19

2 Introduction to Information, Information Science, and Information Systems 21 Kathleen Mastrian and Dee McGonigle Introduction 21 Information 22 Information Science 25 Information Processing 26 Information Science and the Foundation of Knowledge 27 Introduction to Information Systems 28 Summary 32 References 33

3 Computer Science and the Foundation of Knowledge Model 35 Dee McGonigle, Kathleen Mastrian, and June Kaminski Introduction 35 The Computer as a Tool for Managing Information and Generating Knowledge 36 Components 38 What Is the Relationship of Computer Science to Knowledge? 53 How Does the Computer Support Collaboration and Information Exchange? 54 Cloud Computing 57 Looking to the Future 59 Summary 61 Working Wisdom 61 Application Scenario 62 References 62



4 Introduction to Cognitive Science and Cognitive Informatics 65 Kathleen Mastrian and Dee McGonigle Introduction 65 Cognitive Science 65 Sources of Knowledge 68 Nature of Knowledge 69 How Knowledge and Wisdom Are Used in Decision Making 69 Cognitive Informatics 70 Cognitive Informatics and Nursing Practice 71 What Is AI? 72 Summary 73 References 74

5 Ethical Applications of Informatics 77 Dee McGonigle, Kathleen Mastrian, and Nedra Farcus Introduction 77 Ethics 78 Bioethics 79 Ethical Issues and Social Media 80 Ethical Dilemmas and Morals 81 Ethical Decision Making 82 Theoretical Approaches to Healthcare Ethics 83 Applying Ethics to Informatics 86 Case Analysis Demonstration 91 New Frontiers in Ethical Issues 95 Summary 96 References 97


6 History and Evolution of Nursing Informatics 105 Kathleen Mastrian and Dee McGonigle Introduction 105 The Evolution of a Specialty 106 What Is Nursing Informatics? 108 The DIKW Paradigm 109 Capturing and Codifying the Work of Nursing 112 The Nurse as a Knowledge Worker 117 The Future 123 Summary 123 References 124

7 Nursing Informatics as a Specialty 127 Dee McGonigle, Kathleen Mastrian, Julie A. Kenney, and Ida Androwich Introduction 127 Nursing Contributions to Healthcare Informatics 127

Contents ix

Scope and Standards 128 Nursing Informatics Roles 129 Specialty Education and Certification 131 Nursing Informatics Competencies 133 Rewards of NI Practice 138 NI Organizations and Journals 138 The Future of Nursing Informatics 139 Summary 141 References 142

8 Legislative Aspects of Nursing Informatics: HITECH and HIPAA 145 Kathleen M. Gialanella, Kathleen Mastrian, and Dee McGonigle Introduction 145 HIPAA Came First 145 Overview of the HITECH Act 148 How a National HIT Infrastructure Is Being Developed 153 How the HITECH Act Changed HIPAA 154 Implications for Nursing Practice 161 Future Regulations 165 Summary 165 References 166


9 Systems Development Life Cycle: Nursing Informatics and Organizational Decision Making 175 Dee McGonigle and Kathleen Mastrian Introduction 175 Waterfall Model 178 Rapid Prototyping or Rapid Application Development 180 Object-Oriented Systems Development 181 Dynamic System Development Method 181 Computer-Aided Software Engineering Tools 184 Open Source Software and Free/Open Source Software 184 Interoperability 185 Summary 186 References 187

10 Administrative Information Systems 189 Marianela Zytkowski, Susan Paschke, Kathleen Mastrian, and Dee McGonigle Introduction 189 Types of Healthcare Organization Information Systems 190 Communication Systems 190 Core Business Systems 191 Order Entry Systems 193 Patient Care Support Systems 194

x Contents

Interoperability 195 Aggregating Patient and Organizational Data 197 Department Collaboration and Exchange of Knowledge and Information 202 Summary 203 References 204

11 The Human–Technology Interface 207 Dee McGonigle, Kathleen Mastrian, and Judith A. Effken Introduction 207 The Human–Technology Interface 208 The Human–Technology Interface Problem 211 Improving the Human–Technology Interface 212 A Framework for Evaluation 221 Future of the Human–Technology Interface 221 Summary 223 References 224

12 Electronic Security 229 Lisa Reeves Bertin, Kathleen Mastrian, and Dee McGonigle Introduction 229 Securing Network Information 229 Authentication of Users 231 Threats to Security 232 Security Tools 237 Offsite Use of Portable Devices 238 Summary 241 References 242

13 Workflow and Beyond Meaningful Use 245 Dee McGonigle, Kathleen Mastrian, and Denise Hammel-Jones Introduction 245 Workflow Analysis Purpose 245 Workflow and Technology 249 Workflow Analysis and Informatics Practice 251 Informatics as a Change Agent 256 Measuring the Results 258 Future Directions 259 Summary 260 References 261


14 The Electronic Health Record and Clinical Informatics 267 Emily B. Barey, Kathleen Mastrian, and Dee McGonigle Introduction 267 Setting the Stage 268

Contents xi

Components of Electronic Health Records 269 Advantages of Electronic Health Records 274 Standardized Terminology and the EHR 278 Ownership of Electronic Health Records 280 Flexibility and Expandability 283 Accountable Care Organizations and the EHR 285 The Future 285 Summary 287 References 287

15 Informatics Tools to Promote Patient Safety and Quality Outcomes 293 Dee McGonigle and Kathleen Mastrian Introduction 293 What Is a Culture of Safety? 294 Strategies for Developing a Safety Culture 296 Informatics Technologies for Patient Safety 301 Role of the Nurse Informaticist 313 Summary 315 References 317

16 Patient Engagement and Connected Health 323 Kathleen Mastrian and Dee McGonigle Introduction 323 Consumer Demand for Information 324 Health Literacy and Health Initiatives 325 Healthcare Organization Approaches to Engagement 327 Promoting Health Literacy in School-Aged Children 329 Supporting Use of the Internet for Health Education 330 Future Directions for Engaging Patients 335 Summary 337 References 338

17 Using Informatics to Promote Community/Population Health 341 Dee McGonigle, Kathleen Mastrian, Margaret Ross Kraft, and Ida Androwich Introduction 341 Core Public Health Functions 343 Community Health Risk Assessment: Tools for Acquiring Knowledge 345 Processing Knowledge and Information to Support Epidemiology and Monitoring Disease Outbreaks 347 Applying Knowledge to Health Disaster Planning and Preparation 349 Informatics Tools to Support Communication and Dissemination 350 Using Feedback to Improve Responses and Promote Readiness 351 Summary 353 References 355

xii Contents

18 Telenursing and Remote Access Telehealth 359 Original contribution by Audrey Kinsella, Kathleen Albright, Sheldon Prial, and Schuyler F. Hoss; revised by Kathleen Mastrian and Dee McGonigle Introduction 359 The Foundation of Knowledge Model and Home Telehealth 359 Nursing Aspects of Telehealth 361 History of Telehealth 362 Driving Forces for Telehealth 363 Telehealth Care 366 Telenursing 370 Telehealth Patient Populations 372 Tools of Home Telehealth 375 Home Telehealth Software 378 Home Telehealth Practice and Protocols 380 Legal, Ethical, and Regulatory Issues 381 The Patient’s Role in Telehealth 382 Telehealth Research 383 Evolving Telehealth Models 385 Parting Thoughts for the Future and a View Toward What the Future Holds 386 Summary 387 References 388


19 Nursing Informatics and Nursing Education 397 Heather E. McKinney, Sylvia DeSantis, Kathleen Mastrian, and Dee McGonigle Introduction: Nursing Education and the Foundation of Knowledge Model 397 Knowledge Acquisition and Sharing 398 Evolution of Learning Management Systems 398 Delivery Modalities 400 Technology Tools Supporting Education 405 Internet-Based Tools 413 Promoting Active and Collaborative Learning 420 Knowledge Dissemination and Sharing 423 Exploring Information Fair Use and Copyright Restrictions 426 The Future 427 Summary 428 References 429

20 Simulation, Game Mechanics, and Virtual Worlds in Nursing Education 433 Dee McGonigle, Kathleen Mastrian, Brett Bixler, and Nickolaus Miehl Introduction 433 Simulation in Nursing Informatics Education 434 Nursing Informatics Competencies in Nursing Education 436 A Case for Simulation in Nursing Informatics Education and Nursing Education 437

Contents xiii

Incorporating EHRs into the Learning Environment 441 Challenges and Opportunities 445 The Future of Simulation in Nursing Informatics Education 445 Game Mechanics and Virtual World Simulation for Nursing Education 446 Game Mechanics and Educational Games 448 Virtual Worlds in Education 450 Choosing Among Simulations, Educational Games, and Virtual Worlds 451 The Future of Simulations, Games, and Virtual Worlds in Nursing Education 452 Summary 453 References 454


21 Nursing Research: Data Collection, Processing, and Analysis 463 Heather E. McKinney, Sylvia DeSantis, Kathleen Mastrian, and Dee McGonigle Introduction: Nursing Research and the Foundation of Knowledge Model 463 Knowledge Generation Through Nursing Research 464 Acquiring Previously Gained Knowledge Through Internet and Library Holdings 466 Fair Use of Information and Sharing 468 Informatics Tools for Collecting Data and Storage of Information 469 Tools for Processing Data and Data Analysis 471 The Future 473 Summary 473 References 474

22 Data Mining as a Research Tool 477 Dee McGonigle and Kathleen Mastrian Introduction: Big Data, Data Mining, and Knowledge Discovery 477 KDD and Research 481 Data Mining Concepts 482 Data Mining Techniques 483 Data Mining Models 486 Benefits of KDD 489 Data Mining and Electronic Health Records 490 Ethics of Data Mining 491 Summary 491 References 492

23 Translational Research: Generating Evidence for Practice 495 Jennifer Bredemeyer, Ida Androwich, Dee McGonigle, and Kathleen Mastrian Introduction 495 Clarification of Terms 495 History of Evidence-Based Practice 498 Evidence 498 Bridging the Gap Between Research and Practice 499 Barriers to and Facilitators of Evidence-Based Practice 500 The Role of Informatics 500

xiv Contents

Developing EBP Guidelines 503 Meta-Analysis and Generation of Knowledge 504 The Future 505 Summary 506 References 507

24 Bioinformatics, Biomedical Informatics, and Computational Biology 511 Dee McGonigle and Kathleen Mastrian Introduction 511 Bioinformatics, Biomedical Informatics, and Computational Biology Defined 511 Why Are Bioinformatics and Biomedical Informatics So Important? 514 What Does the Future Hold? 516 Summary 518 References 519


25 The Art of Caring in Technology-Laden Environments 525 Kathleen Mastrian and Dee McGonigle Introduction 525 Caring Theories 526 Presence 529 Strategies for Enhancing Caring Presence 530 Reflective Practice 533 Summary 534 References 535

26 Nursing Informatics and the Foundation of Knowledge 537 Dee McGonigle and Kathleen Mastrian Introduction 537 Foundation of Knowledge Revisited 537 The Nature of Knowledge 539 Knowledge Use in Practice 541 Characteristics of Knowledge Workers 544 Knowledge Management in Organizations 545 Managing Knowledge Across Disciplines 547 The Learning Healthcare System 548 Summary 550 References 551

Abbreviations 553 Glossary 556 Index 586

Contents xv


The idea for this text originated with the development of nursing informatics (NI) classes, the publication of articles related to technology-based education, and the creation of the Online Journal of Nursing Infor- matics (OJNI), which Dee McGonigle cofounded with Renee Eggers. Like most nurse informaticists, we fell into the specialty; our love affair with technology and gadgets and our willingness to be the first to try new things helped to hook us into the specialty of informatics. The rapid evolution of technology and its transformation of the ways of nursing prompted us to try to capture the essence of NI in a text.

As we were developing the first edition, we realized that we could not possibly know all there is to know about informatics and the way in which it supports nursing practice, education, administration, and research. We also knew that our faculty roles constrained our opportunities for exposure to changes in this rapidly evolving field. Therefore, we developed a tentative outline and a working model of the theoretical framework for the text and invited participation from informatics experts and specialists around the world. We were pleased with the enthusiastic responses we received from some of those invited contributors and a few volunteers who heard about the text and asked to participate in their particular area of expertise.

In the second edition, we invited the original contributors to revise and update their chapters. Not everyone chose to participate in the second edition, so we revised several of the chapters using the original work as a springboard. The revisions to the text were guided by the contributors’ growing informatics expertise and the reviews provided by textbook adopters. In the revisions, we sought to do the following:

• Expand the audience focus to include nursing students from BS through DNP programs as well as nurses thrust into informatics roles in clinical agencies.

• Include, whenever possible, an attention-grabbing case scenario as an introduction or an illustrative case scenario demonstrating why the topic is important.

• Include important research findings related to the topic. Many chapters have research briefs pre- sented in text boxes to encourage the reader to access current research.

• Focus on cutting-edge innovations, meaningful use, and patient safety as appropriate to each topic. • Include a paragraph describing what the future holds for each topic.

New chapters that were added to the second edition included those focusing on technology and patient safety, system development life cycle, workflow analysis, gaming, simulation, and bioinformatics.

In the third edition, we reviewed and updated all of the chapters, reordered some chapters for better content flow, eliminated duplicated content, split the education and research content into two sections, integrated social media content, and added two new chapters: Data Mining as a Research Tool and The Art of Caring in Technology-Laden Environments.

In this fourth edition, we reviewed and updated all of the chapters based on technological advance- ments and changes to the healthcare arena, including reimbursement mechanisms for services. We have pared this edition down to 26 chapters from the previous edition’s 29; one chapter each was deleted from Sections II, V, and VII. Section I includes updates to the same five chapters on the building blocks of nurs- ing informatics, with extensive changes to Chapter 3, Computer Science and the Foundation of Knowledge Model. To improve flow, we combined content. In Section II, the previous four chapters were narrowed to three. New Chapters 6, History and Evolution of Nursing Informatics and 7, Nursing Informatics as


a Specialty, were developed and appropriate material from previous Chapters 6, 7, and 8 were assimi- lated. This section ends with an updated Chapter 8, Legislative Aspects of Nursing Informatics: HITECH and HIPAA (formerly Chapter 9). Section III contains the same five chapters, although all were updated and Chapter 13, Workflow and Beyond Meaningful Use (formerly Chapter 14) now reflects the payment models and reimbursement issues that we are adjusting to after meaningful use has gone away. Section IV contains the same five chapters with updated content and some name changes to reflect the current status of informatics and healthcare. Chapter 15 was renamed to Informatics Tools to Promote Patient Safety and Quality Outcomes, and Chapter 16 has been changed to Patient Engagement and Connected Health. Section V went from three chapters to two chapters: Chapter 19 (formerly Chapter 20) was updated, while the new Chapter 20, Simulation, Game Mechanics, and Virtual Worlds in Nursing Education, had content from former Chapters 21 and 22 integrated during its development. Section VI was renamed to Research Applications of Nursing Informatics. It still has the same four chapters, which have been updated, but the first chapter in this section, 21, was renamed to reflect nursing research; its new name is Nursing Research: Data Collection, Processing, and Analysis. Section VII went from three chapters to two chapters. Because emerging technologies are discussed throughout the text, the chapter focusing specifically on that was removed. The two chapters that remain are Chapter 25, The Art of Caring in Technology-Laden Environ- ments, and the new Chapter 26, Nursing Informatics and Knowledge Management. In addition, the ancil- lary materials have been updated and enhanced to include competency-based self-assessments and mapping the content to the current NI standards.

We believe that this text provides a comprehensive elucidation of this exciting field. Its theoretical under- pinning is the Foundation of Knowledge model. This model is introduced in its entirety in the first chapter (Nursing Science and the Foundation of Knowledge), which discusses nursing science and its relationship to NI. We believe that humans are organic information systems that are constantly acquiring, processing, and generating information or knowledge in both their professional and personal lives. It is their high degree of knowledge that characterizes humans as extremely intelligent, organic machines. Individuals have the ability to manage knowledge—an ability that is learned and honed from birth. We make our way through life inter- acting with our environment and being inundated with information and knowledge. We experience our envi- ronment and learn by acquiring, processing, generating, and disseminating knowledge. As we interact in our environment, we acquire knowledge that we must process. This processing effort causes us to redefine and re- structure our knowledge base and generate new knowledge. We then share (disseminate) this new knowledge and receive feedback from others. The dissemination and feedback initiate this cycle of knowledge over again, as we acquire, process, generate, and disseminate the knowledge gained from sharing and re-exploring our own knowledge base. As others respond to our knowledge dissemination and we acquire new knowledge, we engage in rethinking and reflecting on our knowledge, processing, generating, and then disseminating anew.

The purpose of this text is to provide a set of practical and powerful tools to ensure that the reader gains an understanding of NI and moves from information through knowledge to wisdom. Defining the demands of nurses and providing tools to help them survive and succeed in the Knowledge Era remains a major challenge. Exposing nursing students and nurses to the principles and tools used in NI helps to prepare them to meet the challenge of practicing nursing in the Knowledge Era while striving to improve patient care at all levels.

The text provides a comprehensive framework that embraces knowledge so that readers can develop their knowledge repositories and the wisdom necessary to act on and apply that knowledge. The text is divided into seven sections.

• Section I, Building Blocks of Nursing Informatics, covers the building blocks of NI: nursing science, information science, computer science, cognitive science, and the ethical management of information.

• Section II, Perspectives on Nursing Informatics, provides readers with a look at various viewpoints on NI and NI practice as described by experts in the field.

Preface xvii

• Section III, Nursing Informatics Administrative Applications: Precare and Care Support, covers important functions of administrative applications of NI.

• Section IV, Nursing Informatics Practice Applications: Care Delivery, covers healthcare delivery applications including electronic health records (EHRs), clinical information systems, telehealth, patient safety, patient and community education, and care management.

• Section V, Education Applications of Nursing Informatics, presents subject matter on how informat- ics supports nursing education.

• Section VI, Research Applications of Nursing Informatics, covers informatics tools to support nursing research, including data mining and bioinformatics.

• Section VII, Imagining the Future of Nursing Informatics, focuses on the future of NI, emphasizes the need to preserve caring functions in technology-laden environments, and reviews the relationship of nursing informatics to organizational knowledge management.

The introduction to each section explains the relationship between the content of that section and the Foundation of Knowledge model. This text places the material within the context of knowledge acqui- sition, processing, generation, and dissemination. It serves both nursing students (BS to DNP/PhD) and professionals who need to understand, use, and evaluate NI knowledge. As nursing professors, our major responsibility is to prepare the practitioners and leaders in the field. Because NI permeates the entire scope of nursing (practice, administration, education, and research), nursing education curricula must include NI. Our primary objective is to develop the most comprehensive and user-friendly NI text on the market to prepare nurses for current and future practice challenges. In particular, this text provides a solid ground- work from which to integrate NI into practice, education, administration, and research.

Goals of this text are as follows:

• Impart core NI principles that should be familiar to every nurse and nursing student • Help the reader understand knowledge and how it is acquired, processed, generated, and

disseminated • Explore the changing role of NI professionals • Demonstrate the value of the NI discipline as an attractive field of specialization

Meeting these goals will help nurses and nursing students understand and use fundamental NI princi- ples so that they efficiently and effectively function as current and future nursing professionals to enhance the nursing profession and improve the quality of health care. The overall vision, framework, and peda- gogy of this text offer benefits to readers by highlighting established principles while drawing out new ones that continue to emerge as nursing and technology evolve.

xviii Preface


We are deeply grateful to the contributors who provided this text with a richness and diversity of content that we could not have captured alone. Joan Humphrey provided social media content integrated throughout the text. We especially wish to acknowledge the superior work of Alicia Mastrian, graphic designer of the Foundation of Knowledge model, which serves as the theoretical framework on which this text is anchored. We could never have completed this project without the dedicated and patient efforts of the Jones & Bartlett Learning staff, especially Amanda Martin, Emma Huggard, and Christina Freitas, all of whom fielded our questions and concerns in a very professional, respectful, and timely manner.

Dee acknowledges the undying love, support, patience, and continued encouragement of her best friend and husband, Craig, and her son, Craig, who has made her so very proud. She sincerely thanks her cousins Camille, Glenn, Mary Jane, and Sonny, and her dear friends for their support and encouragement, espe- cially Renee.

Kathy acknowledges the loving support of her family: husband Chip; children Ben and Alicia; sisters Carol and Sue; and parents Robert and Rosalie Garver. She dedicates her work on this edition to her dad, Robert, who died September 17, 2016. Kathy also acknowledges those friends who understand the impor- tance of validation, especially Katie, Lisa, Kathy, Maureen, Anne, Barbara, and Sally.


This text provides an overview of nursing informatics from the perspective of diverse experts in the field, with a focus on nursing informatics and the Foundation of Knowledge model. We want our readers and students to focus on the relationship of knowledge to informatics and to embrace and maintain the caring functions of nursing—messages all too often lost in the romance with technology. We hope you enjoy the text!

Authors’ Note



Ida Androwich, PhD, RN, BC, FAAN Loyola University Chicago School of Nursing Maywood, IL

Emily Barey, MSN, RN Director of Nursing Informatics Epic Systems Corporation Madison, WI

Lisa Reeves Bertin, BS, EMBA Pennsylvania State University Sharon, PA

Brett Bixler, PhD Pennsylvania State University University Park, PA

Jennifer Bredemeyer, RN Loyola University Chicago School of Nursing Skokie, IL

Steven Brewer, PhD Assistant Professor, Administration of Justice Pennsylvania State University Sharon, PA

Sylvia M. DeSantis, MA Pennsylvania State University University Park, PA

Judith Effken, PhD, RN, FACMI University of Arizona College of Nursing Tucson, AZ

Nedra Farcus, MSN, RN Retired from Pennsylvania State University, Altoona Altoona, PA

Kathleen M. Gialanella, JD, RN, LLM Law Offices Westfield, NJ Associate Adjunct Professor Teachers College, Columbia University New York, NY Adjunct Professor Seton Hall University, College of Nursing &

School of Law South Orange & Newark, NJ

Denise Hammel-Jones, MSN, RN-BC, CLSSBB Greencastle Associates Consulting Malvern, PA

Nicholas Hardiker, PhD, RN Senior Research Fellow University of Salford School of Nursing & Midwifery Salford, UK

Glenn Johnson, MLS Pennsylvania State University University Park, PA

June Kaminski, MSN, RN Kwantlen University College Surrey, British Columbia, Canada

Julie Kenney, MSN, RNC-OB Clinical Analyst Advocate Health Care Oak Brook, IL

Margaret Ross Kraft, PhD, RN Loyola University Chicago School of Nursing Maywood, IL


Wendy L. Mahan, PhD, CRC, LPC Pennsylvania State University University Park, PA

Heather McKinney, PhD Pennsylvania State University University Park, PA

Nickolaus Miehl, MSN, RN Oregon Health Sciences University Monmouth, OR

Lynn M. Nagle, PhD, RN Assistant Professor University of Toronto Toronto, Ontario, Canada

Ramona Nelson, PhD, RN-BC, FAAN, ANEF Professor Emerita, Slippery Rock University President, Ramona Nelson Consulting Pittsburgh, PA

Nancy Staggers, PhD, RN, FAAN Professor, Informatics University of Maryland Baltimore, MD

Jeff Swain Instructional Designer Pennsylvania State University University Park, PA

Denise D. Tyler, MSN/MBA, RN-BC Implementation Specialist Healthcare Provider, Consulting ACS, a Xerox Company Dearborn, MI

The Editors also acknowledge the work of the following first edition contributors (original contributions edited by McGonigle and Mastrian for second edition):

Kathleen Albright, BA, RN Strategic Account Manager at GE Healthcare Philadelphia, PA

Schuyler F. Hoss, BA Northwest Healthcare Management Vancouver, WA

Audrey Kinsella, MA, MS Information for Tomorrow Telehealth Planning Services Asheville, NC

Peter J. Murray, PhD, RN, FBCS Coachman’s Cottage Nocton, Lincoln, UK

Susan M. Paschke, MSN, RN The Cleveland Clinic Cleveland, OH

Sheldon Prial, RPH, BS Pharmacy Sheldon Prial Consultance Melbourne, FL

Jackie Ritzko Pennsylvania State University Hazelton, PA

Marianela Zytkowsi, MSN, RN The Cleveland Clinic Cleveland, OH

xxii Contributors

section i

Building Blocks of Nursing Informatics Chapter 1 Nursing Science and the Foundation of Knowledge

Chapter 2 Introduction to Information, Information Science, and Information Systems

Chapter 3 Computer Science and the Foundation of Knowledge Model

Chapter 4 Introduction to Cognitive Science and Cognitive Informatics

Chapter 5 Ethical Applications of Informatics

Nursing professionals are information-dependent knowledge workers. As health care continues to evolve in an increasingly competitive information marketplace, professionals—that is, the knowledge workers—must be well prepared to make significant contributions by harnessing appropriate and timely information. Nurs- ing informatics (NI), a product of the scientific synthesis of information in nursing, encompasses concepts from computer science, cognitive science, information science, and nursing science. NI continues to evolve as more and more professionals access, use, and develop the information, computer, and cognitive sciences necessary to advance nursing science for the betterment of patients and the profession. Regard- less of their future roles in the healthcare milieu, it is clear that nurses need to understand the ethical application of computer, information, and cognitive sciences to advance nursing science.

To implement NI, one must view it from the perspective of both the current healthcare delivery system and specific, individual organizational needs, while antici- pating and creating future applications in both the healthcare system and the nursing profession. Nursing professionals should be expected to discover opportunities to use NI, participate in the design of solutions, and be challenged to identify, develop, evaluate, modify, and enhance applications to improve patient care. This text is designed to provide the reader with the information and knowledge needed to meet this expectation.

Section I presents an overview of the building blocks of NI: nursing, information, computer, and cognitive sciences. Also included in this section is a chapter on ethical applications of healthcare informatics. This section lays the foundation for the remainder of the book.

The Nursing Science and the Foundation of Knowledge chapter describes nurs- ing science and introduces the Foundation of Knowledge model as the conceptual framework for the book. In this chapter, a clinical case scenario is used to illustrate the concepts central to nursing science. A definition of nursing science is also derived from the American Nurses Association’s definition of nursing. Nursing science is the ethical application of knowledge acquired through education, research, and practice to provide services and interventions to patients to maintain, enhance, or restore their health, and to acquire, process, generate, and disseminate nursing knowledge to advance the nursing profession. Information is a central concept and health care’s most valuable resource. Information science and systems, together with computers, are constantly changing the way healthcare organizations conduct their business. This will continue to evolve.

2 seCtIoN I Building Blocks of Nursing Informatics

To prepare for these innovations, the reader must understand fundamental infor- mation and computer concepts, covered in the Introduction to Information, Informa- tion Science, and Information Systems and Computer Science and the Foundation of Knowledge Model chapters, respectively. Information science deals with the in- terchange (or flow) and scaffolding (or structure) of information and involves the application of information tools for solutions to patient care and business problems in health care. To be able to use and synthesize information effectively, an individual must be able to obtain, perceive, process, synthesize, comprehend, convey, and man- age the information. Computer science deals with understanding the development, design, structure, and relationship of computer hardware and software. This science offers extremely valuable tools that, if used skillfully, can facilitate the acquisition and manipulation of data and information by nurses, who can then synthesize these resources into an ever-evolving knowledge and wisdom base. This not only facilitates professional development and the ability to apply evidence-based practice decisions within nursing care, but, if the results are disseminated and shared, can also advance the profession’s knowledge base. The development of knowledge tools, such as the automation of decision making and strides in artificial intelligence, has altered the understanding of knowledge and its representation. The ability to structure knowl- edge electronically facilitates the ability to share knowledge structures and enhance collective knowledge.

As discussed in the Introduction to Cognitive Science and Cognitive Informatics chapter, cognitive science deals with how the human mind functions. This science encompasses how people think, understand, remember, synthesize, and access stored information and knowledge. The nature of knowledge, including how it is developed, used, modified, and shared, provides the basis for continued learning and intellectual growth.

The Ethical Applications of Informatics chapter focuses on ethical issues associ- ated with managing private information with technology and provides a framework for analyzing ethical issues and supporting ethical decision making.

The material within this book is placed within the context of the Foundation of Knowledge model (shown in Figure I-1 and periodically throughout the book, but more fully introduced and explained in the Nursing Science and the Foundation of Knowledge chapter). The Foundation of Knowledge model is used throughout the text to illustrate how knowledge is used to meet the needs of healthcare delivery sys- tems, organizations, patients, and nurses. It is through interaction with these building blocks—the theories, architecture, and tools—that one acquires the bits and pieces of

seCtIoN I Building Blocks of Nursing Informatics 3

data necessary, processes these into information, and generates and disseminates the resulting knowledge. Through this dynamic exchange, which includes feedback, indi- viduals continue the interaction and use of these sciences to input or acquire, process, and output or disseminate generated knowledge. Humans experience their environ- ment and learn by acquiring, processing, generating, and disseminating knowledge. When they then share (disseminate) this new knowledge and receive feedback on the knowledge they have shared, the feedback initiates the cycle of knowledge all over again. As individuals acquire, process, generate, and disseminate knowledge, they are motivated to share, rethink, and explore their own knowledge base. This complex process is captured in the Foundation of Knowledge model. Throughout the chapters in the Building Blocks of Nursing Informatics section, readers are challenged to think about how the model can help them to understand the ways in which they acquire, process, generate, disseminate, and then receive and process feedback on their new knowledge of the building blocks of NI.

4 seCtIoN I Building Blocks of Nursing Informatics

Figure I-1 Foundation of Knowledge Model Designed by Alicia Mastrian

KA - Knowledge acquisition KD - Knowledge dissemination KG - Knowledge generation KP - Knowledge processing



Information Information



Bytes Bytes



Bits Data



Bytes Bytes Bits

Bits Data Information






seCtIoN I Building Blocks of Nursing Informatics 5

Key terms » Borrowed theory » Building blocks » Clinical

databases » Clinical practice

guidelines » Conceptual


» Data » Data mining » Evidence » Feedback » Foundation of

Knowledge model » Information » Knowledge

» Knowledge acquisition

» Knowledge dissemination

» Knowledge generation

» Knowledge processing

» Knowledge worker » Nursing

informatics » Nursing science » Nursing theory » Relational

database » Transparent wisdom

1. Define nursing science and its relationship to various nursing roles and nursing informatics.

2. Introduce the Foundation of Knowledge model as the organizing conceptual framework for the text.

3. Explain the relationships among knowledge acquisition, knowledge processing, knowledge generation, knowledge dissemination, and wisdom.


Introduction Nursing informatics has been traditionally defined as a specialty that integrates nursing science, computer science, and information science to manage and communicate data, information, knowledge, and wisdom in nursing practice. This chapter focuses on nursing science as one of the building blocks of nurs- ing informatics. As depicted in Figure 1-1, the  traditional definition of nursing

Nursing science and the Foundation of Knowledge Dee McGonigle and Kathleen Mastrian

Figure 1-1 Building Blocks of Nursing Informatics

Nursing Informatics

Nursing Science

Computer Science

Cognitive Science

Information Science



informatics is extended to include cognitive science. The Foundation of Knowledge model is also introduced as the organizing conceptual framework of this text, and the model is tied to nursing science and the practice of nursing informatics. To lay the groundwork for this discussion, consider the following patient scenario:

Tom H. is a registered nurse who works in a very busy metropolitan hos- pital emergency room. He has just admitted a 79-year-old man whose wife brought him to the hospital because he is having trouble breathing. Tom immediately clips a pulse oximeter to the patient’s finger and performs a very quick assessment of the patient’s other vital signs. He discovers a rapid pulse rate and a decreased oxygen saturation level in addition to the rapid and labored breathing. Tom determines that the patient is not in immedi- ate danger and that he does not require intubation. Tom focuses his initial attention on easing the patient’s labored breathing by elevating the head of the bed and initiating oxygen treatment; he then hooks the patient up to a heart monitor. Tom continues to assess the patient’s breathing status as he performs a head-to-toe assessment of the patient that leads to the nursing diagnoses and additional interventions necessary to provide comprehensive care to this patient.

Consider Tom’s actions and how and why he intervened as he did. Tom relied on the immediate data and information that he acquired during his initial rapid assessment to deliver appropriate care to his patient. Tom also used tech- nology (a pulse oximeter and a heart monitor) to assist with and support the delivery of care. What is not immediately apparent, and some would argue is transparent (done without conscious thought), is the fact that during the rapid assessment, Tom reached into his knowledge base of previous learning and experiences to direct his care, so that he could act with transparent wisdom. He used both nursing theory and borrowed theory to inform his practice. Tom certainly used nursing process theory, and he may have also used one of several other nurs- ing theories, such as Rogers’s science of unitary human beings, Orem’s theory of self-care deficit, or Roy’s adaptation theory. In addition, Tom may have applied his knowledge from some of the basic sciences, such as anatomy, physiology, psy- chology, and chemistry, as he determined the patient’s immediate needs. Informa- tion from Maslow’s hierarchy of needs, Lazarus’s transaction model of stress and coping, and the health belief model may have also helped Tom practice profes- sional nursing. He gathered data, and then analyzed and interpreted those data to form a conclusion—the essence of science. Tom has illustrated the practical aspects of nursing science.

The American Nurses Association (2016) defines nursing in this way: “Nursing is the protection, promotion, and optimization of health and abilities, prevention of illness and injury, facilitation of healing, alleviation of suffering through the diagnosis and treatment of human response, and advocacy in the care of individu- als, families, groups, communities, and populations” (para. 1). Thus the focus of nursing is on human responses to actual or potential health problems and advo- cacy for various clients. These human responses are varied and may change over

8 CHAPteR 1 Nursing Science and the Foundation of Knowledge

time in a single case. Nurses must possess the technical skills to manage equip- ment and perform procedures, the interpersonal skills to interact appropriately with people, and the cognitive skills to observe, recognize, and collect data; ana- lyze and interpret data; and reach a reasonable conclusion that forms the basis of a decision. At the heart of all of these skills lies the management of data and information. This definition of nursing science focuses on the ethical application of knowledge acquired through education, research, and practice to provide ser- vices and interventions to patients to maintain, enhance, or restore their health and to acquire, process, generate, and disseminate nursing knowledge to advance the nursing profession.

Nursing is an information-intensive profession. The steps of using information, applying knowledge to a problem, and acting with wisdom form the basis of nursing practice science. Information is composed of data that were processed using knowledge. For information to be valuable, it must be accessible, accurate, timely, complete, cost-effective, flexible, reliable, relevant, simple, verifiable, and secure. Knowledge is the awareness and understanding of a set of information and ways that information can be made useful to support a specific task or arrive at a decision. In the case scenario, Tom used accessible, accurate, timely, relevant, and verifiable data and information. He compared that data and information to his knowledge base of previous experiences to determine which data and information were relevant to the current case. By applying his previous knowledge to data, he converted those data into information, and information into new knowledge—that is, an understanding of which nursing interventions were appropriate in this case. Thus information is data made functional through the application of knowledge.

Humans acquire data and information in bits and pieces and then transform the information into knowledge. The information-processing functions of the brain are frequently compared to those of a computer, and vice versa (see a dis- cussion of cognitive informatics for more information). Humans can be thought of as organic information systems that are constantly acquiring, processing, and generating information or knowledge in their professional and personal lives. They have an amazing ability to manage knowledge. This ability is learned and honed from birth as individuals make their way through life interacting with the environment and being inundated with data and information. Each person experi- ences the environment and learns by acquiring, processing, generating, and dis- seminating knowledge.

Tom, for example, acquired knowledge in his basic nursing education program and continues to build his foundation of knowledge by engaging in such activities as reading nursing research and theory articles, attending continuing education programs, consulting with expert colleagues, and using clinical databases and clinical practice guidelines. As he interacts in the environment, he acquires knowledge that must be processed. This processing effort causes him to redefine and restructure his knowledge base and generate new knowledge. Tom can then share (disseminate) this new knowledge with colleagues, and he may receive feedback on the knowledge that he shares. This dissemination and feedback builds the knowledge foundation anew

Introduction 9

as Tom acquires, processes, generates, and disseminates new knowledge as a result of his interactions. As others respond to his knowledge dissemination and he acquires yet more knowledge, he is engaged to rethink, reflect on, and re-explore his knowledge acquisition, leading to further processing, generating, and then disseminating knowledge. This ongoing process is captured in the Foundation of Knowledge model, which is used as an organizing framework for this text.

At its base, the model contains bits, bytes (a computer term used to quantify data), data, and information in a random representation. Growing out of the base are separate cones of light that expand as they reflect upward; these cones represent knowledge acquisition, knowledge generation, and knowledge dissemination. At the in- tersection of the cones and forming a new cone is knowledge processing. Encircling and cutting through the knowledge cones is feedback that acts on and may transform any or all aspects of knowledge represented by the cones. One should imagine the model as a dynamic figure in which the cones of light and the feedback rotate and interact rather than remain static. Knowledge acquisition, knowledge generation, knowledge dissemination, knowledge processing, and feedback are constantly evolving for nurse scientists. The transparent effect of the cones is deliberate and is intended to suggest that as knowledge grows and expands, its use becomes more transparent—a person uses this knowledge during practice without even being consciously aware of which aspect of knowledge is being used at any given moment.

Experienced nurses, thinking back to their novice years, may recall feeling like their head was filled with bits of data and information that did not form any type of cohesive whole. As the model depicts, the processing of knowledge begins a bit later (imagine a timeline applied vertically) with early experiences on the bottom and ex- pertise growing as the processing of knowledge ensues. Early on in nurses’ education, conscious attention is focused mainly on knowledge acquisition, and beginning nurses depend on their instructors and others to process, generate, and disseminate knowl- edge. As nurses become more comfortable with the science of nursing, they begin to take over some of the other Foundation of Knowledge functions. However, to keep up with the explosion of information in nursing and health care, they must continue to rely on the knowledge generation of nursing theorists and researchers and the dis- semination of their work. In this sense, nurses are committed to lifelong learning and the use of knowledge in the practice of nursing science.

The Foundation of Knowledge model (Figure 1-2) permeates this text, reflecting the understanding that knowledge is a powerful tool and that nurses focus on informa- tion as a key building block of knowledge. The application of the model is described to help the reader understand and appreciate the foundation of knowledge in nursing science and see how it applies to nursing informatics. All of the various nursing roles (practice, administration, education, research, and informatics) involve the science of nursing. Nurses are knowledge workers, working with information and generating information and knowledge as a product. They are knowledge acquirers, provid- ing convenient and efficient means of capturing and storing knowledge. They are knowledge users, meaning individuals or groups who benefit from valuable, viable knowledge. Nurses are knowledge engineers, designing, developing, implementing, and maintaining knowledge. They are knowledge managers, capturing and processing collective expertise and distributing it where it can create the largest benefit. Finally,

10 CHAPteR 1 Nursing Science and the Foundation of Knowledge

they are knowledge developers and generators, changing and evolving knowledge based on the tasks at hand and the information available.

In the case scenario, at first glance one might label Tom as a knowledge worker, a knowledge acquirer, and a knowledge user. However, stopping here might sell Tom short in his practice of nursing science. Although he acquired and used knowledge to help him achieve his work, he also processed the data and information he collected to develop a nursing diagnosis and a plan of care. The knowledge stores Tom used to develop and glean knowledge from valuable information are generative (having the ability to originate and produce or generate) in nature. For example, Tom may have learned something new about his patient’s culture from the patient or his wife that he will file away in the knowledge repository of his mind to be used in another similar situation. As he compares this new cultural information to what he already knows, he may gain insight into the effect of culture on a patient’s response to illness. In this sense, Tom is a knowledge generator. If he shares this newly acquired knowledge with another practitioner, and as he records his observations and his conclusions, he is then disseminating knowledge. Tom also uses feedback from the various technologies he has applied to monitor his patient’s status. In addition, he may rely on feedback from laboratory reports or even other practitioners to help him rethink, revise, and apply the knowledge about this patient that he is generating.

Figure 1-2 Foundation of Knowledge Model Designed by Alicia Mastrian

KA - Knowledge acquisition KD - Knowledge dissemination KG - Knowledge generation KP - Knowledge processing



Information Information



Bytes Bytes



Bits Data



Bytes Bytes Bits

Bits Data Information






Introduction 11

To have ongoing value, knowledge must be viable. Knowledge viability refers to applications (most technology based) that offer easily accessible, accurate, and timely information obtained from a variety of resources and methods and presented in a manner so as to provide the necessary elements to generate new knowledge. In the case scenario, Tom may have felt the need to consult an electronic database or a clinical guidelines repository that he has downloaded on his tablet or smartphone, or that resides in the emergency room’s networked computer system, to assist him in the development of a comprehensive care plan for his patient. In this way, Tom uses tech- nology and evidence to support and inform his practice. It is also possible in this sce- nario that an alert might appear in the patient’s electronic health record or the clinical information system (CIS) reminding Tom to ask about influenza and pneumonia vac- cines. Clinical information technologies that support and inform nursing practice and nursing administration are an important part of nursing informatics.

This text provides a framework that embraces knowledge so that readers can develop the wisdom necessary to apply what they have learned. Wisdom is the applica- tion of knowledge to an appropriate situation. In the practice of nursing science, one expects actions to be directed by wisdom. Wisdom uses knowledge and experience to heighten common sense and insight to exercise sound judgment in practical mat- ters. It is developed through knowledge, experience, insight, and reflection. Wisdom is sometimes thought of as the highest form of common sense, resulting from accumu- lated knowledge or erudition (deep, thorough learning) or enlightenment (education that results in understanding and the dissemination of knowledge). It is the ability to apply valuable and viable knowledge, experience, understanding, and insight while being prudent and sensible. Knowledge and wisdom are not synonymous: Knowledge abounds with others’ thoughts and information, whereas wisdom is focused on one’s own mind and the synthesis of experience, insight, understanding, and knowledge. Wisdom has been called the foundation of the art of nursing.

Some nursing roles might be viewed as more focused on some aspects rather than other aspects of the foundation of knowledge. For example, some might argue that nurse educators are primarily knowledge disseminators and that nurse researchers are knowledge generators. Although the more frequent output of their efforts can certainly be viewed in this way, it is important to realize that nurses use all of the aspects of the Foundation of Knowledge model regardless of their area of practice. For nurse educators to be effective, they must be in the habit of constantly building and rebuilding their foundation of knowledge about nursing science. In addition, as they develop and implement curricular innovations, they must evaluate the effective- ness of those changes. In some cases, they use formal research techniques to achieve this goal and, therefore, generate knowledge about the best and most effective teach- ing strategies. Similarly, nurse researchers must acquire and process new knowledge as they design and conduct their research studies. All nurses have the opportunity to be involved in the formal dissemination of knowledge via their participation in pro- fessional conferences, either as presenters or as attendees. In addition, some nurses disseminate knowledge by formal publication of their ideas. In the cases of conference presentation and publication, nurses may receive feedback that stimulates rethinking about the knowledge they have generated and disseminated, in turn prompting them to acquire and process data and information anew.

12 CHAPteR 1 Nursing Science and the Foundation of Knowledge

All nurses, regardless of their practice arena, must use informatics and technology to inform and support that practice. The case scenario discussed Tom’s use of vari- ous monitoring devices that provide feedback on the physiologic status of the patient. It was also suggested that Tom might consult a clinical database or nursing practice guidelines residing on a tablet or smartphone, in the cloud (a virtual information storage system), or on a clinical agency network as he develops an appropriate plan of action for his nursing interventions. Perhaps the CIS in the agency supports the collection of data about patients in a relational database, providing an opportunity for data mining by nursing administrators or nurse researchers. In this way, administra- tors and researchers can glean information about best practices and determine which improvements are necessary to deliver the best and most effective nursing care (Swan, Lang, & McGinley, 2004).

The future of nursing science and nursing informatics is closely associated with nursing education and nursing research. Skiba (2007) suggested that techno-savvy and well-informed faculty who can demonstrate the appropriate use of technologies to enhance the delivery of nursing care are needed. Along those lines, Whitman-Price, Kennedy, and Godwin (2012) conducted research among senior nursing students to determine perceptions of personal phone use to access healthcare information dur- ing clinical. Their study indicated that ready access to electronic resources enhanced clinical decision making and confidence in patient care. Girard (2007) discussed cutting-edge operating room technologies, such as nanosurgery using nanorobots, smart fabrics that aid in patient assessment during surgery, biopharmacy techniques for the safe and effective delivery of anesthesia, and virtual reality training. She made an extremely provocative point about nursing education: “Educators will need to expand their knowledge and teach for the future and not the past. They must take heed that the old tried-and-true nursing education methods and curriculum that has lasted 100 years will have to change, and that change will be mandated for all areas of nursing” (p. 353). Bassendowski (2007) specifically addressed the potential for the generation of knowledge in educational endeavors as faculty apply new technologies to teaching and the focus shifts away from individual to group instruction that pro- motes sharing and processing of knowledge.

Several key national groups continue to promote the inclusion of informatics content in nursing education programs. These initiatives include the Vision Series by the National League for Nursing (NLN; 2015); recommendations in the Quality and Safety Education for Nurses (QSEN) learning modules (2014a); the Technology Informatics Guiding Education Reform (TIGER) Initiative (Healthcare Information and Management Systems Society, 2016); and Nursing Informatics Deep Dive by the American Association of Colleges of Nursing (AACN; 2016). These organizations focus on the need to integrate informatics competencies into nursing curricula to prepare future nurses for the tasks of managing data, information, and knowledge; alleviating errors and promoting safety; supporting decision making; and improving the quality of patient care. Nurse educators are challenged to prepare informatics- competent nurses who can practice safely in technology-laden settings.

The TIGER (2007) initiative identified steps toward a 10-year vision and stated a key purpose: “to create a vision for the future of nursing that bridges the quality chasm with information technology, enabling nurses to use informatics in practice

Introduction 13

and education to provide safer, higher-quality patient care” (p. 4). The pillars of the TIGER vision include the following:

• Management and Leadership: Revolutionary leadership that drives, empowers, and executes the transformation of health care.

• Education: Collaborative learning communities that maximize the possibilities of technology toward knowledge development and dissemination, driving rapid deployment and implementation of best practices.

• Communication and Collaboration: Standardized, person-centered, technology- enabled processes to facilitate teamwork and relationships across the continuum of care.

• Informatics Design: Evidence-based, interoperable intelligence systems that sup- port education and practice to foster quality care and safety.

• Information Technology: Smart, people-centered, affordable technologies that are universal, useable, useful, and standards based.

• Policy: Consistent, incentives-based initiatives (organizational and governmen- tal) that support advocacy and coalition-building, achieving and resourcing an ethical culture of safety.

• Culture: A respectful, open system that leverages technology and informatics across multiple disciplines in an environment where all stakeholders trust each other to work together toward the goal of high quality and safety (p. 4).

The Essentials of Baccalaureate Education for Professional Nursing Practice (AACN, 2008, pp. 18–19) includes the following technology-related outcomes for baccalaureate nursing graduates:

1. Demonstrate skills in using patient care technologies, information systems, and communication devices that support safe nursing practice.

2. Use telecommunication technologies to assist in effective communication in a variety of healthcare settings.

3. Apply safeguards and decision-making support tools embedded in patient care technologies and information systems to support a safe practice environment for both patients and healthcare workers.

4. Understand the use of CIS to document interventions related to achieving nurse-sensitive outcomes.

5. Use standardized terminology in a care environment that reflects nursing’s unique contribution to patient outcomes.

6. Evaluate data from all relevant sources, including technology, to inform the delivery of care.

7. Recognize the role of information technology in improving patient care out- comes and creating a safe care environment.

8. Uphold ethical standards related to data security, regulatory requirements, con- fidentiality, and clients’ right to privacy.

9. Apply patient care technologies as appropriate to address the needs of a diverse patient population.

10. Advocate for the use of new patient care technologies for safe, quality care.

14 CHAPteR 1 Nursing Science and the Foundation of Knowledge

11. Recognize that redesign of workflow and care processes should precede imple- mentation of care technology to facilitate nursing practice.

12. Participate in the evaluation of information systems in practice settings through policy and procedure development.

The report suggests the following sample content for achieving these student out- comes (AACN, 2008, pp. 19–20):

• Use of patient care technologies (e.g., monitors, pumps, computer-assisted devices)

• Use of technology and information systems for clinical decision making • Computer skills that may include basic software, spreadsheet, and healthcare

databases • Information management for patient safety • Regulatory requirements through electronic data-monitoring systems • Ethical and legal issues related to the use of information technology, including

copyright, privacy, and confidentiality issues • Retrieval information systems, including access, evaluation of data, and applica-

tion of relevant data to patient care • Online literature searches • Technological resources for evidence-based practice • Web-based learning and online literature searches for self and patient use • Technology and information systems safeguards (e.g., patient monitoring, equip-

ment, patient identification systems, drug alerts and IV systems, and bar coding) • Interstate practice regulations (e.g., licensure, telehealth) • Technology for virtual care delivery and monitoring • Principles related to nursing workload measurement and resources and informa-

tion systems • Information literacy • Electronic health record and physician order entry • Decision support tools • Role of the nurse informaticist in the context of health informatics and informa-

tion systems

The Informatics and Healthcare Technologies Essentials of Master’s Education in Nursing includes the following elements:

Essential V: Informatics and Healthcare Technologies Rationale Informatics and healthcare technologies encompass five broad areas:

• Use of patient care and other technologies to deliver and enhance care • Communication technologies to integrate and coordinate care • Data management to analyze and improve outcomes of care • Health information management for evidence-based care and health

education • Facilitation and use of electronic health records to improve patient care

(AACN, 2011, pp. 17–18)

Introduction 15


Knowledge skills Attitudes

Explain why information and technology skills are essential for safe patient care

Seek education about how information is managed in care settings before providing care

Apply technology and information management tools to support safe processes of care

Appreciate the necessity for all health professionals to seek lifelong, continuous learning of information technology skills

Identify essential information that must be available in a common database to support patient care

Contrast benefits and limitations of different communication technologies and their impact on safety and quality

Navigate the electronic health record

Document and plan patient care in an electronic health record

Employ communication technologies to coordinate care for patients

Value technologies that support clinical decision making, error prevention, and care coordination

Protect the confidentiality of protected health information in electronic health records

Describe examples of how technology and information management are related to the quality and safety of patient care

Recognize the time, effort, and skill required for computers, databases, and other technologies to become reliable and effective tools for patient care

Respond appropriately to clinical decision-making supports and alerts

Use information management tools to monitor outcomes of care processes

Use high quality electronic sources of healthcare information

Value nurses’ involvement in design, selection, implementation, and evaluation of information technologies to support patient care

Definition: Use information and technology to communicate, manage knowledge, mitigate error, and support decision making.

Reproduced from Cronenwett, L., Sherwood, G., Barnsteiner J., Disch, J., Johnson, J., Mitchell, P., . . . Warren, J. (2007). Quality and safety education for nurses. Nursing Outlook, 55(3), 122–131. Copyright 2007, with permission from Elsevier.

Quality and safety education for Nurses As nursing science evolves, it is critical that patient care improves. Sometimes, un- fortunately, patient care is less-than-adequate and is unsafe. Therefore, quality and safety have become paramount. The QSEN Institute project seeks to prepare future nurses who will have the knowledge, skills, and attitudes (KSAs) necessary to con- tinuously improve the quality and safety of the healthcare systems within which they work.

Prelicensure informatics KSAs include the following (QSEN Institute, 2014c):

16 CHAPteR 1 Nursing Science and the Foundation of Knowledge

Graduate-level informatics KSAs include the following (QSEN Institute, 2014b):


Knowledge skills Attitudes

Contrast benefits and limitations of common information technology strategies used in the delivery of patient care

Evaluate the strengths and weaknesses of information systems used in patient care

Participate in the selection, design, implementation, and evaluation of information systems

Communicate the integral role of information technology in nurses’ work

Model behaviors that support implementation and appropriate use of electronic health records

Assist team members to adopt information technology by piloting and evaluating proposed technologies

Value the use of information and communication technologies in patient care

Formulate essential information that must be available in a common database to support patient care in the practice specialty

Evaluate benefits and limitations of different communication technologies and their impact on safety and quality

Promote access to patient care information for all professionals who provide care to patients

Serve as a resource for how to document nursing care at basic and advanced levels

Develop safeguards for protected health information

Champion communication technologies that support clinical decision making, error prevention, care coordination, and protection of patient privacy

Appreciate the need for consensus and collaboration in developing systems to manage information for patient care

Value the confidentiality and security of all patient records

Describe and critique taxonomic and terminology systems used in national efforts to enhance interoperability of information systems and knowledge management systems

Access and evaluate high quality electronic sources of healthcare information

Participate in the design of clinical decision-making supports and alerts

Search, retrieve, and manage data to make decisions using information and knowledge management systems

Anticipate unintended consequences of new technology

Value the importance of standardized terminologies in conducting searches for patient information

Appreciate the contribution of technological alert systems

Appreciate the time, effort, and skill required for computers, databases, and other technologies to become reliable and effective tools for patient care

Definition: Use information and technology to communicate, manage knowledge, mitigate error, and support decision making.

Reproduced from Cronenwett, L., Sherwood, G., Pohl, J., Barnsteiner J., Moore, D., Sullivan, D., . . . Warren, J. (2009). Quality and safety education for nurses. Nursing Outlook, 57(6), 338–348. Copyright 2009, with permission from Elsevier.

Quality and Safety Education for Nurses 17

This text is designed to include the necessary content to prepare nurses for prac- tice in the ever-changing and technology-laden healthcare environments. Informatics competence has been recognized as necessary in order to enhance clinical decision making and improve patient care for many years. This is evidenced by Goossen (2000), who reflected on the need for research in this area and believed that the focus of nursing informatics research should be on the structuring and processing of patient information and the ways that these endeavors inform nursing decision mak- ing in clinical practice. The increased use of technology to enhance nursing practice, nursing education, and nursing research will open new avenues for acquiring, pro- cessing, generating, and disseminating knowledge.

In the future, nursing research will make significant contributions to the devel- opment of nursing science. Technologies and translational research will abound, and clinical practices will continue to be evidence based, thereby improving patient outcomes and decreasing safety concerns. Schools of nursing will embrace nursing science as they strive to meet the needs of changing student populations and the increasing complexity of healthcare environments.

summary Nursing science influences all areas of nursing practice. This chapter provided an overview of nursing science and considered how nursing science relates to typical nursing practice roles, nursing education, informatics, and nursing research. The Foundation of Knowledge model was introduced as the organizing conceptual framework for this text. Finally, the relationship of nursing science to nursing informatics was discussed. In subsequent chapters the reader will learn more about how nursing informatics supports nurses in their many and varied roles. In  an ideal world, nurses would embrace nursing science as knowledge users, knowledge managers, knowledge developers, knowledge engineers, and knowl- edge workers.


1. Imagine you are in a social situation and someone asks you, “What does a nurse do?” Think about how you will capture and convey the richness that is nursing science in your answer.

2. Choose a clinical scenario from your recent experience and analyze it using the Foundation of Knowledge model. How did you acquire knowledge? How did you process knowledge? How did you generate knowledge? How did you dis- seminate knowledge? How did you use feedback, and what was the effect of the feedback on the foundation of your knowledge?

18 CHAPteR 1 Nursing Science and the Foundation of Knowledge

References American Association of Colleges of Nursing (AACN). (2008, October 20). The essentials of

baccalaureate education for professional nursing practice. Retrieved from http://www.aacn .nche.edu/education-resources/BaccEssentials08.pdf

American Association of Colleges of Nursing (AACN). (2011, March 21). The essentials of master’s education in nursing. Retrieved from http://www.aacn.nche.edu/ education -resources/MastersEssentials11.pdf

American Association of Colleges of Nursing (AACN). (2016). Background and overview: Nursing informatics Deep Dive. Retrieved from http://www.aacn.nche.edu/qsen-informatics /background-overview

American Nurses Association. (2016). What is nursing? Retrieved from http://www.nursingworld .org/EspeciallyForYou/What-is-Nursing

Bassendowski, S. (2007). NursingQuest: Supporting an analysis of nursing issues. Journal of Nursing Education, 46(2), 92–95. Retrieved from Education Module database [document ID: 1210832211].

Cronenwett, L., Sherwood, G., Barnsteiner J., Disch, J., Johnson, J., Mitchell, P., . . . Warren, J. (2007). Quality and safety education for nurses. Nursing Outlook, 55(3), 122–131.

Girard, N. (2007). Science fiction comes to the OR. Association of Operating Room Nurses. AORN Journal, 86(3), 351–353. Retrieved from Health Module database [document ID: 1333149261].

Goossen, W. (2000). Nursing informatics research. Nurse Researcher, 8(2), 42. Retrieved from ProQuest Nursing & Allied Health Source database [document ID: 67258628].

Healthcare Information and Management Systems Society. (2016). The TIGER initiative. Retrieved from http://www.himss.org/professional-development/tiger-initiative

National League for Nursing (NLN). (2015). A vision for the changing faculty role: Preparing students for the technological world of health care. Retrieved from https://www.nln.org /docs/default-source/about/nln-vision-series-(position-statements)/a-vision-for-the-changing -faculty-role-preparing-students-for-the-technological-world-of-health-care.pdf?sfvrsn=0

Quality and Safety Information for Nurses (QSEN) Institute. (2014a). Courses: Learning modules. Retrieved from http://www.qsen.org/courses/learning-modules

QSEN Institute. (2014b). Graduate KSAs. Retrieved from http://www.qsen.org/competencies /graduate-ksas

QSEN Institute. (2014c). Pre-licensure KSAs. Retrieved from http://www.qsen.org/competencies /pre-licensure-ksas

Skiba, D. (2007). Faculty 2.0: Flipping the novice to expert continuum. Nursing Education Perspectives, 28(6), 342–344. Retrieved from ProQuest Nursing & Allied Health Source database [document ID: 1401240241].

Swan, B., Lang, N., & McGinley, A. (2004). Access to quality health care: Links between evidence, nursing language, and informatics. Nursing Economic$, 22(6), 325–332. Retrieved from Health Module database [document ID: 768191851].

Technology Informatics Guiding Education Reform. (2007). Evidence and informatics transforming nursing: 3-year action steps toward a 10-year vision. Retrieved from http:// www.aacn.nche.edu/education-resources/TIGER.pdf

Whitman-Price, R., Kennedy, L., & Godwin, C. (2012). Use of personal phones by senior nursing students to access health care information during clinical education: Staff nurses’ and students’ perceptions. Journal of Nursing Education, 51(11), 642–646.

References 19

Key Terms » Acquisition » Alert » Analysis » Chief information

officers » Chief technical

officers » Chief technology

officers » Cloud computing » Cognitive science » Communication

science » Computer-based

information systems » Computer science » Consolidated

Health Informatics » Data

» Dissemination » Document » Electronic health

records » Federal Health

Information Exchange

» Feedback » Health information

exchange » Health Level Seven » Indiana Health

Information Exchange

» Information » Information

science » Information


» Information technology

» Input » Interfaces » Internet2 » Internet of

Things (IoT) » Knowledge » Knowledge worker » Library science » Massachusetts

Health Data Consortium

» National Health Information Infrastructure

» National Health Information Network

» New England Health EDI Network

» Next-Generation Internet

» Outcome » Output » Processing » Rapid

Syndromic Validation Project

» Report » Social sciences » Stakeholders » Summaries » Synthesis » Telecommunications

1. Reflect on the progression from data to informa- tion to knowledge.

2. Describe the term information. 3. Assess how information is acquired. 4. Explore the characteristics of quality information. 5. Describe an information system. 6. Explore data acquisition or input and processing

or retrieval, analysis, and synthesis of data.

7. Assess output or reports, documents, summaries, alerts, and outcomes.

8. Describe information dissemination and feedback. 9. Define information science.

10. Assess how information is processed. 11. Explore how knowledge is generated in informa-

tion science.


Introduction This chapter explores information, information systems (ISs), and infor­ mation science as one of the building blocks of informatics. (Refer to Figure 2-1.) The key word here, of course, is information. Information and information processing are central to the work of health care. A healthcare professional is known as a knowledge worker because he or she deals with and processes information on a daily basis to make it meaningful and inform his or her practice.

Healthcare information is complex, and many concerns and issues arise with healthcare information, such as ownership, access, disclosure, exchange, security, privacy, disposal, and dissemination. The widespread implementation of electronic health records (EHRs) has promoted collabora­ tion among public­ and private­sector stakeholders on a wide­ranging va­ riety of healthcare information solutions. Some of these initiatives include Health Level Seven (HL7), the eGov initiative by Consolidated Health Informat- ics (CHI), the National Health Information Infrastructure (NHII), the National Health Information Network (NHIN), Next-Generation Internet (NGI), Internet2, and iHealth record. There are also health information exchange (HIE) systems, such as Connecting for Health, the eHealth initiative, the Federal Health Information Exchange (FHIE), the Indiana Health Information Exchange (IHIE), the Massachusetts Health Data Consortium (MHDC), the New England Health EDI Network (NEHEN), the State of New Mexico Rapid Syndromic Validation Project (RSVP), the Southeast Michigan e­Prescribing Initiative, and the Tennessee Volunteer eHealth Initiative (Goldstein, Groen, Ponkshe, & Wine, 2007). Many of these were sparked by the HITECH Act of 2011, which set the 2014 deadline for implementing EHRs and provided the impetus for HIE initiatives.

It is quite evident from the previous brief listing that there is a need to remedy healthcare information technology (IT) concerns, challenges, and issues faced today. One of the main issues deals with how healthcare infor­ mation is managed to make it meaningful. It is important to understand

Introduction to Information, Information Science, and Information Systems Kathleen Mastrian and Dee McGonigle



how people obtain, manipulate, use, share, and dispose of information. This chapter deals with the information piece of this complex puzzle.

Information Suppose someone states the number 99.5. What does that mean? It could be a radio station or a score on a test. Now suppose someone says that Ms. Howsunny’s tem­ perature is 99.5°F—what does that convey? It is then known that 99.5 is a person’s temperature. The data (99.5) were processed to the information that 99.5° is a spe­ cific person’s temperature. Data are raw facts. Information is processed data that has meaning. Healthcare professionals constantly process data and information to pro­ vide the best possible care for their patients.

Many types of data exist, such as alphabetic, numeric, audio, image, and video data. Alphabetic data refer to letters, numeric data refer to numbers, and alphanumeric data combine both letters and numbers. This includes all text and the numeric outputs of digital monitors. Some of the alphanumeric data encountered by healthcare profes­ sionals are in the form of patients’ names, identification numbers, or medical record numbers. Audio data refer to sounds, noises, or tones—for example, monitor alerts or alarms, taped or recorded messages, and other sounds. Image data include graphics and pictures, such as graphic monitor displays or recorded electrocardiograms, radio­ graphs, magnetic resonance imaging (MRI) outputs, and computed tomography (CT) scans. Video data refer to animations, moving pictures, or moving graphics. Using

22 CHAPTER 2 Introduction to Information, Information Science, and Information Systems

Figure 2-1 Building Blocks of Nursing Informatics

Nursing Informatics

Nursing Science

Computer Science

Cognitive Science

Information Science

these data, one may review the ultrasound of a pregnant patient, examine a patient’s echocardiogram, watch an animated video for professional development, or learn how to operate a new technology tool, such as a pump or monitoring system. The data we gather, such as heart and lung sounds or X­rays, help us produce information. For ex­ ample, if a patient’s X­rays show a fracture, it is interpreted into information such as spiral, compound, or hairline. This information is then processed into knowledge and a treatment plan is formulated based on the healthcare professional’s wisdom.

The integrity and quality of the data, rather than the form, are what matter. Integrity refers to whole, complete, correct, and consistent data (Figure 2-2). Data integrity can be compromised through human error; viruses, worms, or other computer bugs; hard­ ware failures or crashes; transmission errors; or hackers entering the system. Figure 2-3 illustrates some ways that data can be compromised. Information technologies help to decrease these errors by putting into place safeguards, such as backing up files on a routine basis, error detection for transmissions, and user interfaces that help people enter the data correctly. High­quality data are relevant and accurately represent their cor­ responding concepts. Data are dirty when a database contains errors, such as duplicate, incomplete, or outdated records. One author (D.M.) found 50 cases of tongue cancer in a database she examined for data quality. When the records were tracked down and analyzed, and the dirty data were removed, only one case of tongue cancer remained. In this situation, the data for the same person had been entered erroneously 49 times. The major problem was with the patient’s identification number and name: The num­ ber was changed or his name was misspelled repeatedly. If researchers had just taken the number of cases in that defined population as 50, they would have concluded that tongue cancer was an epidemic, resulting in flawed information that is not meaningful. As this example demonstrates, it is imperative that data be clean if the goal is quality information. The data that are processed into information must be of high quality and integrity to create meaning to inform assessments and decision making.

To be valuable and meaningful, information must be of good quality. Its value relates directly to how the information informs decision making. Characteristics of valuable, quality information include accessibility, security, timeliness, accuracy, rel­ evancy, completeness, flexibility, reliability, objectivity, utility, transparency, verifiabil­ ity, and reproducibility.

Information 23

Figure 2-2 Data Integrity

Data integrity

C on

si st

en t

Co rre

ctC om

ple te

Wh ole Quality data

Accessibility is a must; the right user must be able to obtain the right information at the right time and in the right format to meet his or her needs. Getting meaningful information to the right user at the right time is as vital as generating the information in the first place. The right user refers to an authorized user who has the right to obtain the data and information he or she is seeking. Security is a major challenge because unauthorized users must be blocked while the authorized user is provided with open, easy access (see the Electronic Security chapter).

Timely information means that the information is available when it is needed for the right purpose and at the right time. Knowing who won the lottery last week does not help one to know if the person won it today. Accurate information means that there are no errors in the data and information. Relevant information is a subjective descriptor, in that the user must have information that is relevant or applicable to his or her needs. If a healthcare provider is trying to decide whether a patient needs insulin and only the patient’s CT scan information is available, this information is not relevant for that cur­ rent need. However, if one needed information about the CT scan, the information is relevant.

Complete information contains all of the necessary essential data. If the healthcare provider needs to contact the only relative listed for the patient and his or her contact information is listed but the approval for that person to be a contact is missing, this information is considered incomplete. Flexible information means that the informa­ tion can be used for a variety of purposes. Information concerning the inventory of

24 CHAPTER 2 Introduction to Information, Information Science, and Information Systems

Figure 2-3 Threats to Data Integrity

• Connectivity issues • Data corruption • Lost data

• Hardware failures • Software crashes

• Viruses • Worms • Spam • Ransomware

• Incorrect data entry • Spelling errors

Human Error Malware

Transmission ErrorsMachine Error

supplies on a nursing unit, for example, can be used by nurses who need to know if an item is available for use for a patient. The nurse manager accesses this information to help decide which supplies need to be ordered, to determine which items are used most frequently, and to do an economic assessment of any waste.

Reliable information comes from reliable or clean data gathered from authorita­ tive and credible sources. Objective information is as close to the truth as one can get; it is not subjective or biased, but rather is factual and impartial. If someone states something, it must be determined whether that person is reliable and whether what he or she is stating is objective or tainted by his or her own perspective.

Utility refers to the ability to provide the right information at the right time to the right person for the right purpose. Transparency allows users to apply their intellect to accomplish their tasks while the tools housing the information disappear into the background. Verifiable information means that one can check to verify or prove that the information is correct. Reproducibility refers to the ability to produce the same information again.

Information is acquired either by actively looking for it or by having it conveyed by the environment. All of the senses (vision, hearing, touch, smell, and taste) are used to gather input from the surrounding world, and as technologies mature, more and more input will be obtained through the senses. Currently, people receive infor­ mation from computers (output) through vision, hearing, or touch (input); and the response (output) to the computer (input) is the interface with technology. Gesture recognition is increasing, and interfaces that incorporate it will change the way people become informed. Many people access the Internet on a daily basis seeking information or imparting information. Individuals are constantly becoming informed, discovering, or learning; becoming reinformed, rediscovering, or relearning; and purging what has been acquired. The information acquired through these processes is added to the personal knowledge base. Knowledge is the awareness and understanding of a set of information and ways that information can be made useful to support a specific task or arrive at a decision. This knowledge building is an ongoing process engaged in while a person is conscious and going about his or her normal daily activities.

Information Science Information science has evolved over the last 50 or so years as a field of scientific inquiry and professional practice. It can be thought of as the science of informa­ tion, studying the application and usage of information and knowledge in organi­ zations and the interface or interaction between people, organizations, and ISs. This extensive, interdisciplinary science integrates features from cognitive  science, communication science, computer science, library science, and the social sciences. Information science is primarily concerned with the input, processing, output, and feedback of data and information through technology integration with a focus on comprehending the perspective of the stakeholders involved and then applying IT as needed. It is systemically based, dealing with the big picture rather than individual pieces of technology.

Information Science 25

Information science can also be related to determinism. Specifically, it is a response to technologic determinism—the belief that technology develops by its own laws, that it realizes its own potential, limited only by the material resources available, and must therefore be regarded as an autonomous system controlling and ultimately permeating all other subsystems of society (Web Dictionary of Cybernetics and Systems, 2007, para. 1).

This approach sets the tone for the study of information as it applies to itself, the people, the technology, and the varied sciences that are contextually related depending on the needs of the setting or organization; what is important is the interface between the stakeholders and their systems, and the ways they generate, use, and locate information. According to Cornell University (2010), “Informa­ tion Science brings together faculty, students and researchers who share an interest in combining computer science with the social sciences of how people and society interact with information” (para. 1). Information science is an interdisciplinary, people­oriented field that explores and enhances the interchange of information to transform society, communication science, computer science, cognitive science, library science, and the social sciences. Society is dominated by the need for information, and knowledge and information science focus on systems and individ­ ual users by fostering user­centered approaches that enhance society’s information capabilities, effectively and efficiently linking people, information, and technology. This impacts the configuration and mix of organizations and influences the nature of work—namely, how knowledge workers interact with and produce meaningful information and knowledge.

Information Processing Information science enables the processing of information. This processing links people and technology. Humans are organic ISs, constantly acquiring, process­ ing, and generating information or knowledge in their professional and personal lives. This high degree of knowledge, in fact, characterizes humans as extremely intelligent organic machines. The premise of this text revolves around this concept, and the text is organized on the basis of the Foundation of Knowledge model: knowledge acquisition, knowledge processing, knowledge generation, and knowl­ edge dissemination.

Information is data that are processed using knowledge. For information to be valu­ able or meaningful, it must be accessible, accurate, timely, complete, cost­effective, flexible, reliable, relevant, simple, verifiable, and secure. Knowledge is the awareness and understanding of an information set and ways that information can be made useful to support a specific task or arrive at a decision. As an example, if an architect were going to design a building, part of the knowledge necessary for developing a new building is understanding how the building will be used, what size of building is needed compared to the available building space, and how many people will have or need access to this building. Therefore, the work of choosing or rejecting facts based on their significance or relevance to a particular task, such as designing a build­ ing, is also based on a type of knowledge used in the process of converting data into information. Information can then be considered data made functional through the

26 CHAPTER 2 Introduction to Information, Information Science, and Information Systems

application of knowledge. The knowledge used to develop and glean knowledge from valuable information is generative (having the ability to originate and produce or generate) in nature. Knowledge must also be viable. Knowledge viability refers to ap­ plications that offer easily accessible, accurate, and timely information obtained from a variety of resources and methods and presented in a manner so as to provide the necessary elements to generate knowledge.

Information science and computational tools are extremely important in enabling the processing of data, information, and knowledge in health care. In this environ­ ment, the hardware, software, networking, algorithms, and human organic ISs work together to create meaningful information and generate knowledge. The links between information processing and scientific discovery are paramount. However, without the ability to generate practical results that can be disseminated, the process­ ing of data, information, and knowledge is for naught. It is the ability of machines (inorganic ISs) to support and facilitate the functioning of people (human organic ISs) that refines, enhances, and evolves nursing practice by generating knowledge. This knowledge represents five rights: the right information, accessible by the right people in the right settings, applied the right way at the right time.

An important and ongoing process is the struggle to integrate new knowledge and old knowledge so as to enhance wisdom. Wisdom is the ability to act appropriately; it assumes actions directed by one’s own wisdom. Wisdom uses knowledge and experi­ ence to heighten common sense, and uses insight to exercise sound judgment in prac­ tical matters. It is developed through knowledge, experience, insight, and reflection. Wisdom is sometimes thought of as the highest form of common sense, resulting from accumulated knowledge or erudition (deep, thorough learning) or enlightenment (education that results in understanding and the dissemination of knowledge). It is the ability to apply valuable and viable knowledge, experience, understanding, and insight while being prudent and sensible. Knowledge and wisdom are not synony­ mous, because knowledge abounds with others’ thoughts and information, whereas wisdom is focused on one’s own mind and the synthesis of one’s own experience, insight, understanding, and knowledge.

If clinicians are inundated with data without the ability to process it, the situation results in too much data and too little wisdom. Consequently, it is crucial that clini­ cians have viable ISs at their fingertips to facilitate the acquisition, sharing, and use of knowledge while maturing wisdom; this process leads to empowerment.

Information Science and the Foundation of Knowledge Information science is a multidisciplinary science that encompasses aspects of com­ puter science, cognitive science, social science, communication science, and library science to deal with obtaining, gathering, organizing, manipulating, managing, stor­ ing, retrieving, recapturing, disposing of, distributing, and broadcasting information. Information science studies everything that deals with information and can be defined as the study of ISs. This science originated as a subdiscipline of computer science, as practitioners sought to understand and rationalize the management of technology

Information Science and the Foundation of Knowledge 27

within organizations. It has since matured into a major field of management and is now an important area of research in management studies. Moreover, information science has expanded its scope to examine the human–computer interaction, interfac­ ing, and interaction of people, ISs, and corporations. It is taught at all major universi­ ties and business schools worldwide.

Modern­day organizations have become intensely aware of the fact that informa­ tion and knowledge are potent resources that must be cultivated and honed to meet their needs. Thus information science or the study of ISs—that is, the application and usage of knowledge—focuses on why and how technology can be put to best use to serve the information flow within an organization.

Information science impacts information interfaces, influencing how people inter­ act with information and subsequently develop and use knowledge. The information a person acquires is added to his or her knowledge base. Knowledge is the awareness and understanding of an information set and ways that information can be made use­ ful to support a specific task or arrive at a decision.

Healthcare organizations are affected by and rely on the evolution of information science to enhance the recording and processing of routine and intimate information while facilitating human­to­human and human­to­systems communications, delivery of healthcare products, dissemination of information, and enhancement of the organiza­ tion’s business transactions. Unfortunately, the benefits and enhancements of informa­ tion science technologies have also brought to light new risks, such as glitches and loss of information and hackers who can steal identities and information. Solid leadership, guidance, and vision are vital to the maintenance of cost­effective business performance and cutting­edge, safe information technologies for the organization. This field studies all facets of the building and use of information. The emergence of information science and its impact on information have also influenced how people acquire and use knowledge.

Information science has already had a tremendous impact on society and will undoubtedly expand its sphere of influence further as it continues to evolve and inno­ vate human activities at all levels. What visionaries only dreamed of is now possible and part of reality. The future has yet to fully unfold in this important arena.

Introduction to Information Systems Consider the following scenario: You have just been hired by a large healthcare facil­ ity. You enter the personnel office and are told that you must learn a new language to work on the unit where you have been assigned. This language is used just on this unit. If you had been assigned to a different unit, you would have to learn another language that is specific to that unit, and so on. Because of the differences in various units’ languages, interdepartmental sharing and information exchange (known as interoperability) are severely hindered.

This scenario might seem far­fetched, but it is actually how workers once operated in health care—in silos. There was a system for the laboratory, one for finance, one for clinical departments, and so on. As healthcare organizations have come to appreciate the importance of communication, tracking, and research, however, they have devel­ oped integrated information systems that can handle the needs of the entire organization.

28 CHAPTER 2 Introduction to Information, Information Science, and Information Systems

Information and IT have become major resources for all types of organizations, and health care is no exception (see Box 2-1). Information technologies help to shape a healthcare organization, in conjunction with personnel, money, materials, and equipment. Many healthcare facilities have hired chief information officers (CIOs) or chief technical officers (CTOs), also known as chief technology officers. The CIO is in­ volved with the IT infrastructure, and this role is sometimes expanded to include the position of chief knowledge officer. The CTO is focused on organizationally based scientific and technical issues and is responsible for technological research and devel­ opment as part of the organization’s products and services. The CTO and CIO must be visionary leaders for the organization, because so much of the business of health care relies on solid infrastructures that generate potent and timely information and


Information System How It Is Used

Clinical Information System (CIS) Comprehensive and integrative system that manages the administrative, financial, and clinical aspects of a clinical facility; a CIS should help to link financial and clinical outcomes. An example is the EHR.

Decision Support System (DSS) Organizes and analyzes information to help decision makers formulate decisions when they are unsure of their decision’s possible outcomes. After gathering relevant and useful information, develops “what if” models to analyze the options or choices and alternatives.

Executive Support System Collects, organizes, analyzes, and summarizes vital information to help executives or senior management with strategic decision making. Provides a quick view of all strategic business activities.

Geographic Information System (GIS) Collects, manipulates, analyzes, and generates information related to geographic locations or the surface of the earth; provides output in the form of virtual models, maps, or lists.

Management Information Systems (MIS) Provides summaries of internal sources of information, such as information from the transaction processing system, and develops a series of routine reports for decision making.

Office Systems Facilitates communication and enhances the productivity of users needing to process data and information.

Transaction Processing System (TPS) Processes and records routine business transactions, such as billing systems that create and send invoices to customers, and payroll systems that generate employees’ pay stubs and wage checks and calculate tax payments.

Hospital Information System (HIS) Manages the administrative, financial, and clinical aspects of a hospital enterprise. It should help to link financial and clinical outcomes.

Introduction to Information Systems 29

knowledge. The CTO and CIO are sometimes interchangeable positions, but in some organizations the CTO reports to the CIO. These positions will become critical roles as companies continue to shift from being product oriented to knowledge oriented, and as they begin emphasizing the production process itself rather than the product. In health care, ISs must be able to handle the volume of data and information neces­ sary to generate the needed information and knowledge for best practices, because the goal is to provide the highest quality of patient care.

Information Systems ISs can be manually based, but for the purposes of this text, the term refers to computer-based information systems (CBISs). According to Jessup and Valacich (2008), CBISs “are combinations of hardware, software and telecommunications networks that people build and use to collect, create, and distribute useful data, typically in organizational settings” (p. 10). Along the same lines, ISs are also defined as “a col­ lection of interconnected elements that gather, process, store and distribute data and information while providing a feedback structure to meet an objective” (Stair & Reynolds, 2016, p. 4). ISs are designed for specific purposes within organizations. They are only as functional as the decision­making capabilities, problem­solving skills, and programming potency built in and the quality of the data and information input into them. The capability of the IS to disseminate, provide feedback, and adjust the data and information based on these dynamic processes is what sets them apart. The IS should be a user­friendly entity that provides the right information at the right time and in the right place.

An IS acquires data or inputs; processes data through the retrieval, analysis, or synthesis of those data; disseminates or outputs information in the form of reports, documents, summaries, alerts, prompts, or outcomes; and provides for responses or feedback. Input or data acquisition is the activity of collecting and acquiring raw data. Input devices include combinations of hardware, software, and telecommunications, including keyboards, light pens, touch screens, mice or other pointing devices, automatic scanners, and machines that can read magnetic ink characters or lettering. To watch a pay­per­view movie, for example, the viewer must first input the chosen movie, verify the purchase, and have a payment method approved by the vendor. The IS must acquire this information before the viewer can receive the movie.

Processing—the retrieval, analysis, or synthesis of data—refers to the alteration and transformation of the data into helpful or useful information and outputs. The processing of data can range from storing it for future use; to comparing the data, making calculations, or applying formulas; to taking selective actions. Processing devices consist of combinations of hardware, software, and telecommunications and include processing chips where the central processing unit (CPU) and main memory are housed. Some of these chips are quite ingenious. According to Schupak (2005), the bunny chip could save the pharmaceutical industry money while sparing “millions of furry creatures, with a chip that mimics a living organism” (para. 1). The HµREL Corporation has developed environments or biologic ISs that reside on chips and actually mimic the functioning of the human body. Researchers can use these environ­ ments to test for both the harmful and beneficial effects of drugs, including those that

30 CHAPTER 2 Introduction to Information, Information Science, and Information Systems

are considered experimental and that could be harmful if used in human and animal testing. Such chips also allow researchers to monitor a drug’s toxicity in the liver and other organs.

One patented HµREL microfluidic “biochip” comprises an arrangement of sepa­ rate but fluidically interconnected “organ” or “tissue” compartments. Each com­ partment contains a culture of living cells drawn from, or engineered to mimic the primary functions of, the respective organ or tissue of a living animal. Microfluidic channels permit a culture medium that serves as a “blood surrogate” to recirculate just as in a living system, driven by a microfluidic pump. The geometry and fluidics of the device are fashioned to simulate the values of certain related physiologic parameters found in the living creature. Drug candidates or other substrates of interest are added to the culture medium and allowed to recirculate through the device. The effects of drug compounds and their metabolites on the cells within each respective organ compartment are then detected by measuring or monitoring key physiologic events. The cell types used may be derived from either standard cell culture lines or primary tissues (HµREL Corporation, 2010, para. 2–3). As new technologies such as the HµREL chips continue to evolve, more and more robust ISs that can handle a variety of biological and clinical applications will be seen.

Returning to the movie rental example, the IS must verify the data entered by the viewer and then process the request by following the steps necessary to provide ac­ cess to the movie that was ordered. This processing must be instantaneous in today’s world, where everyone wants everything now. After the data are processed, they are stored. In this case, the rental must also be processed so the vendor receives payment for the movie, whether electronically, via a credit card or checking account with­ drawal, or by generating a bill for payment.

Output or dissemination produces helpful or useful information that can be in the form of reports, documents, summaries, alerts, or outcomes. A report is designed to inform and is generally tailored to the context of a given situation or user or user group. Reports may include charts, figures, tables, graphics, pictures, hyperlinks, ref­ erences, or other documentation necessary to meet the needs of the user. A document represents information that can be printed, saved, emailed, or otherwise shared, or displayed. Summaries are condensed versions of the original information designed to highlight the major points. An alert is comprised of warnings, feedback, or additional information necessary to assist the user in interacting with the system. An outcome is the expected result of input and processing. Output devices are combinations of hardware, software, and telecommunications and include sound and speech synthesis outputs, printers, and monitors.

Continuing with the movie rental example, the IS must be able to provide the con­ sumer with the movie ordered when it is wanted and somehow notify the purchaser that he or she has, indeed, purchased the movie and is granted access. The IS must also be able to generate payment either electronically or by generating a bill, while storing the transactional record for future use.

Feedback or responses are reactions to the inputting, processing, and outputs. In ISs, feedback refers to information from the system that is used to make modifica­ tions in the input, processing actions, or outputs. In the movie rental example, what if the consumer accidentally entered the same movie order three times, but really

Introduction to Information Systems 31

wanted to order the movie only once? The IS would determine that more than one movie order is out of range for the same movie order at the same time and provide feedback. Such feedback is used to verify and correct the input. If undetected, the viewer’s error would result in an erroneous bill and decreased customer satisfaction while creating more work for the vendor, which would have to engage in additional transactions with the customer to resolve this problem. The Nursing Informatics Practice Applications: Care Delivery section of this text provides detailed descriptions of clinical ISs that operate on these same principles to support healthcare delivery.

Summary Information systems deal with the development, use, and management of an organi­ zation’s IT infrastructure. An IS acquires data or inputs; processes data through the retrieval, analysis, or synthesis of those data; disseminates or outputs in the form of reports, documents, summaries, alerts, or outcomes; and provides for responses or feedback. Quality decision­making and problem­solving skills are vital to the develop­ ment of effective, valuable ISs. Today’s organizations now recognize that their most precious asset is their information, as represented by their employees, experience, competence or know­how, and innovative or novel approaches, all of which are de­ pendent on a robust information network that encompasses the information technol­ ogy infrastructure.

In an ideal world, all ISs would be fluid in their ability to adapt to any and all users’ needs. They would be Internet oriented and global, where resources are avail­ able to everyone. Think of cloud computing—it is just the beginning point from which ISs will expand and grow in their ability to provide meaningful information to their users. As technologies advance, so will the skills and capabilities to compre­ hend and realize what ISs can become. As wearable tracking technologies and other health­related mobile applications expand, more robust and timely health data will be generated, and this data will need to be processed into meaningful information. “Practitioners and medical researchers can look forward to technologies that enable them to apply data analysis to develop new insights into finding cures for difficult diseases. Healthcare CIOs and other IT leaders can expect to be called upon to man­ age all the new data and devices that will be transforming healthcare as we know it” (Schindler, 2015, para. 2). Devices with sensors communicating with each other is known as the Internet of Things (IoT) and the future possibilities for health care are tremendous. “The IoT raises the bar—enabling connection and communication from anywhere to anywhere—and allows analytics to replace the human decision­maker” (Glasser, 2015, para. 3). Essentially, the sensor­collected data are transmitted to another technology, triggering an action or an alert that prompts feedback for an action. For example, “imagine a miniaturized, implanted device or skin patch that monitors a diabetic’s blood sugar, movement, skin temperature and more, and informs an insulin pump to adjust the dosage” (para. 8).

It is important to continue to develop and refine functional, robust, visionary ISs that meet the current meaningful information needs while evolving systems that are even better prepared to handle future information and knowledge needs of the health­ care industry.

32 CHAPTER 2 Introduction to Information, Information Science, and Information Systems

References Cornell University. (2010). Information science. Retrieved from http://www.infosci.cornell.edu Goldstein, D., Groen, P., Ponkshe, S., & Wine, M. (2007). Medical informatics 20/20. Sudbury,

MA: Jones and Bartlett. Glasser, J. (2015). How the Internet of Things will affect health care. Hospitals and Health

Networks. Retrieved from http://www.hhnmag.com/articles/3438­how­the­internet­of ­things­will­affect­health­care

HµREL Corporation. (2010). Human­relevant: HµREL. Technology overview. Retrieved from http://www.hurelcorp.com/overview.php

Jessup, L., & Valacich, J. (2008). Information systems today (3rd ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

Schindler, E. (2015). Healthcare IT: Hot Trends for 2016, Part 1. InformationWeek. Retrieved from http://www.informationweek.com/healthcare/leadership/healthcare­it­hot­trends­for ­2016­part­1/d/d­id/1323722

Schupak, A. (2005). Technology: The bunny chip. Forbes. Retrieved from http://www.forbes .com/forbes/2005/0815/053.html

Stair, R., & Reynolds, G. (2016). Principles of information systems (12th ed.). Boston, MA: Cengage Learning.

Web Dictionary of Cybernetics and Systems. (2007). Technological determinism. Retrieved from http://pespmc1.vub.ac.be/ASC/TECHNO_DETER.html


1. How do you acquire information? Choose 2 hours out of your busy day and try to notice all of the information that you receive from your environment. Keep diaries indicating where the information came from and how you knew it was information and not data.

2. Reflect on an IS with which you are familiar, such as the automatic banking ma­ chine. How does this IS function? What are the advantages of using this system (i.e., why not use a bank teller instead)? What are the disadvantages? Are there enhancements that you would add to this system?

3. In health care, think about a typical day of practice and describe the setting. How many times does the nurse interact with ISs? What are the ISs that we interact with, and how do we access them? Are they at the bedside, handheld, or station based? How do their location and ease of access impact nursing care?

4. Briefly describe an organization and discuss how our need for information and knowledge impacts the configuration and interaction of that organization with other organizations. Also discuss how the need for information and knowledge influences the nature of work or how knowledge workers interact with and produce information and knowledge in this organization.

5. If you could meet only four of the rights discussed in this chapter, which one would you omit and why? Also, provide your rationale for each right you chose to meet.

References 33

1. Describe the essential components of com- puter systems, including both hardware and software.

2. Recognize the rapid evolution of computer systems and the benefit of keeping up-to-date with current trends and developments.

3. Analyze how computer systems function as tools for managing information and generating knowledge.

4. Define the concept of human–technology interfaces. 5. Assess how computers can support collaboration,

networking, and information exchange.


Key Terms » Acquisition » AMOLED (Active

Matrix Organic Light-Emitting Diode)

» Applications » Arithmetic logic

units » Basic input/output

system (BIOS) » Binary system » Bit » Bus » Byte » Cache memory » Central processing

unit (CPU) » Cloud computing » Cloud storage » Communication

software » Compact disk

read-only memory (CD-ROM)

» Compact disk- recordable (CD-R)

» Compact disk- rewritable (CD-RW)

» Compatibility » Computer » Computer science » Conferencing

software » Creativity software

» Database » Desktop » Digital video disk

(DVD) » Digital video disk-

recordable (DVD-R) » Digital video disk-

rewritable (DVD-RW) » Dissemination » Dots per inch (DPI)

switch » Double data rate

synchronous dynamic random- access memory (DDR SDRAM)

» Dynamic random access memory (DRAM)

» Email » Email client » Electronically eras-

able programmable read-only memory (EEPROM)

» Embedded device » Exabyte (EB) » Executes » Extensibility » FireWire » Firmware » Flash memory » Gigabyte (GB) » Gigahertz

» Graphical user interface

» Graphics card » Haptic » Hard disk » Hard drive » Hardware » High-definition

multimedia interface (HDMI)

» Information » Information Age » Infrastructure as a

service (IaaS) » Instant message (IM) » Integrated drive

electronics (IDE) » Internet browser » IPS LCD (In-Plane

Switching Liquid Crystal Display)

» Keyboard » Knowledge » Laptop » Main memory » Mainframes » Megabyte (MB) » Megahertz » Memory » Microprocessor » Microsoft Surface » Millions of instruc-

tions per second (MIPS)

» Mobile device » Modem » Monitor » Motherboard » Mouse » MPEG-1 Audio

Layer-3 (MP3) » Networks » Office suite » Open source » Operating system

(OS) » Parallel port » Peripheral compo-

nent interconnec- tion (PCI)

» Personal computer (PC)

» Petabytes (PB) » Platform as a

service (PaaS) » Plug and play » Port » Portability » Portable operating

system interface for UNIX (POSIX)

» Power supply » Presentation » Private cloud » Processing » Processor » Productivity


Introduction In this chapter, the discipline of computer science is introduced through a focus on computers and the hardware and software that make up these evolving systems; computer science is one of the building blocks of nursing informatics (refer to Figure 3-1). Computer science offers extremely valuable tools that, if used skillfully, can facilitate the acquisition and manipulation of data and information by nurses, who can then synthesize these into an evolving knowledge and wisdom base. This process can facilitate professional development and the ability to apply evidence-based practice decisions within nursing care, and if the results are disseminated and shared, can also advance the professional knowledge base.

This chapter begins with a look at common computer hardware, followed by a brief overview of operating, productivity, creativity, and communication software. It concludes with a glimpse at how computer systems help to shape knowledge and collaboration and an introduction to human–technology interface dynamics.

Computer Science and the Foundation of Knowledge Model Dee McGonigle, Kathleen Mastrian, and June Kaminski

» Professional development

» Programmable read- only memory (PROM)

» Public cloud » Publishing » Quantum bits

(Qubits) » Quantum

computing » QWERTY » Random-access

memory (RAM)

» Read-only memory (ROM)

» Security » Serial port » Small Computer

System Interface (SCSI)

» Software » Software as a

service (SaaS) » Sound card » Spreadsheet » Supercomputers

» Synchronous dynamic random- access memory (SDRAM)

» Technology » Terabytes (TB) » Throughput » Touch pad » Touch screen » Universal serial bus

(USB) » USB flash drive » User friendly

» User interface » Video adapter card » Virtual memory » Wearable technology » Wi-Fi » Wisdom » Word processing » World Wide Web

(WWW) » Yottabyte (YB) » Zettabyte (ZB) 



The Computer as a Tool for Managing Information and Generating Knowledge Throughout history, various milestones have signaled discoveries, inventions, or phil- osophic shifts that spurred a surge in knowledge and understanding within the human race. The advent of the computer is one such milestone, which has sparked an intel- lectual metamorphosis whose boundaries have yet to be fully understood. Computer technology has ushered in what has been called the Information Age, an age when data, information, and knowledge are both accessible and able to be manipulated by more people than ever before in history. How can a mere machine lead to such a revolu- tionary state of knowledge potential? To begin to answer this question, it is best to examine the basic structure and components of computer systems.

Essentially, a computer is an electronic information-processing machine that serves as a tool with which to manipulate data and information. The easiest way to begin to understand computers is to realize they are input–output systems. These unique machines accept data input via a variety of devices, process data through logical and arithmetic rendering, store the data in memory components, and output data and information to the user.

Since the advent of the first electronic computer in the mid-1940s, computers have evolved to become essential tools in every walk of life, including the profession of nursing. The complexity of computers has increased dramatically over the years,

36 CHAPTER 3 Computer Science and the Foundation of Knowledge Model

Figure 3-1 Building Blocks of Nursing Informatics

Nursing Informatics

Nursing Science

Computer Science

Cognitive Science

Information Science

and will continue to do so. “Computing has changed the world more than any other invention of the past hundred years, and has come to pervade nearly all human endeavors. Yet, we are just at the beginning of the computing revolution; today’s computing offers just a glimpse of the potential impact of computers” (Evans, 2010, p. 3). Major computer manufacturers and researchers, such as Intel, have identified the need to design computers to mask this growing complexity. The sophistication of computers is evolving at amazing speed, yet ease of use or user-friendly aspects are also increasing accordingly. This is achieved by honing hardware and software capa- bilities until they work seamlessly together to ensure user-friendly, intuitive tools for users of all levels of expertise. Box 3-1 provides information about haptic technology, computing surfaces, and multi-touch interfaces, which are evolving technologies.

The Computer as a Tool for Managing Information and Generating Knowledge 37


Dee McGonigle Do not get too attached to your mouse and keyboard, because they will be out- dated soon if Immersion, Microsoft, and PQ Labs have their way. From Immer- sion’s (2016) haptic technology, the Microsoft Surface (Microsoft Corporation, 2016), and PQ Labs (2016) multi-touch capabilities, have you ever thought of digital information you can touch and grab? The sense of touch is a powerful sense that we use daily. Haptic technology continues to advance and “brings the sense of touch to digital content” (Immersion, 2016, para. 4). Haptic technology combined with a visual display can be used to prepare users for tasks necessitat- ing hand–eye coordination, such as surgical procedures. Microsoft and PQ Labs are leading us into and evolving the next generation of computing, known as surface or table computing. Surface or table computing consists of a multi-touch, multiuser interface that allows one to “grab” digital information and then collab- orate, share, and store that information, without using a mouse or keyboard— just the hands and fingers and devices such as a digital camera or smartphone. These interfaces can actually sense objects, touch, and gestures from many users.

We can enter a restaurant and interact with the menu through the surface of the table where you sit to eat. Once you have completed your order, you can be- gin computing by using the capabilities built into the surface or using your own device, such as a smartphone. You can set a smartphone on the table’s surface and download images, graphics, and text to the surface. You can even communi- cate with others using full audio and video while waiting for your order. When you have finished eating, you simply set your credit card on the surface and it is automatically charged; you pick up your credit card and leave. This is a different kind of eating experience—but one that will become commonplace for the next generation of users. You can routinely experience this in Las Vegas, as well as in selected casinos, banks, restaurants, and hotels throughout the world.

You should seek to explore this new interface, which will forever change how we interact and compute. Think of the ramifications for health care especially as it relates to the haptic experience and wearables. Explore the Immersion refer- ence provided for you.

As our capabilities evolve, so does the complexity of computer operations. The goal for vendors that provide computer systems and software is to decrease the learn- ing curve for the user while enhancing the user’s capacity to manipulate the system to meet their computing needs. Therefore, the complexity of the operation is concealed by the ease of use.

One example of this type of complexity masked in simplicity is the evolution of “plug and play” computer add-ons, where a peripheral, such as an iPod or game console, can be simply plugged into a serial or other port and instantly used.

Computers are universal machines, because they are general-purpose, symbol- manipulating devices that can perform any task represented in specific programs. For instance, they can be used to draw an image, calculate statistics, write an essay, or record nursing care data. In a nutshell, computers can be used for data and informa- tion storage, retrieval, analysis, generation, and transformation.

Most computers are based on scientist John Von Neumann’s model of a processor– memory–input–output architecture. In this model, the logic unit and control unit are parts of the processor, the memory is the storage region, and the input and output segments are provided by the various computer devices, such as the keyboard, mouse, monitor, and printer. Recent developments have provided alternative configurations to the Von Neumann model—for example, the parallel computing model, where multiple processors are set up to work together. Nevertheless, today’s computer systems share the same basic configurations and components inherent in the earliest computers.

Components Hardware Computer hardware refers to the actual physical body of the computer and its com- ponents. Several key components in the average computer work together to shape a complex yet highly usable machine that serves as a tool for knowledge management, communication, and creativity.

Protection: The Casing The most noticeable component of any computer is the outer case. Desktop personal computers have either a desktop case, which lies horizontally (flat) on a desk, often with the computer monitor positioned on top of it; or a tower case, which stands ver- tically, and usually sits beside the monitor or on a lower shelf or the floor. Most cases come equipped with a case fan, which is extremely critical for keeping the computer

38 CHAPTER 3 Computer Science and the Foundation of Knowledge Model


Immersion. (2016). Touch. Feel. Engage. Retrieved from http://www.immersion.com /wearables

Microsoft Corporation. (2016). Designed on Surface: A global art project. Retrieved from https://www.microsoft.com/surface/en-us/art

PQ Labs. (2016). Introducing G5: 4K Touch Fidelity. Retrieved from http://multitouch .com/product.html

components cool when in use. Laptop and surface computers combine the compo- nents into a flat rectangular casing that is attached to the hinged or foldable monitor. Smartphones also have a protective outer plastic or metal case with a display screen.

Central Processing Unit (CPU)/Processor The central processing unit (CPU) is an older term for the processor and microprocessor. Sometimes conceptualized as the “brain” of the computer, the processor is the computer component that actually executes, calculates, and processes the binary computer code (which consists of various configurations of 0s and 1s), instigated by the operating system (OS) and other applications on the computer. The processor and microprocessor serve as the command center that directs the actions of all other computer components, and they manage both incoming and outgoing data that are processed across components. Some of the best processors include the AMD FX-9590, AMD FX-8320, AMD FX-6300, Intel Core i7-5820K, Intel Core i7- 4930K, Intel Core i7-5960X, Intel Core i5-6600K, and Intel Xeon processor (Futuremark, 2016).

The processor contains specific mechanical units, including registers, arithmetic logic units, a floating point unit, control circuitry, and cache memory. Together, these inner components form the computer’s central processor. Registers consist of data- storing circuits whose contents are processed by the adjacent arithmetic and logic units or the floating point unit. Cache memory is extremely quick memory that holds whatever data and code are being used at any one time. The processor uses the cache to store in-process data so that it can be quickly retrieved as needed. The processor is protected by a heat sink, a copper or aluminum metal block that cools the processor (often with the help of a fan) to prevent overheating (refer to Figure 3-2).

In the past, the speed and power of a processor were measured in units of megahertz and was written as a value in MHz (e.g., 400 MHz, meaning the microprocessor ran at 400 MHz, executing 400 million cycles per second). Today, it is more common to see the speed measured in gigahertz (1 GHz is equal to 1,000 MHz); thus a processor that operates at 4 GHz is 1,000 times faster than an older one that operated at 4 MHz. The more cycles a processor can complete per second, the faster computer programs can run. However, according to Anderson (2016),

Intel has said that new technologies in chip manufacturing will favour better energy consumption over faster execution times—effectively calling an end to “Moore’s Law,” which successfully predicted the doubling of density in integrated circuits, and therefore speed, every two years. (para. 1)

For example, the Intel Xeon processor E5-2699 v4 has a speed of 2.20 Ghz with 55 MB cache (Intel Corporation, 2016), making it more efficient at a lower speed.

In recent years, processor manufacturers, such as Intel, have moved to multicore microprocessors, which are chips that combine two or more processors. In fact, mul- tiple microprocessors have become a standard in both personal and professional-level computers. Minicomputers were replaced by servers using microprocessors and multi- processors have replaced most mainframes.

As mobile devices and embedded devices are being integrated into our daily routines, mainframes can create secure transactions with the analytics necessary for organizations

Components 39

to improve their business processes. IBM has found its niche and continues to build mainframes. According to Alba (2015),

The concept of a “mobile transaction” is a bit of marketing-speak. Tons of transactions take place via mobile devices, and the mainframe is good at transaction processing. Put them together, and voilà: a computer the size of a backyard shed becomes a mobile product. (para. 6)

Powerful supercomputers are also using collections of microprocessors.

Motherboard The motherboard has been called the “central nervous system” of the computer because it facilitates communication among all of the different computer components. This makes it a key foundational component because all other components are connected to it in some way (either directly via local sockets, attached directly to it, or connected via cables). This includes universal serial bus (USB) controllers, Ether- net network controllers, integrated graphics controllers, and so forth. The essential

40 CHAPTER 3 Computer Science and the Foundation of Knowledge Model

Figure 3-2 Computer Components





Hard drive



Heat sink

Power supply




Other input peripherals

Other output





CPU/processor Processes the data

Data and information remain in this memory while the computer is “on”

Displays the data and information

Inputs or enters and edits the data

Input Peripheral Processing/Memory Output Peripheral


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structures of the motherboard include the major chipset, Super Input/Output chip, basic input/output system read-only memory, bus communications pathways, and a variety of sockets that allow components to plug into the board. The chipset (often a pair of chips) determines the computer’s CPU type and memory. It also houses the north bridge and south bridge controllers that allow the buses to transfer data from one to another.

Power Supply The power supply is a critical component of any computer, because it provides the essential electrical energy needed to allow a computer to operate. The power supply unit converts the 120-volt AC main power (provided via the power cable from the wall socket into which the computer is plugged) into low-voltage DC power. Comput- ers depend on a reliable, steady supply of DC power to function properly. The more devices and programs used on a computer, the larger the power supply should be to avoid damage and malfunctioning. Power supplies normally range from 160 to 700 watts, with an average of 300 to 400 watts. Most contemporary power supply units come equipped with at least one fan to cool the unit under heavy use. The power supply is controlled by pressing the on and off switch, as well as the reset switch (which restarts the system) of a computer.

Laptop and other portable computing machines, such as electronic readers and tablet computers, are equipped with a both rechargeable battery power supply and the standard plug-in variety.

Hard Disk This component is so named because of the rigid hard disks that reside in it, which are mounted to a spindle that is spun by a motor when in use. Drive heads (most computers have two or more heads) produce a magnetic field through their transduc- ers that magnetizes the disk surface as a voltage is applied to the disk. The hard disk acts as a permanent data storage area that holds gigabytes (GB) or even terabytes (TB) worth of data, information, documents, and programs saved on the computer, even when the computer is shut off. Disk drives are not infallible, however, so backing up important data is imperative.

The computer writes binary data to the hard drive by magnetizing small areas of its surface. Each drive head is connected to an actuator that moves along the disk to hover over any point on the disk surface as it spins. The parts of the hard disk are encased in a sealed unit. The hard drive is managed by a disk controller, which is a circuit board that controls the motor and actuator arm assembly. The hard drive produces the voltage waveform that contacts the heads to write and read data, and handles communications with the motherboard. It is usually located within the com- puter’s hard outer casing. Some people also attach a second hard drive externally, to increase available memory or to back up data.

Main Memory or Random-Access Memory Random-access memory (RAM) is considered to be volatile memory because it is a tem- porary storage system that allows the processor to access program codes and data while working on a task. The contents of RAM are lost once the system is rebooted, is shut off, or loses power.

Components 41

The memory is actually situated on small chip boards, which sport rows of pins along the bottom edge and are plugged into the motherboard of the computer. These memory chips contain complex arrays of tiny memory circuits that can be either set by the processor during write operations (puts them into storage) or read during data retrieval. The circuits store the data in binary form as either a low (on) voltage stage, expressed as a 0, or a high (off) voltage stage, expressed as a 1. All of the work being done on a computer resides in RAM until it is saved onto the hard drive or other storage drive. Computers generally come with 2 GB of RAM or more, and some offer more RAM via graphics cards and other expan- sion cards.

A certain portion of the RAM, called the main memory, serves the hard disk and facilitates interactions between the hard disk and central processor. Main memory is provided by dynamic random access memory (DRAM) and is attached to the processor using specific addresses and data buses.

Synchronous dynamic random-access memory (SDRAM) (also known as static dynamic RAM) protects its data bits. The newer chip is double data rate synchronous dynamic random-access memory (DDR SDRAM) that allows for greater bandwidth and twice the transfers per the computer’s internal clock’s unit of time.

Read-Only Memory Read-only memory (ROM) is essential permanent or semipermanent nonvolatile memory that stores saved data and is critical in the working of the computer’s OS and other activities. ROM is stored primarily in the motherboard, but it may also be available through the graphics card, other expansion cards, and peripherals. In recent years, rewritable ROM chips that may include other forms of ROM, such as programmable read-only memory (PROM), erasable ROM, electronically erasable programmable read-only memory (EEPROM), and flash memory (a variation of electronically erasable program- mable ROM), have become available.

Basic Input/Output System The basic input/output system (BIOS) is a specific type of ROM used by the computer when it first boots up to establish basic communication between the processor, moth- erboard, and other components. Often called boot firmware, it controls the computer from the time the machine is switched on until the primary OS (e.g., Windows, Mac OS X, or Linux) takes over. The firmware initializes the hardware and boots (loads and executes) the primary OS.

Virtual Memory Virtual memory is a special type of memory that is stored on the hard disk to provide temporary data storage so data can be swapped in and out of the RAM as needed. This capability is particularly handy when working with large data-intensive programs, such as games and multimedia.

Integrated Drive Electronics Controller The integrated drive electronics (IDE) controller component is the primary interface for the hard drive, compact disk read-only memory (CD-ROM), digital video disk (DVD) drive, and the floppy disk drive (found largely on pre-2010 computers).

42 CHAPTER 3 Computer Science and the Foundation of Knowledge Model

Peripheral Component Interconnection Bus This component is important for connecting additional plug-in components to the computer. It uses a series of slots on the motherboard to allow peripheral component interconnection (PCI) card plug-in.

Small Computer System Interface The Small Computer System Interface (SCSI) component provides the means to attach additional devices, such as scanners and extra hard drives, to the computer.

DVD/CD Drive The CD-ROM drive reads and records data to portable CDs, using a laser diode to emit an infrared light beam that reflects onto a track on the CD using a mirror posi- tioned by a motor. The light reflected on the disk is directed by a system of lenses to a photodetector that converts the light pulses into an electrical signal; this signal is then decoded by the drive electronics to the motherboard. There are compact disk-recordable (CD-R) and compact disk-rewritable (CD-RW), digital video disk-recordable (DVD-R), and digital video disk-rewritable (DVD-RW) drives. A DVD drive can do everything a CD drive can do, plus it can play the content of disks and, if it is a recordable unit, can record data on blank DVDs.

Flash or USB Flash Drive This portable memory device uses electronically erasable programmable ROM to provide fast permanent memory. The USB flash drive is typically a removable and rewritable device that includes flash memory and an integrated USB interface. They are easily portable due to their small size and are durable and dependable, and obtain their power from the device they are connected to via the USB port.

Modem A modem is a component that can be situated either externally (external modem) or internally (internal modem) relative to the computer and enables Internet connectivity via a cable connection through network adapters situated within the computer apparatus.

Connection Ports All computers have connection ports made to fit different types of plug-in devices. These ports include a monitor cable port, keyboard and mouse ports, a network cable port, microphone/speaker/auxiliary input ports, USB ports, and printer ports (SCSI or parallel). These ports allow data to move to and from the computer via peripheral or storage devices. Specific ports include the following:

• Parallel port: Connects to a printer • Serial port: Connects to an external modem • USB: Connects to a myriad of plug-in devices, such as portable flash drives,

digital cameras, MPEG-1 Audio Layer-3 (MP3) players, graphics tablets, and light pens, using a plug-and-play connection (the ability to add devices automati- cally). The development of the USB Type-C–to–high definition multimedia interface (HDMI) adapter (Sexton, 2016) has expanded connectivity and transfer. HDMI is replacing analog video standards as an audio/video interface that can transfer

Components 43

compressed and uncompressed video and digital audio data from any device that is HDMI-compliant to compatible monitors, televisions, video projectors, and audio devices.

• FireWire (IEEE 1394): Often used to connect digital-video devices to the computer

• Ethernet: Connects networking apparatus, such as Internet and modem cables

Graphics Card Most computers come equipped with a graphics accelerator card slotted in the micro- processor of a computer to process image data and output those data to the monitor. These in situ graphic cards provide satisfactory graphics quality for two-dimensional art and general text and numerical data. However, if a user intends to create or view three-dimensional images or is an active game user, one or more graphics enhance- ment cards are often installed.

Video Adapter Cards Video adapter cards provide video memory, a video processor, and a digital-to-analog converter that works with the processor to output higher quality video images to the monitor.

Sound Card The sound card converts digital data into an analog signal that is then output to the computer’s speakers or headphones. The reverse is also accomplished by inputting a signal from a microphone or other audio recording equipment, which then converts the analog signal to a digital signal.

Bit A bit is the smallest possible chunk of data memory used in computer processing and is depicted as either a 1 or a 0. Bits make up the binary system of the computer.

Byte A byte is a chunk of memory that consists of 8 bits; it is considered to be the best way to indicate computer memory or storage capacity. In modern computers, bytes are described in units of megabytes (MB); gigabytes (GB), where 1 GB equals 1,000 MB; or terabytes (TB), where 1 TB equals 1 trillion bytes or 1,000 GB. Box 3-2 discusses storage capacities.

44 CHAPTER 3 Computer Science and the Foundation of Knowledge Model


Dee McGonigle and Kathleen Mastrian Storage and memory capacities are evolving. In the past few decades, there have been great leaps in data storage. It all begins with the bit, the basic unit of data storage, composed of 0s and 1s, also known as binary digits. A byte is generally considered to be equal to 8 bits. The files on a computer are stored as binary files. The software that is used translates these binary files into words, numbers, pic- tures, images, or video. Using this binary code in the binary numbering system,

Components 45

measurement is counted by factors of 2, such as 1, 2, 4, 8, 16, 32, 64, and 128. These multiples of the binary system in computer usage are also prefixed based on the metric system. Therefore, a kilobyte (KB) is actually 2 to the 10th power (210) or 1,024 bytes, but is typically considered to be 1,000 bytes. This is why one sees 1,024 or multiples of that number instead of an even 1,000 mentioned at times in relation to kilobytes.

In the early 1980s, kilobytes were the norm as far as computer capacity went, and 128 KB machines were launched for personal use. Subsequent decades, how- ever, have seen advanced computing power and storage capacity. As capabilities soared, so did the ability to save and store what was used and created. Mega- bytes (MB) emerged as a common unit of measure; 1 megabyte is 1,048,576 bytes but is considered to be roughly equivalent to 1 million bytes. The next leap in computer capacity was one that some people could not even imagine: gigabytes (GB). A gigabyte is 1,073,741,824 bytes but is generally rounded to 1 billion bytes. Some computing experts are very concerned that valuable bytes are lost when these measurements are rounded, whereas hard drive manufactur- ers use the decimal system so their capacity is expressed as an even 1 billion bytes per gigabyte.

Computer capacity has moved into and beyond the range of terabytes, with capacities moving into the range of petabytes (PB), exabytes (EB), zettabytes (ZB), and yottabytes (YB). These terms for storage capacity are defined as follows:

1 TB = 1,000 GB 1 PB = 1,000,000 GB 1 EB = 1,000 PB 1 ZB = 1,000 EB 1 YB = 1,000 ZB

To put all of this in perspective, Lyman and Varian describe the data powers of 10:

2 KB: A typewritten page 2 MB: A high-resolution photograph 10 MB: A minute of high-fidelity sound or a digital chest X-ray 50 MB: A digital mammogram 1 GB: A symphony in high-fidelity sound or a movie at TV quality 1 TB: All the X-ray films in a large, technologically advanced hospital 2 PB: The contents of all U.S. academic research libraries 5 EB: All words ever spoken by human beings

We have not even addressed ZB and YB. Stay tuned . . .


Lyman, P., & Varian, H. R. (2003). How much information? Retrieved from http://groups .ischool.berkeley.edu/archive/how-much-info-2003/

46 CHAPTER 3 Computer Science and the Foundation of Knowledge Model

Software Software comprises the application programs developed to facilitate various user func- tions, such as writing, artwork, organizing meetings, surfing the Internet, communi- cating with others, and so forth. For the purposes of this overview, the various types of software have been divided into four categories: (1) OS software, (2) productivity software, (3) creativity software, and (4) communication software.

User friendliness is a critical condition for effective software adoption. The easier and more intuitive a software package seems to be to a user influences that user’s per- ception of how clear the package is to understand and to use. The rapid evolution of hardware mentioned previously has been equally matched by the phenomenal devel- opment in software over the past three or four decades.

Commercial Software Several large commercial software companies, such as Apple, Microsoft, IBM, and Adobe, dominate the market for software, and have done so since the advent of the personal computer (PC). Licensed software has evolved over time; hence, most products have a long version history. Many software packages, such as office suites, are expen- sive to purchase; in turn, there is a “digital divide” as far as access and affordability go across societal spheres, especially when viewed from a global perspective.

Open Source Software The open source initiative began in the late 1990s and has become a powerful move- ment that is changing the software production and consumer market. In addition to commercially available software, a growing number of open source software pack- ages are being developed in all four of the categories addressed in this chapter. The open source movement was begun by developers who wished to offer their creations to others for the good of the community and encouraged them to do the same. Users who modify or contribute to the evolution of open source software are obligated to share their new code, but essentially the software is free to all. Apache OpenOffice, Google Docs, and NeoOffice are examples of open source productivity software (refer to Figure 3-3).

OS Software The OS is the most important software on any computer. It is the very first program to load on computer start-up and is fundamental for the operation of all other software and the computer hardware. Examples of commonly used OSs include the Microsoft Windows family, Linux, and Mac OS X. The OS manages both the hardware and the software and provides a reliable, consistent interface for the software applications to work with the computer’s hardware. An OS must be both powerful and flexible to adapt to the myriad of types of software available, which are made by a variety of development companies. New versions of the major OSs are equipped to deal with multiple users and handle multitasking with ease. For example, a user can work on a word processing document while listening for an “email received” signal, have an Internet browser window open to look for references on the Internet as needed, listen to music in the CD drive, and download a file—all at the same time.

Components 47

OS tasks can be described in terms of six basic processes:

• Memory management • Device management • Processor management • Storage management • Application interface • User interface (usually a graphical user interface [GUI])

A GUI (pronounced “gooey”) is used by OSs to display a combination of graphics and text such as icons, drop-down menus, and buttons; it allows you to use input and output devices as well as icons that represent files, programs, actions, and processes.

OSs should be convenient to use, easy to learn, reliable, safe, and fast. They should also be easy to design, implement, and maintain and should be flexible, reliable, error free, and efficient. For example, Silbershatz, Baer Galvin, and Gagne (2013) described how “Microsoft’s design goals for Windows included security, reliability, Windows and POSIX application compatibility, high performance,

Figure 3-3 Open Source Software

Open Source Software

Collaborative effort

Publicly accessible source code is shared with the users

and developers

Stumbling blocks • Lack of interoperability • People are not aware or familiar with open source

Benefits transparency; free users are encouraged to modify, enhance existing or create new versions

48 CHAPTER 3 Computer Science and the Foundation of Knowledge Model

extensibility, portability, and international support” (p. 831). The following goals were established by Microsoft:

• Portability: The OS can be moved from one hardware architecture to another with few changes needed.

• Security: The OS incorporates hardware protection for virtual memory and soft- ware protection mechanisms for OS resources, including encryption and digital signature capabilities.

• Portable operating system interface for Unix (POSIX) compliance: Applications designed to follow the POSIX (IEEE 1003.1) standard can be compiled to run on Windows without changing the source code. Windows OSs have varying levels of compatibility with the applications that ran on earlier versions of Windows OSs.

• Multiprocessor support: The OS is designed for symmetrical multiprocessing. • Extensibility: This capability is provided by using a layered architecture with a

protected executive layer for basic system services, several server subsystems that operate in user mode, and a modular structure that allows additional envi- ronmental subsystems to be added without affecting the executive layer.

• International support: The Windows OS supports different locales via the na- tional language support application programming interface (API).

• Compatibility with MS-DOS and MS-Windows applications.

Productivity Software Productivity software, such as an office suite, is the type of software most commonly used both in the workplace and on personal computers. Several software companies produce this type of multiple-program software, which usually bundles together word processing, spreadsheet, database, presentation, Web development, and email programs.

The intent of office suites is generally to provide all of the basic programs that office or knowledge workers need to do their work. The bundled programs within the suite are organized to be compatible with one another, are designed to look similar to one another for ease of use, and provide a powerful array of tools for data manipulation, information gathering, and knowledge generation. Some office suites add other programs, such as database creation software, mathematical editors, drawing, and desktop publishing programs. Table 3-1 summarizes the application of programs included in some of the popular office suites: Microsoft Office, Apache OpenOffice, NeoOffice, Corel WordPerfect Suite, and Apple iWork.

Creative Software Creative software includes programs that allow users to draw, paint, render, record music and sound, and incorporate digital video and other multimedia in professional aesthetic ways to share and convey information and knowledge (Table 3-2).

Communication Software Networking and communication software enable users to dialogue, share, and network with other users via the exchange of email or instant message (IM), by accessing the World Wide Web, or by engaging in virtual meetings using conferencing software (Table 3-3)

Components 49

Acquisition of Data and Information: Input Components Input devices include the keyboard; mouse; joysticks (typically used for playing com- puter games); game controllers or pads; Web cameras (webcams); stylus (often used with tablets or personal digital assistants); image scanners for copying a digital image of a document or picture; touch pads; or other plug-and-play input devices, such as digital cameras, digital video recorders (camcorders), MP3 players, electronic musical instruments, and physiologic monitors. These devices are the origin or medium used to input text, visual, audio, or multimedia data into the computer system for viewing, listening, manipulating, creating, or editing. The primary input devices on a computer are the keyboard, mouse, touch pad, and touch screen.

Keyboard A computer keyboard is very similar to the typewriter keyboards of earlier days and usually serve as the prime input device that enables the user to type words, numbers, and commands into the computer’s programs. Standard computer key- boards have 110 keys and are organized to facilitate Latin-based languages using a QWERTY layout (so named because these letters appear on the first six keys in the first row of letters).

Table 3-1 Office Suite Software Features and Examples

Office Suite Software

Program Application Examples

Word processing Composition, editing, formatting, and producing text documents

Microsoft Word, Open Office Writer, KOffice KWord, Corel WordPerfect or Corel Write, Apple Pages

Spreadsheets Grid-based documents in ledger format; organizes numbers and text; calculates statistical formulae

Microsoft Excel, Open Office Calc, KOffice KSpread, Corel Quattro Pro, Apple Numbers

Presentations Slideshow software, usually used for business or classroom presentations using text, images, graphs, media

Microsoft PowerPoint, Open Office Impress, KOffice KPresenter, Corel Show, Apple Keynote

Databases Database creation for text and numbers Microsoft Access (in elite packages), Open Office Base, KOffice Kexi, Corel Calculate, Corel Paradox

Email Integrated email program to send and receive electronic mail

Microsoft Outlook, Corel WordPerfect Mail, Mozilla Thunderbird

Drawing Graphics and diagram drawing Open Office Draw, Corel Presentation Graphics, KOffice Kivio, Karbon, Krita

Math formulas Inserts math equations in word processing and presentation work

Open Office Math, KOffice KFormula

Desktop publishing Page layouts and publication-ready documents Microsoft Publisher (in elite packages), Apple Pages

50 CHAPTER 3 Computer Science and the Foundation of Knowledge Model

Certain keys are used as command keys, particularly the control (CTRL), alternate (Alt), delete (Del), and shift keys, which can all be used to activate useful commands. The escape (ESC) key allows the user instantly to exit a process or program. The F keys, numbered F1 through F12, are function keys. They are used in different ways by particular programs. If a program instructs users to press the “F8” key, they would do so by pressing F8. The print screen (PrtSc) key sends a graphical picture or screen shot of a computer screen to the clipboard. This cop- ied screen shot can then be pasted in any graphic program that can work with bitmap files.

Some keyboards have a wire and plug in, while others are wireless or cord- less. Touch screen or virtual keyboards are those being incorporated into the touch screens of phones, gaming machines, and tablets, and they are also available through ease-of-access tools on laptops.

Table 3-2 Creative Software Features and Examples

Creative Software

Program and Application Software Examples

Raster graphics programs

Draw, paint, render, manipulate, and edit images, fonts, and photographs to create pixel-based (dot points) digital art and graphics.

Adobe Photoshop and Fireworks, Ulead PhotoImpact, Corel Draw, Painter, and Paint Shop Pro, GIMP (open source), KOffice Krita (open source)

Vector graphics programs

Mathematically rendered, geometric modeling is applied through shapes, curves, lines, and points and manipulated for shape, color, and size. Ideal for printing and three-dimensional (3D) modeling.

Adobe Flash, Freehand, and Illustrator; CorelDraw and Designer, Open Office Draw (open source), Mirosoft Visio, Xara Xtreme, KOffice Karbon14 (open source)

Desktop publishing programs

Page layout and publishing preparation for printed and Web documents, such as magazines, journals, books, newsletters, and brochures.

Adobe InDesign, Corel PageMaker, Microsoft Publisher, Scribus (open source), QuarkXPress, Apple Pages (note that many of the graphics programs can also be used for DTP)

Web design programs

Create, edit, and update webpages using specific codes, such as XML, CSS, HTML, and Java.

Adobe Dreamweaver, Coffee Cup, Microsoft FrontPage, Nvu (open source), W3C’s Amaya (open source)

Multimedia programs

Combines text, audio, images, animation, and video into interactive content for electronic presentation.

Adobe Flash, Microsoft Movie Maker, Apple QuickTime and FinalCut Studio, Corel VideoStudio, Ulead VideoStudio, Real Studio, CamStudio (open source), Audacity (open source)

Components 51

Mouse The mouse is the second-most-commonly used input device. It is manipulated by the user’s hand to point, click, and move objects around on the computer screen. A mouse can come in a number of different configurations, including a standard mechanical trackball serial mouse, bus mouse, PS/2 mouse, USB-connected mouse, optical lens mouse, cordless mouse, and optomechanical mouse. Even though “the mouse may be a simple device in concept,” it has evolved and increased in complexity and capability over time (Bagaza & Westover, 2016, para. 2). For example, “[g]aming mice take the basic mouse concept and amplify every element to extremes” (Bagaza & Westover, para. 4). Some manufacturers offer specialized features, but there is a com- mon “combination of high-performance parts—laser sensors, light-click buttons, and gold-plated USB connectors—and customization, like adjustable weight, programma- ble macro commands, and on-the-fly DPI switching. For non-gamers, these features are overkill; for dedicated gamers, they provide a competitive edge” (Bagaza & Westover, para. 4). The dots per inch (DPI) switch is an actual switch on a computer

Table 3-3 Communication Software Features and Examples

Communication Software

Email client

Allows user to read, edit, forward, and send email messages to other users via an Internet connection. The software can be resident on the computer or accessed via the World Wide Web.

Resident programs

Microsoft Outlook and Outlook Express, Eudora, Pegasus, Mozilla Thunderbird, Lotus Notes

Web-based programs

Gmail, Yahoo Mail, Hotmail

Internet browsers

Enables user to access, browse, download, upload, and interact with text, audio, video, and other Web-based documents.

Mozilla Firefox, Microsoft Internet Explorer, Google Chrome, Apple Safari, Opera, Microbrowser (for mobile access)

Instant messaging (IM)

Real-time text messaging between users, can attach images, videos, and other documents via personal computer, cell phone, handheld devices.

MSN Instant Messenger, Microsoft Live Messenger, Yahoo Messenger, Apple iChat


Enables user to communicate in a virtual meeting room setting to share work, discussions, planning, using an intranet or Internet environment; can exhibit files, video, and screenshots of content.

Adobe Acrobat Connect, Microsoft Live Meeting or Meeting Space, GoToMeeting, Meeting Bridge, Free Conference, RainDance, WebEx

mouse that allows you to adjust the mouse’s sensitivity to movement, as in faster or slower mouse pointer speeds. Having the ability to do this on the fly or as needed without pausing could enhance the computing or gaming experience.

Touch Pad The touch pad is a device that senses the pressure of the user’s finger along with the movement of the finger on the touch pad to control input positioning. It is an alterna- tive to using a mouse.

Touch Screen The touch screen is a display used as an input device for interacting with or relating to the display’s materials or content. The user can touch or press on the designated display area to respond, execute, or request information or output.

Processing of Data and Information: Throughput/Processing Components All of the hardware discussed earlier in this chapter is involved in the throughput or processing of input data and in the preparation of output data and information. Specific software is used, depending on the application and data involved. One key hardware component, the computer monitor, is a unique example of a visible throughput component—it is the part of the computer that users focus on the most when they are working on a computer. Input data can be visualized and accessed by manipulating the mouse and keyboard input devices, but it is the monitor that receives the user’s attention. The monitor is critical for the efficient rendering dur- ing this part of the cycle, because it facilitates user access and control of the data and information.

Monitor The monitor is the visual display that serves as the landscape for all interactions between user and machine. It typically resembles a television screen, and comes in various sizes (usually ranging from 15 to 21 inches) and configurations. Monitors either are based on cathode ray tubes (the conventional monitor with a large section behind the screen) or are thinner, flat-screen liquid crystal display devices. Some com- puter monitors also have a touch screen that can serve as an input device when the user touches specific areas of the screen.

Monitors vary in their refresh rate (usually measured in megahertz) and dot pitch. Both of these characteristics are important for user comfort. The faster the refresh rate, the cleaner and clearer the image on the screen, because the monitor refreshes the screen contents more frequently. For example, a monitor with a 100 MHz refresh rate refreshes the screen contents 100 times per second. Similarly, the larger the dot pitch factor, the smaller the dots that make up the screen image, which provides a more detailed display on the monitor and also facilitates clarity and ease of viewing.

If equipped with a touch screen, a monitor can also serve as an input device when activated by a stylus or finger pressure. Some users might also consider the monitor to be an output device, because access to input and stored documents is often performed via the screen (e.g., reading a document that is stored on the computer or viewable

52 CHAPTER 3 Computer Science and the Foundation of Knowledge Model

from the Internet). As we advance to more engaged computing, larger screens and ultra-wide monitors are evolving to provide immersive experiences.

Smartphone displays can be a form of AMOLED (Active Matrix Organic Light-Emitting Diode) or IPS LCD (In-Plane Switching Liquid Crystal Display) . In the AMOLED type, the individual pixels are lit separately (active matrix); the next-generation super AMOLED type includes touch sensors. The IPS LCD–type uses polarized light passing through a color filter and all of the pixels are backlit. The liquid crystals control the brightness and which pixels are on or off. With the active matrix, you have crisp, vivid colors and darker blacks.

Dissemination: Output Components Output devices carry data in a usable form through exit devices in or attached to a computer. Common forms of output include printed documents, audio or video files, physiologic summaries, scan results, and saved files on portable disk drives, such as a CD, DVD, flash drive, or external hard drive. Output devices literally put data and information at the user’s fingertips, which can then be used to develop knowledge and even wisdom. The most commonly used output devices include printers, speakers, and portable disk drives.

Printer Printers are external components that can be attached to a computer using a printer cord that is secured into the computer’s printer port. Printers enable users to print a hard paper copy of documents that are housed on the computer.

The most common printer types are the inkjet and laser printers. Inkjet printers are more economical to use and offer good quality print; they apply ink to paper using a jet-spray mechanism. Laser printers produce publisher-ready quality print- ing if combined with good-quality paper, but cost more in terms of printing supplies. Both types of printers can print in black and white or in color. Printers can be single function (print only), but typically they are all-in-one machines or multifunction printers that can also scan, fax, and copy. There are printers that can be accessed via the Internet using Wi-Fi. There are also three-dimensional (3D) printers that can create a 3D solid object produced layer by layer from a 3D software digital file.

Speakers All computers have some sort of speaker setup, usually small speakers embedded in the monitor, in the case, or, if a laptop, close to the keyboard. Often, external speak- ers are added to a computer system using speaker connectors; these devices provide enhanced sound and a more enjoyable listening experience.

What Is the Relationship of Computer Science to Knowledge? Scholars and researchers are beginning to understand the effects that computer sys- tems, architecture, applications, and processes have on the potential for knowledge acquisition and development. Users who have access to contemporary computers

What Is the Relationship of Computer Science to Knowledge? 53

54 CHAPTER 3 Computer Science and the Foundation of Knowledge Model

equipped with full Internet access have resources at their fingertips that were only dreamed of before the 21st century. Entire library collections are accessible, with many documents available in full printable form. Users are also able to contribute to the development of knowledge through the use of productivity, creativity, and communication software. In addition, using the World Wide Web (WWW) interface, us- ers are able to disseminate knowledge on a grand scale with other users. This deluge of information available via computers must be mastered and organized by the user if knowledge is to emerge. Discernment and the ability to critique and filter this in- formation must also be present to facilitate the further development of wisdom.

The development of an understanding of computer science principles as they apply to technology used in nursing can facilitate optimal usage of the technology for knowledge development in the profession. The maxim that “knowledge is power” and that the skillful use of computers lies at the heart of this power is a presump- tion. Once nurses become comfortable with the various technologies, they can shape them, refine them, and apply them in new and different ways, just as they have always adapted earlier equipment and technologies. Nurses must harness the power of data and information through the use of computer technologies to build knowledge and gain wisdom.

How Does the Computer Support Collaboration and Information Exchange? Computers can be linked to other computers through networking software and hard- ware to promote communication, information exchange, work sharing, and collabo- ration. Such networks can be local or organizationally based, with computers joined together into a local area network; organized on a wider area scope (e.g., a city or district) using a metropolitan area network; or encompassing computers at an even greater distance (e.g., a whole country or continent, or the Internet itself) using a wide area network configuration (Sarkar, 2006). Network interface cards are used to con- nect a computer and its modem to a network.

Networks within health care can manifest in several different configurations, including client-focused networks, such as in telenursing, e-health, and client support networks; work-related networks, including virtual work and virtual social networks; and learning and research networks, as in communities of practice. These trends are still evolving in most nursing work environments (and most nurses’ personal lives), but they are predicted to continue to grow dramatically. We are experiencing one of the greatest upsurges in shared information and our ability to access, exchange, and utilize this information to enhance knowledge.

Virtual social networks are another form of professional network that have expanded phenomenally since the advent of the Internet and other computer soft- ware and hardware. Nursing-related virtual social networks provide a cyberspace for nurses to make contacts, share information and ideas, and build a sense of community.

Social communication software is used to provide a dynamic virtual environ- ment, and often virtual social networks provide communicative capabilities through

How Does the Computer Support Collaboration and Information Exchange? 55

posting tools, such as blogs, forums, and wikis; email for sharing ideas on a smaller scale; collaborative areas for interaction, creating, and building digital artifacts or planning projects; navigation tools for moving through the virtual network land- scape; and profiles to provide a space for each member to disclose personal informa- tion with others. Nurses who have to engage in shift work often find that virtual social networks can provide a sense of connection with other professionals that is available around the clock. Because time is often a factor in any social interchange, virtual communication offers an alternative for practicing nurses, who can access information and engage in interchanges at any time of day. With active participation, the interchanges and shared information and ideas of the network can culminate in valuable social and cultural capital, available to all members of that network. Often, nursing virtual social networks are created for the purpose of exchanging ideas on practice issues and best practices; to become more knowledgeable about new trends, research, and innovations in health care; or to participate in advocacy, activist, and educational initiatives.

Through the use of portable disk devices, such as flash drives, CDs, and DVDs, as well as Web-based and cloud spaces, people can share information, documents, and communications by exchanging files. Since the advent of the Internet in the mid-1980s, the World Wide Web has evolved to become a viable and user-friendly way for people to collaborate and exchange information, projects, and other knowledge-based files, such as websites, email, social networking applications, and webinar logs. Box 3-3 pro- vides information on Web 2.0, the latest iteration of the World Wide Web, and beyond.


Dee McGonigle, Kathleen Mastrian, and Wendy Mahan Web 2.0—the name given to the new World Wide Web tools—enables users to collaborate, network socially, and disseminate knowledge with other users on a scale that was once not even comprehensible. These programs promote data and information exchange, feedback, and knowledge development and dissemination.

To facilitate a selective review of the Web 2.0 tools available, they have been categorized into three areas here: (1) tools for creating and sharing information, (2) tools for collaborating, and (3) tools for communicating. Examples of tools for creating and sharing information include blogs, podcasts, Flickr, YouTube, Hellodeo, Jing, Screencast-o-matic, Facebook, MySpace, Box, Samepage, Wrike, Snapchat, and MakeBeliefsComix. Examples of tools for collaborating with oth- ers include Google Docs, Zoho, wikis, Del.icio.us, and Gliffy. Finally, some tools for communicating with others include Adobe Connect, GoToMeeting, BlueJeans, WebEx Meeting Center, Vyew, Skype, Twitter, and instant messaging.

The application of the creating and sharing information tools has led to an explosion of social networking on the Web. YouTube has promoted the “broad- cast yourself” proliferation. Anyone can post a video onto YouTube that is shared with others over the Web. Similarly, Flickr allows users to upload and tag personal photos to share either privately or publicly. Facebook and MySpace

56 CHAPTER 3 Computer Science and the Foundation of Knowledge Model

both promote socializing on the Web. Facebook is a social utility and MySpace is a place for friends, according to the descriptions found on these websites. Other tools let users create and share recorded messages, diagrams, screen captures, and even custom comic strips.

Collaborating over the Web has become easier. Indeed, it is a way of life for many people. Google Docs and Zoho allow users to create online and share and collaborate in real time. Wikis are server-based software programs that enable users to generate and edit webpage content using any browser. Del.icio.us is a social bookmarking manager that uses tags to identify or describe the bookmarks that can be shared with others.

Communicating with others includes audio- and videoconferencing in real time. Adobe Connect is a comprehensive Web communications solution. Although a fee- based service, it does provide a free trial. Users should read all of the documentation on Adobe’s site before downloading, installing, and using this software. Vyew is free, always-on collaboration plus live webinars. Skype allows users to make calls in audio only or with video. Users can download Skype for free but depending on the type of calls made, fees or charges could be assessed. Individuals should read through all of the information before downloading, installing, and using this software. Twitter allows participants to answer the question “What are you doing?” with messages containing 140 or fewer characters. Although Twitter can be used to keep the friends in a person’s network updated on daily activities, it can also be used for other purposes, such as asking questions or expressing thoughts. In addi- tion, Twitter can be accessed by cell phones, so users can stay in touch on the go.

Along with all of the advantages and intellectual harvesting capabilities from the use of these tools come serious security issues. Wagner (2007) warned the user to “bear in mind before you jump in that you’re giving information to a third-party company to store” (para. 5). He also states that “you should talk to your company’s legal and compliance offices to be sure you’re obeying the law and regulations with regard to managing company’s information” (para. 5). One suggestion that Wagner offers is that if you do not want to involve a third party, “Wikis provide a good alternative for organizations looking to maintain control of their own software. Organizations can install wiki software on their own, internal servers” (para. 6).

This new wave of Web-based tools facilitates the ability to interact, exchange, collaborate, communicate, and share in ways that have only begun to be realized. As the tools and their innovative uses continue to expand, users need to stay vigi- lant to handle the associated security challenges. These Web 2.0 and beyond tools are providing a new cyber-playground that is limited only by users’ own imagi- nations and intelligence. We encourage you to explore these tools.


Wagner, M. (2007). Nine easy Web-based collaborative tools. Forbes. Retrieved from http://www.forbes.com/2007/02/26/google-microsoft-bluetie-enttech-cx_mw _0226smallbizresource.html

Cloud Computing 57

Cloud Computing Cloud computing has Web browser–based login-accessible data, software, and hardware that you can access and use. Using the cloud, you could link systems together and reduce costs (Figure 3-4). According to Griffith (2016), “cloud computing means stor- ing and accessing data and programs over the Internet instead of [on] your computer’s hard drive. The cloud is just a metaphor for the Internet” (para. 2). IBM (2016) stated that cloud computing, “referred to as simply ‘the cloud,’ is the delivery of on-demand computing resources—everything from applications to data centers—over the Internet on a pay-for-use basis” (para. 1). IBM described services as elastic resources, either metered or self-service. Elastic resources refer to those that are able to be scaled up or down to meet the consumer’s needs. Metered services allow you to pay only for what you use, and self-service refers to having self-service access to all of the IT resources the consumer needs. Woodford (2016) stated that cloud computing is different because it is managed; on-demand; and can be public, private, or a hybrid of both. The public cloud is owned and operated by companies offering public access to computing resources. It is believed to be more affordable and economically sound because the user does not need to purchase the hardware, software, or supporting infrastructure, as these are managed and owned by the cloud provider (IBM, 2016). The private cloud is operated for a single organization with the infrastructure being managed and/or hosted internally or out- sourced to a third party; it provides added control and avoids multi-tenancy (IBM).

Figure 3-4 Cloud Computing

Cloud Computing advantages

Agile deployment Increased


Pay as you go as needed



Situational and context-driven

Enhanced collaboration

Scalable: Dynamic ramp up or dynamic

ramp down

Resources: Shared and distributed


Less capital expenditures

Market adaptability

User-distinct encounters and


58 CHAPTER 3 Computer Science and the Foundation of Knowledge Model

As we explore Web-based apps and computing over the Internet, we are cloud computing. Griffith (2016) described some common major examples of cloud computing that you might be using right now: Google Drive, Microsoft Office Online, Microsoft OneDrive, Apple iCloud, Amazon Cloud Drive, Box, Dropbox, and SugarSync. There is also cloud hardware; the primary example of a device that is completely cloud-centric is the Chromebook, a laptop that has just enough local storage and power to run the Chrome OS, which essentially turns the Google Chrome Web browser into an operating system. “With a Chromebook, most everything you do is online: apps, media, and storage are all in the cloud” (Griffith, 2016, para. 16).

Cloud storage is data storage provided by networked online servers that are typi- cally outside of the institution whose data are being housed.

There are also additional services based in the cloud that are mainly business related: software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) (Figure 3-5). SaaS, such as Salesforce.com refers to cloud-based applica- tions with the following benefits: quickly start using innovative or specific business apps that are scalable to your needs, any connected computer can access the apps and data, and data is not lost if your hard drive crashes because the data is stored in the cloud (Griffith, 2016; IBM, 2016). PaaS provides everything needed to support the cloud application’s building and delivery, enabling users to develop and launch custom Web applications rapidly to the cloud (Griffith, 2016; IBM, 2016). IaaS such as Amazon, Microsoft, Google, and Rackspace provide a rentable backbone to companies, en- abling the scalable, on-demand infrastructure they need to support their dynamic

Figure 3-5 Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS)

Cloud Computing

On-demand Self-service Resource

Pooling Broad Network Access

SaaS Software as a Service


Rapid Elasticity

PaaS Platform as a Service

2 IaaS Infrastructure as a Service


Measured Services

Looking to the Future 59

workloads; the user pays only for what they use and he or she does not have to invest in hardware such as networks, storage, and data center space (Griffith, 2016; IBM, 2016). You can access and receive services from Netflix and Pinterest because they are customers of Amazon’s cloud services. According to Griffith (2016), cloud computing is truly big business and could generate 500 billion dollars within the next 5 years.

Cloud computing is Internet computing, and it has the same pitfalls and benefits as using the Internet. Some are not sold on the claims that it is totally reliable, safe, and/or secure. Others believe it is a more environmentally friendly option because it uses fewer resources and less energy, and yet many people can share efficiently man- aged, centralized cloud-based systems (Woodford, 2016). One of the driving forces behind the initiation of cloud computing was the need for scalable resources that are affordable. As with anything on the Internet, these resources can be shared or pri- vately held. Cloud computing will continue to grow as long as there is demand and it can meet the scalability requirements while maintaining secure, reliable spaces.

In an ideal world, nurses would be able to use and interact with computer tech- nologies effectively to enhance patient care. They would understand computer science and know how to harness its capabilities to benefit the profession and ultimately their patients.

Looking to the Future The use of the cloud will continue to expand. The market for wearable technology, which is comprised of smaller and faster handheld and portable computer systems, and high-quality voice-activated inventions will further facilitate the use of comput- ers in nursing practice and professional development. The field of computer science will continue to contribute to the evolving art and science of nursing informatics. New trends promise to bring wide-sweeping and (it is hoped) positive changes to the practice of nursing. Computers and other technologies have the potential to sup- port a more client-oriented healthcare system in which clients truly become active participants in their own healthcare planning and decisions. Mobile health technol- ogy, telenursing, sophisticated electronic health records, and next-generation technol- ogy are predicted to contribute to high-quality nursing care and consultation within healthcare settings, including patients’ homes and communities.

Computers are becoming more powerful, yet more compact, which will contribute to the development of several technologic initiatives that are currently still in their infancy, such as quantum computing. Some of these initiatives are described here. These predicted innovations are only some of the many computer and technologic applica- tions being developed. As nurses gain proficiency in capitalizing on the creative, time- saving, and interactive capabilities emerging from information technology research, the field of nursing informatics will grow in similar proportions.

Quantum Computing Quantum bits (qubits) are three-dimensional arrays of atoms in quantum states. A quan- tum computer is a proposed machine that is not based on the binary system, but instead performs calculations based on the behavior of subatomic particles or qubits. It is estimated that if quantum computing, the act of using a quantum computer, is ever

realized, we will be able to execute millions of instructions per second (MIPS) due to the qubits existing in more than one state at a time or having the ability to simultaneously execute and process. According to Kennedy (2016), “the era of quantum computers is one step closer” (para. 1) due to the creation of qubits by David Weiss’s research team.

Voice-Activated Communicators Voice-activated communicators are already on the market, with new iterations being developed by a variety of companies, including Vocera Communications. Vocera (2015) developed the Vocera B3000n Communication Badge, which

is a lightweight, voice-controlled, wearable device that enables instant two-way or one to many conversations using intuitive and simple commands. The Vocera Badge is widely used by mobile workers who need wearable devices that provide the convenience and expedience of being able to respond to calls without pressing a button (i.e. sterile operating rooms, nuclear power plants, hotel staff, security personnel). (para. 1)

These new technologies will permit nurses to use wireless, hands-free devices to communicate with one another and to record data. This technology is becoming a user-friendly and cost-effective way to increase clinical productivity.

Game and Simulation Technology Game and simulation technology is offering realistic, innovative ways to teach content in general, including healthcare informatics concepts and skills. The same technology that powers video games is being used to create dynamic educational interfaces to help students learn about pathophysiology, care guidelines, and a host of other topics. Such applications are also very valuable for client education and health promotion materi- als. The “serious games” industry is growing now that video game producers are look- ing beyond mere entertainment to address public and private policy, management, and leadership issues and topics, including those related to health care. For example, the Games for Health Project, initiated by the Robert Wood Johnson Foundation (2015), is working on developing best practices to support innovation in healthcare training, messaging, and illness management. The Serious Games & VE Arcade & Showcase is presented at the annual meetings of the Society for Simulation in Healthcare and is continuing to flourish with numerous products available to demonstrate.

Virtual Reality Virtual reality is another technological breakthrough that is and will continue to influence healthcare education and professional development. Virtual reality is a three-dimensional, computer-generated “world” where a person (with the right equip- ment) can move about and interact as if he or she was actually in the visualized location. The person’s senses are immersed in this virtual reality world using special gadgetry, such as head-mounted displays, data gloves, joysticks, and other hand tools. The equipment and special technology provide a sense of presence that is lacking in multimedia and other complex programs. According to Smith (2015), “It’s crazy but true: Virtual reality will be a real thing in people’s homes by this time next year”

60 CHAPTER 3 Computer Science and the Foundation of Knowledge Model

(para. 1). There are numerous products available. Virtual Realities (2015) stated that they provide “head mounted displays, head trackers, motion trackers, data gloves, 3D controllers, haptic devices, stereoscopic 3D displays, VR domes and virtual reality software. Virtual Realities’ products are used by government, educational, industrial, medical and entertainment markets worldwide” (para. 1). Oculus VR (2015) devel- oped Rift, which is the next generation of virtual reality products, and they are cur- rently distributing the developer kits. HTC (2015) manufactures consumer electronics and developed the Vive headset. The Morpheus headset is used with PlayStation 4.

Mobile Devices Mobile devices will be used more by nurses both at the point of care and in planning, documenting, interacting with the interprofessional healthcare team, and research. Nurses also will be using such powerful wearable technologies as nano-based diagnos- tic sensors in their personal lives, and will be generating their own data streams and receiving data from the wearable and mobile devices their patients use. Silbershatz et al. (2013) stated that Apple iOS and Google Android are “currently dominating mobile computing” (p. 37). Perry (2015) stated that it is “estimated more than 177 million wearable devices will be in use by 2018” (para. 5). Cisco (2014) reported that “by the year 2020, the majority of Generation X and Y professionals believe that smartphones and wearable devices will be the workforce’s most important ‘connected’ device—while the laptop remains the workplace device of choice” (para. 1). Data are truly at our fingertips.

Summary The field of computer science is one of the fastest-growing disciplines. Astonishing innovations in computer hardware, software, and architecture have occurred over the past few decades, and there are no indications that this trend will come to a halt anytime soon. Computers have increased in speed, accuracy, and efficiency, yet now cost less and have reduced physical size compared to their forebears. These trends are predicted to continue. Current computer hardware and software serve as vital and valuable tools for both nurses and patients to engage in on-screen and online activi- ties that provide rich access to data and information. Productivity, creativity, and communication software tools also enable nurses to work with computers to further foster knowledge acquisition and development. Wide access to vast stores of informa- tion and knowledge shared by others facilitates the emergence of wisdom in users, which can then be applied to nursing in meaningful and creative ways. It is imperative that nurses become discerning, yet skillful users of computer technology to apply the principles of nursing informatics to practice to improve patient care and to contribute to the profession’s ever-growing body of knowledge.

Working Wisdom Since the beginning of the profession, nurses have applied their ingenuity, resourceful- ness, and professional awareness of what works to adapt technology and objects to support nursing care, usually with the intention of promoting efficiency but also in

Working Wisdom 61

62 CHAPTER 3 Computer Science and the Foundation of Knowledge Model

support of client comfort and healing. This resourcefulness could also be applied ef- fectively to the adaptation of information technology within the care environment, to ensure that the technology truly does serve clients and nurses and the rest of the interprofessional team.

Consider this question: “How can you develop competency in using the various computer hardware and software not only to promote efficient, high-quality nursing care and to develop yourself professionally, but also to further the development of the profession’s body of knowledge?”

Application Scenario Dan P. is a first-year student in graduate studies in nursing. In the past, he has learned to use his family’s personal computer to surf the World Wide Web, exchange email with friends, and play some computer games. Now, however, Dan realizes that the computer is a vital tool for his academic success. He has saved up enough money to purchase a laptop computer. He has decided on an Intel processor with 1 TB of stor- age and 8 GB of RAM. Dan also wishes to choose appropriate software for his system. He is on a limited budget but wants to make the most of his investment.

1. Dan still wants to learn more about computers. You recommend that he review the following information: Domingo (2016), Knapp (2016), and PCMag Digital Group (2016).

2. Which of the four categories of software discussed in this chapter would bene- fit Dan the most in his studies (OS, productivity, creativity, or communication)? Dan definitely needs an OS—this is critical. He would also directly benefit from productivity software and at least connective email and web browser software from the communication group so he can access the Internet for research, to collaborate with peers, and to communicate with his teachers.

3. How could Dan afford to install software from all four groups on his new lap- top? If Dan accessed some open source software (e.g., Apache OpenOffice for his productivity software), he could save money to put toward creativity software.

References Alba, D. (2015). Why on earth is IBM still making mainframes? Wired. Retrieved from http://www

.wired.com/2015/01/z13-mainframe Anderson, M. (2016). Intel says chips to become slower but more energy efficient. Retrieved from



1. How can knowledge of computer hardware and software help nurses to partici- pate in information technology adoption decisions in the practice area?

2. How can new computer software help nurses engage in professional develop- ment, collaboration, and knowledge dissemination activities at their own pace and leisure?

References 63

Bagaza, L., & Westover, B. (2016). The 10 best computer mice of 2016. PC Magazine. Retrieved from http://www.pcmag.com/article2/0,2817,2374831,00.asp

Bandura, A. (2002). Growing primacy of human agency in adaptation and change in the electronic era. European Psychologist, 7(1), 2–16.

Cisco. (2014). Working from Mars with an Internet brain implant: Cisco study shows how technology will shape the “Future of Work.” Retrieved from http://newsroom.cisco.com /press-release-content?type=webcontent&articleId=1528226

Domingo, J. (2016). The 10 best desktop PCs of 2016. PC Magazine. Retrieved from http:// www.pcmag.com/article2/0,2817,2372609,00.asp

Evans, D. (2010). Introduction to computing: Explorations in language, logic, and machines. University of Virginia. Retrieved from http://www.computingbook.org

Futuremark. (2016). Best processors May – 2016. Retrieved from http://www.futuremark.com /hardware/cpu

Griffith, E. (2016). What is cloud computing? PC Magazine. Retrieved from http://www.pcmag .com/article2/0,2817,2372163,00.asp

HTC. (2015). HTC’s VR vision. Finally, the future. Retrieved from http://www.htcvr.com IBM. (2016). What is cloud computing? Retrieved from https://www.ibm.com/cloud-computing

/what-is-cloud-computing Intel Corporation. (2016). Intel Xeon processor E5 family: Product specifications. Retrieved from

http://www.intel.com/content/www/us/en/processors/xeon/xeon-processor-e5-family.html Kennedy, B. (2016). New, better way to build circuits for the world’s first useful quantum

computers. Phys.org. Retrieved from http://phys.org/news/2016-06-circuits-world-quantum .html#jCp

Knapp, M. (2016). 9 key things to know before you buy a computer. Gear & Style Cheat Sheet. Retrieved from http://www.cheatsheet.com/technology/9-tips-for-picking-your-machine -computer-shopping-cheat-sheet.html/?a=viewall

Oculus VR. (2015). Step into the Rift. Retrieved from https://www.oculus.com/en-us/rift PCMag Digital Group. (2016). Laptops and notebooks. PC Magazine. Retrieved from http://www

.pcmag.com/reviews/laptop-computers Perry, L. (2015). Evolving millennial connections using wearables. Cisco. Retrieved from

http://blogs.cisco.com/tag/wearable-technology Robert Wood Johnson Foundation. (2015). Games for health. Retrieved from http://gamesforhealth

.org/about Sarkar, N. (2006). Tools for teaching computer networking and hardware concepts. Hershey,

PA: Idea Group. Sexton, M. (2016). StarTech unveils USB type-C to HDMI adapter. Retrieved from http://www

.tomshardware.com/news/startech-usb-typec-hdmi-adapter,31067.html Silbershatz, A., Baer Galvin, P., & Gagne, G. (2013). Operating system concepts (9th ed.).

Hoboken, NJ: John Wiley & Sons. Smith, D. (2015). 3 virtual reality products will dominate our living rooms by this time next

year. Business Insider. Retrieved from http://www.businessinsider.com/virtual-reality-is -getting-real-2015-5

Virtual Realities. (2015). Worldwide distributor of virtual reality. Retrieved from https://www .vrealities.com

Vocera. (2015). Vocera badge. Retrieved from http://www.vocera.com/product/vocera-badge Woodford, C. (2016). Cloud computing. Retrieved from http://www.explainthatstuff.com


Key Terms » Artificial

intelligence » Brain » Cognitive

informatics » Cognitive science » Computer science

» Connectionism » Decision making » Empiricism » Epistemology » Human Mental

Workload (MWL) » Intelligence

» Intuition » Knowledge » Logic » Memory » Mind » Neuroscience » Perception

» Problem solving » Psychology » Rationalism » Reasoning » Wisdom

1. Describe cognitive science. 2. Assess how the human mind processes and gener-

ates information and knowledge.

3. Explore cognitive informatics. 4. Examine artificial intelligence and its relationship

to cognitive science and computer science.


Introduction Cognitive science is the fourth of four basic building blocks used to under- stand informatics (Figure 4-1). The Building Blocks of Nursing Informatics section began by examining nursing science, information science, and computer science, and considering how each relates to and helps one under- stand the concept of informatics. This chapter explores the building blocks of cognitive science, cognitive informatics (CI), and artificial intelligence (AI).

Throughout the centuries, cognitive science has intrigued philosophers and educators alike. Beginning in Greece, the ancient philosophers sought to comprehend how the mind works and what the nature of knowledge is. This age-old quest to unravel the processes inherent in the working brain has been undertaken by some of the greatest minds in history. However, it was only about 50 years ago that computer operations and actions were linked to cognitive science, meaning theories of the mind, intellect, or brain. This association led to the expansion of cognitive science to examine the complete array of cognitive processes, from lower-level perceptions to higher-level critical thinking, logical analysis, and reasoning.

The focus of this chapter is the impact of cognitive science on nursing informatics (NI). This section provides the reader with an introduction and overview of cognitive science, the nature of knowledge, wisdom, and AI as they apply to the Foundation of Knowledge model and NI. The applica- tions to NI include problem solving, decision support systems, usability issues, user-centered interfaces and systems, and the development and use of terminologies.

Cognitive Science The interdisciplinary field of cognitive science studies the mind, intelligence, and behavior from an information-processing perspective. H.  Christopher Longuet-Higgins originated the term “cognitive science” in his 1973 commentary on the Lighthill report, which pertained to the state of

Introduction to Cognitive Science and Cognitive Informatics Kathleen Mastrian and Dee McGonigle



AI research at that time. The Cognitive Science Society and the Cognitive Science Journal date back to 1980 (Cognitive Science Society, 2005). Their interdisci- plinary base arises from psychology, philosophy, neuroscience, computer science, linguistics, biology, and physics; covers memory, attention, perception, reasoning, language, mental ability, and computational models of cognitive processes; and explores the nature of the mind, knowledge representation, language, problem solving, decision making, and the social factors influencing the design and use of technology. Simply put, cognitive science is the study of the mind and how infor- mation is processed in the mind. As described in the Stanford Encyclopedia of Philosophy (2010):

The central hypothesis of cognitive science is that thinking can best be understood in terms of representational structures in the mind and compu- tational procedures that operate on those structures. While there is much disagreement about the nature of the representations and computations that constitute thinking, the central hypothesis is general enough to en- compass the current range of thinking in cognitive science, including con- nectionist theories which model thinking using artificial neural networks. (para. 9)

Connectionism is a component of cognitive science that uses computer modeling through artificial neural networks to explain human intellectual abilities. Neural

66 CHAPTER 4 Introduction to Cognitive Science and Cognitive Informatics

Figure 4-1 Building Blocks of Nursing Informatics

Nursing Informatics

Nursing Science

Computer Science

Cognitive Science

Information Science

networks can be thought of as interconnected simple processing devices or simpli- fied models of the brain and nervous system that consist of a considerable number of elements or units (analogs of neurons) linked together in a pattern of connections (analogs of synapses). A neural network that models the entire nervous system would have three types of units: (1) input units (analogs of sensory neurons), which receive information to be processed; (2) hidden units (analogs to all of the other neurons, not sensory or motor), which work in between input and output units; and (3) output units (analogs of motor neurons), where the outcomes or results of the processing are found.

Connectionism (Figure 4-2) is rooted in how computation occurs in the brain and nervous system or biologic neural networks. On their own, single neurons have mini- mal computational capacity. When interconnected with other neurons, however, they have immense computational power. The connectionism system or model learns by modifying the connections linking the neurons. Artificial neural networks are unique computer programs designed to model or simulate their biologic analogs, the neurons of the brain.

The mind is frequently compared to a computer, and experts in computer science strive to understand how the mind processes data and information. In contrast, experts in cognitive science model human thinking using artificial networks provided by computers—an endeavor sometimes referred to as AI. How does the mind process all of the inputs received? Which items and in which ways are things stored or placed into memory, accessed, augmented, changed, reconfigured, and restored? Cognitive science provides the scaffolding for the analysis and modeling of compli- cated, multifaceted human performance and has a tremendous effect on the issues impacting informatics.

Cognitive Science 67

Figure 4-2 Connectionism


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The end user is the focus of this activity because the concern is with enhancing the performance in the workplace; in nursing, the end user could be the actual clinician in the clinical setting, and cognitive science can enhance the integration and implementation of the technologies being designed to facilitate this knowledge worker with the ultimate goal of improving patient care delivery. Technologies change rapidly, and this evolution must be harnessed for the clinician at the bedside. To do this at all levels of nursing practice, one must understand the nature of knowledge, the information and knowledge needed, and the means by which the nurse processes this information and knowledge in the situational context.

Sources of Knowledge Just as philosophers have questioned the nature of knowledge, so they have also strived to determine how knowledge arises, because the origins of knowledge can help one understand its nature. How do people come to know what they know about themselves, others, and their world? There are many viewpoints on this issue, both scientific and nonscientific.

According to Holt (2006), “There are two competing traditions concerning the ultimate source of our knowledge: empiricism and rationalism” (para. 3). Empiricism is based on knowledge being derived from experiences or senses, whereas rationalism contends that “some of our knowledge is derived from reason alone and that reason plays an important role in the acquisition of all of our knowledge” (para. 5). Empiri- cists do not recognize innate knowledge, whereas rationalists believe that reason is more essential in the acquisition of knowledge than the senses.

Three sources of knowledge have been identified: (1) instinct, (2) reason, and (3) intuition. Instinct is when one reacts without reason, such as when a car is head- ing toward a pedestrian and he jumps out of the way without thinking. Instinct is found in both humans and animals, whereas reason and intuition are found only in humans. Reason “[c]ollects facts, generalizes, reasons out from cause to effect, from effect to cause, from premises to conclusions, from propositions to proofs” (Sivananda, 2004, para. 4). Intuition is a way of acquiring knowledge that cannot be obtained by inference, deduction, observation, reason, analysis, or experience. Intu- ition was described by Aristotle as “[a] leap of understanding, a grasping of a larger concept unreachable by other intellectual means, yet fundamentally an intellectual process” (Shallcross & Sisk, 1999, para. 4).

Some believe that knowledge is acquired through perception and logic. Perception is the process of acquiring knowledge about the environment or situation by obtain- ing, interpreting, selecting, and organizing sensory information from seeing, hearing, touching, tasting, and smelling. Logic is “[a] science that deals with the principles and criteria of validity of inference and demonstration: the science of the formal principles of reasoning” (Merriam-Webster Online Dictionary, 2007, para. 1). Acquiring knowl- edge through logic requires reasoned action to make valid inferences.

The sources of knowledge provide a variety of inputs, throughputs, and outputs through which knowledge is processed. No matter how one believes knowledge is acquired, it is important to be able to explain or describe those beliefs, communicate those thoughts, enhance shared understanding, and discover the nature of knowledge.

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Nature of Knowledge Epistemology is the study of the nature and origin of knowledge—that is, what it means to know. Everyone has a conception of what it means to know based on their own perceptions, education, and experiences; knowledge is a part of life that contin- ues to grow with the person. Thus a definition of knowledge is somewhat difficult to agree on because it reflects the viewpoints, beliefs, and understandings of the person or group defining it. Some people believe that knowledge is part of a sequential learn- ing process resembling a pyramid, with data on the bottom, rising to information, then knowledge, and finally wisdom. Others believe that knowledge emerges from interactions and experience with the environment, and still others think that it is religiously or culturally bound. Knowledge acquisition is thought to be an internal process derived through thinking and cognition or an external process from senses, observations, studies, and interactions. Descartes’s important premise “called ‘the way of ideas’ represents the attempt in epistemology to provide a foundation for our knowledge of the external world (as well as our knowledge of the past and of other minds) in the mental experiences of the individual” (Encyclopedia Britannica, 2007, para. 4).

For the purpose of this text, knowledge is defined as the awareness and under- standing of a set of information and ways that information can be made useful to support a specific task or arrive at a decision. It abounds with others’ thoughts and information or consists of information that is synthesized so that relationships are identified and formalized.

How Knowledge and Wisdom Are Used in Decision Making The reason for collecting and building data, information, and knowledge is to be able to make informed, judicious, prudent, and intelligent decisions. When one considers the nature of knowledge and its applications, one must also examine the concept of wisdom. Wisdom has been defined in numerous ways:

• Knowledge applied in a practical way or translated into actions • The use of knowledge and experience to heighten common sense and insight to

exercise sound judgment in practical matters • The highest form of common sense resulting from accumulated knowledge or

erudition (deep, thorough learning) or enlightenment (education that results in understanding and the dissemination of knowledge)

• The ability to apply valuable and viable knowledge, experience, understanding, and insight while being prudent and sensible

• Focused on our own minds • The synthesis of our experience, insight, understanding, and knowledge • The appropriate use of knowledge to solve human problems

In essence, wisdom entails knowing when and how to apply knowledge. The decision-making process revolves around knowledge and wisdom. It is through

How Knowledge and Wisdom Are Used in Decision Making 69

efforts to understand the nature of knowledge and its evolution to wisdom that one can conceive of, build, and implement informatics tools that enhance and mimic the mind’s processes to facilitate decision making and job performance.

Cognitive Informatics Wang (2003) described CI as an emerging transdisciplinary field of study that bridges the gap in understanding regarding how information is processed in the mind and in the computer. Computing and informatics theories can be applied to help elucidate the information processing of the brain, and cognitive and neurologic sciences can likewise be applied to build better and more efficient computer processing systems. Wang suggested that the common issue among the human knowledge sciences is the drive to develop an understanding of natural intelligence and human problem solving.

Pacific Northwest National Laboratory (PNNL), an organization operated on behalf of the U.S. Department of Energy, suggested the disciplines of neuroscience, linguistics, AI, and psychology constitute this field. PNNL (2008) defined CI as “the multidisciplinary study of cognition and information sciences, which investi- gates human information processing mechanisms and processes and their engineering applications in computing” (para. 1). CI helps to bridge this gap by systematically exploring the mechanisms of the brain and mind and exploring specifically how information is acquired, represented, remembered, retrieved, generated, and commu- nicated. This dawning of understanding can then be applied and modeled in AI situa- tions resulting in more efficient computing applications.

Wang (2003) explained further:

Cognitive informatics attempts to solve problems in two connected areas in a bidirectional and multidisciplinary approach. In one direction, CI uses informatics and computing techniques to investigate cognitive science prob- lems, such as memory, learning, and reasoning; in the other direction, CI uses cognitive theories to investigate the problems in informatics, computing, and software engineering. (p. 120)

Principles of cognitive informatics and an understanding of how humans interact with computers can be used to build information technology (IT) systems that bet- ter meet the needs of users (Figure 4-3). If a system is too complex or too taxing for a user, he or she is likely to resist its use. The National Center for Cognitive Informat- ics and Decision Making in Healthcare (NCCD) was established to respond to “the urgent and long-term cognitive challenges in health IT adoption and meaningful use. NCCD’s vision is to become a national resource that provides strategic leadership in patient-centered cognitive support research and applications in health care” ( HealthIT.gov, 2013, para. 1). Similarly, Longo (2015) emphasized Human Mental Workload (MWL) as a key component in effective system design (Figure 4-4). He stated,

At a low level of MWL, people may often experience annoyance and frustra- tion when processing information. On the other hand, a high level can also be both problematic and even dangerous, as it leads to confusion, decreases performance in information processing and increases the chances of errors and mistakes. (p. 758)

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Cognitive Informatics and Nursing Practice According to Mastrian (2008), the recognition of the potential application of prin- ciples of cognitive science to NI is relatively new. The traditional and widely accepted definition of NI advanced by Graves and Corcoran (1989) is that NI is a combination of nursing science, computer science, and information science used to describe the processes nurses use to manage data, information, and knowledge in nursing prac- tice. Turley (1996) proposed the addition of cognitive science to this mix, as nurse

Cognitive Informatics and Nursing Practice 71

Figure 4-3 Cognitive Informatics Leads to Usable Systems

Cognitive Informatics

• Study of brain and human infomation processing

Informs System Design

• Models cognitive processes • Consider human mental workload

Usable Systems

• Easy and intuitive • Support nursing practice and decision making

Figure 4-4 Human Mental Workload


Working environment

Task demands

Task Performance

Individual perception Individual perception

scientists are seen to strive to capture and explain the influence of the human brain on data, information, and knowledge processing and to elucidate how these factors in turn affect nursing decision making. The need to include cognitive sciences is impera- tive as researchers attempt to model and support nursing decision making in complex computer programs.

In 2003, Wang proposed the term cognitive informatics to signify the branch of information and computer sciences that investigates and explains information pro- cessing in the human brain. The science of CI grew out of interest in AI, as computer scientists developed computer programs that mimic the information processing and knowledge generation functions of the human brain. CI bridges the gap between artificial and natural intelligence and enhances the understanding of how information is acquired, processed, stored, and retrieved so that these functions can be modeled in computer software.

What does this have to do with nursing? At its very core, nursing practice requires problem solving and decision making. Nurses help people manage their responses to illnesses and identify ways that patients can maintain or restore their health. During the nursing process, nurses must first recognize that there is a problem to be solved, identify the nature of the problem, pull information from knowledge stores that is rel- evant to the problem, decide on a plan of action, implement the plan, and evaluate the effectiveness of the interventions. When a nurse has practiced the science of nursing for some time, he or she tends to do these processes automatically; it is instinctively known what needs to be done to intervene in the problem. What happens, however, if the nurse faces a situation or problem for which he or she has no experience on which to draw? The ever-increasing acuity and complexity of patient situations coupled with the explosion of information in health care has fueled the development of deci- sion support software embedded in the electronic health record. This software models the human and natural decision-making processes of professionals in an artificial program. Such systems can help decision makers to consider the consequences of different courses of action before implementing the action. They also provide stores of information that the user may not be aware of and can use to choose the best course of action and ultimately make a better decision in unfamiliar circumstances.

Decision support programs continue to evolve as research in the fields of cognitive science, AI, and CI is continuously generated and then applied to the development of these systems. Nurses must embrace—not resist—these advances as support and enhancement of the practice of nursing science.

What Is AI? The field of AI deals with the conception, development, and implementation of infor- matics tools based on intelligent technologies. This field captures the complex pro- cesses of human thought and intelligence.

Herbert Simon believes that the field of AI could have two functions: “One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. . . . The other is to use a computer’s artificial intel- ligence to understand how humans think. In a humanoid way” (Stewart, 1994, para. 13). According to the AAAI (2014), AI is the “scientific understanding of the

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mechanisms underlying thought and intelligent behavior and their embodiment in machines” (para. 1).

John McCarthy, one of the men credited with founding the field of AI in the 1950s, stated that AI “is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable” (2007, p. 2).

Lamont (2007) interviewed Ray Kurzweil, a visionary who defined AI as “the abil- ity to perform a task that is normally performed by natural intelligence, particularly human natural intelligence. We have in fact artificial intelligence that can perform many tasks that used to require—and could only be done by—human intelligence” (para. 6). The intelligence factor is extremely important in AI and has been defined by McCarthy as “the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals, and some machines” (2007, p. 2).

The challenge of this field rests in capturing, mimicking, and creating the complex processes of the mind in informatics tools, including software, hardware, and other machine technologies, with the goal that the tool be able to initiate and generate its own mechanical thought processing. The brain’s processing is highly intricate and complicated. This complexity is reflected in Cohn’s (2006) comment that “Artificial intelligence is 50 years old this summer, and while computers can beat the world’s best chess players, we still can’t get them to think like a 4-year-old” (para. 1). AI uses cognitive science and computer science to replicate and generate human intelligence. This field will continue to evolve and produce artificially intelligent tools to enhance nurses’ personal and professional lives.

AI in the Future As electronic health records become more ubiquitous and we have access to physiologic data streamed in real time, we will have the potential to process large amounts of data using AI tools and we will begin to see data analytics that will enable machine process- ing that far exceeds the capabilities of the human mind. According to Neill (2013),

Perhaps the next great challenge for AI in healthcare is to develop approaches that can be applied to the entire population of patients monitoring huge quantities of data to automatically detect threats to patient safety (including patterns of suboptimal care, as well as outbreaks of hospital acquired illness), and to discover new best practices of patient care. (p. 93)

Summary Cognitive science is the interdisciplinary field that studies the mind, intelligence, and behavior from an information-processing perspective. CI is a field of study that bridges the gap in understanding regarding how information is processed in the mind and in the computer. Computing and informatics theories can be applied to help elucidate the information processing of the brain, and cognitive and neurologic sciences can likewise be applied to build better and more efficient computer processing systems.

Summary 73

AI is the field that deals with the conception, development, and implementation of informatics tools based on intelligent technologies. This field captures the complex processes of human thought and intelligence. AI uses cognitive science and computer science to replicate and generate human intelligence.

The sources of knowledge, nature of knowledge, and rapidly changing technolo- gies must be harnessed by clinicians to enhance their bedside care. Therefore, we must understand the nature of knowledge, the information and knowledge needed, and the means by which nurses process this information and knowledge in their own situational context. The reason for collecting and building data, information, and knowledge is to be able to build wisdom—that is, the ability to apply valuable and viable knowledge, experience, understanding, and insight while being prudent and sensible. Wisdom is focused on our own minds, the synthesis of our experience, insight, understanding, and knowledge. Nurses must use their wisdom and make informed, judicious, prudent, and intelligent decisions while providing care to patients, families, and communities. Cognitive science, CI, and AI will continue to evolve to help build knowledge and wisdom.

References Association for the Advancement of Artificial Intelligence (AAAI). (2014). Homepage. Retrieved

from http://www.aaai.org Cognitive Science Society. (2005). CSJ archive. Retrieved from http://www.cogsci.rpi.edu

/CSJarchive/1980v04/index.html Cohn, D. (2006). AI reaches the golden years. Wired. Retrieved from http://archive.wired.com

/science/discoveries/news/2006/07/71389 Encyclopedia Britannica. (2007). Epistemology. Retrieved from http://www.britannica.com/eb

/article-247960/epistemology Graves, J., & Corcoran, S. (1989). The study of nursing informatics. Image: Journal of Nursing

Scholarship, 21(4), 227–230. HealthIT.gov. (2013). National center for cognitive informatics and decision making in healthcare.

Retrieved from https://www.healthit.gov/policy-researchers-implementers/national-center- cognitive-informatics-and-decision-making-healthcare

Holt, T. (2006). Sources of knowledge. Retrieved from http://www.theoryofknowledge.info /sourcesofknowledge.html

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1. How would you describe CI? Reflect on a plan of care that you have developed for a patient. How could CI be used to create tools to help with or support this important work?

2. Think of a clinical setting with which you are familiar and envision how AI tools might be applied in this setting. Are there any current tools in use? Which current or emerging tools would enhance practice in this setting and why?

3. Use your creative mind to think of a tool of the future based on cognitive infor- matics that would support your practice.

Lamont, I. (2007). The grill: Ray Kurzweil talks about “augmented reality” and the singularity. Computer World. Retrieved from http://www.computerworld.com/action/article.do?comma nd=viewArticleBasic&articleId=306176

Longo, L. (2015). A defeasible reasoning framework for human mental workload representation and assessment. Behaviour & Information Technology, 34(8), 758–786. doi:10.1080/0144929X.2015.1015166

Longuet-Higgins, H. C. (1973). Comments on the Lighthill report and the Sutherland reply. Artificial Intelligence: A Paper Symposium, 35–37.

Mastrian, K. (2008, February). Invited editorial: Cognitive informatics and nursing practice. Online Journal of Nursing Informatics, 12(1). Retrieved from http://ojni.org/12_1/kathy.html

McCarthy, J. (2007). What is artificial intelligence? Retrieved from http://www.formal.stanford .edu/jmc/whatisai.pdf

Merriam-Webster Online Dictionary. (2007). Logic. Retrieved from http://www.merriam-webster .com/dictionary/logic

Neill, D. (2013). Using artificial intelligence to improve hospital inpatient care. IEEE Intelligent Systems, 92–95.

Pacific Northwest National Laboratory, U.S. Department of Energy. (2008). Cognitive informatics. Retrieved from http://www.pnl.gov/coginformatics

Shallcross, D. J., & Sisk, D. A. (1999). What is intuition? In T. Arnold (Ed.), Hyponoesis glossary: Intuition. Retrieved from http://www.hyponoesis.org/Glossary/Definition/Intuition

Sivananda, S. (2004). Four sources of knowledge. The Divine Life Society. Retrieved from http://www.dlshq.org/messages/knowledge.htm

Stanford Encyclopedia of Philosophy. (2010). Cognitive science. Retrieved from http://plato .stanford.edu/entries/cognitive-science

Stewart, D. (1994). The creator of the first thinking machine on the future of artificial intelligence: Herbert Simon on the mind in the machine. OMNI Q&A. Retrieved from http://www.omnimagazine.com/archives/interviews/simon/index.html

Turley, J. (1996). Toward a model for nursing informatics. Image: Journal of Nursing Scholarship, 28(4), 309–313.

Wang, Y. (2003). Cognitive informatics: A new transdisciplinary research field. Brain and Mind, 4(2), 115–127.

References 75

Key Terms » Alternatives » Antiprinciplism » Applications (Apps) » Autonomy » Beneficence » Bioethics » Bioinformatics » Care ethics » Casuist approach » Confidentiality » Consequences » Courage » Decision making

» Decision support » Duty » Ethical decision

making » Ethical dilemma » Ethical, social, and

legal implications » Ethicists » Ethics » Eudaemonistic » Fidelity » Good » Google Glass

» Harm » Justice » Liberty » Moral dilemmas » Moral rights » Morals » Negligence » Nicomachean » Nonmaleficence » Principlism » Privacy » Rights » Security

» Self-control » Smartphones » Social media » Standards » Truth » Uncertainty » Values » Veracity » Virtue » Virtue ethics » Wisdom

1. Recognize ethical dilemmas in nursing informatics.

2. Examine ethical implications of nursing informatics.

3. Evaluate professional responsibilities for the ethical use of healthcare informatics technology.

4. Explore the ethical model for ethical decision making.

5. Analyze practical ways of applying the ethical model for ethical decision making to manage ethical dilemmas in nursing informatics.


Introduction Those who followed the actual events of Apollo 13, or who were enter- tained by the movie (Howard, 1995), watched the astronauts strive against all odds to bring their crippled spaceship back to Earth. The speed of their travel was incomprehensible to most viewers, and the task of bringing the spaceship back to Earth seemed nearly impossible. They were experienc- ing a crisis never imagined by the experts at NASA, and they made up their survival plan moment by moment. What brought them back to Earth safely? Surely, credit must be given to the technology and the spaceship’s ability to withstand the trauma it experienced. Most amazing, however, were the traditional nontechnological tools, skills, and supplies that were used in new and different ways to stabilize the spacecraft’s environment and keep the astronauts safe while traveling toward their uncertain future.

This sense of constancy in the midst of change serves to stabilize experi- ence in many different life events and contributes to the survival of crisis and change. This rhythmic process is also vital to the healthcare system’s stability and survival in the presence of the rapidly changing events of the Knowledge Age. No one can dispute the fact that the Knowledge Age is changing health care in ways that will not be fully recognized and under- stood for years. The change is paradigmatic, and every expert who ad- dresses this change reminds healthcare professionals of the need to go with the flow of rapid change or be left behind.

As with any paradigm shift, a new way of viewing the world brings with it some of the enduring values of the previous worldview. As health care continues its journey into digital communications, telehealth, and wearable technologies, it brings some familiar tools and skills recognized in the form of values, such as privacy, confidentiality, autonomy, and nonma- leficence. Although these basic values remain unchanged, the standards for living out these values will take on new meaning as health professionals confront new and different moral dilemmas brought on by the adoption

Ethical Applications of Informatics Dee McGonigle, Kathleen Mastrian, and Nedra Farcus



of technological tools for information management, knowledge development, and evidence-based changes in patient care. Ethical decision-making frameworks will re- main constant, but the context for examining these moral issues or ethical dilemmas will become increasingly complex.

This chapter highlights some familiar ethical concepts to consider on the challenging journey into the increasingly complex future of healthcare informatics. Ethics and bio- ethics are briefly defined, and the evolution of ethical approaches from the Hippocratic ethic era, to principlism, to the current antiprinciplism movement of ethical decision making is examined. New and challenging ethical dilemmas are surfacing in the venture into the unfolding era of healthcare informatics (Figure 5-1). Also presented in this chap- ter are findings from some of the more recent literature related to these issues. Readers are challenged to think constantly and carefully about ethics as they become involved in healthcare informatics and to stay abreast of new developments in ethical approaches.

Ethics Ethics is a process of systematically examining varying viewpoints related to moral questions of right and wrong. Ethicists have defined the term in a variety of ways, with each reflecting a basic theoretical philosophic perspective.

Beauchamp and Childress (1994) referred to ethics as a generic term for various ways of understanding and examining the moral life. Ethical approaches to this examination may be normative, presenting standards of right or good action; descrip- tive, reporting what people believe and how they act; or explorative, analyzing the concepts and methods of ethics.

78 CHAPTER 5 Ethical Applications of Informatics

Figure 5-1 Ethics in Health Care


Husted and Husted (1995) emphasized a practice-based ethics, stating “ethics examines the ways men and women can exercise their power in order to bring about human benefit—the ways in which one can act in order to bring about the conditions of happiness” (p. 3).

Velasquez, Andre, Shanks, and Myer (1987) posed the question, “What is eth- ics?”, and answered it with the following two-part response: “First, ethics refers to well-based standards of right and wrong that prescribe what humans ought to do, usually in terms of rights, obligations, benefits to society, fairness, or specific virtues” (para. 10), and “Secondly, ethics refers to the study and development of one’s ethical standards” (para. 11).

Regardless of the theoretical definition, common characteristics regarding ethics are its dialectical, goal-oriented approach to answering questions that have the poten- tial for multiple acceptable answers.

Bioethics Bioethics is defined as the study and formulation of healthcare ethics. Bioethics takes on relevant ethical problems experienced by healthcare providers in the provision of care to individuals and groups. Husted and Husted (1995) state the fundamental background of bioethics that forms its essential nature is:

1. The nature and needs of humans as living, thinking beings 2. The purpose and function of the healthcare system in a human society 3. An increased cultural awareness of human beings’ essential moral status (p. 7)

Bioethics emerged in the 1970s as health care began to change its focus from a mechanistic approach of treating disease to a more holistic approach of treating people with illnesses. As technology advanced, recognition and acknowledgment of the rights and the needs of individuals and groups receiving this high-tech care also increased.

In today’s technologically savvy healthcare environment, patients are being pre- scribed applications (apps) for their smartphones instead of medications in some clinical practices. Patients’ smartphones are being used to interact with them in new ways and to monitor and assess their health in some cases. With apps and add-ons, for example, a provider can see the patient’s ECG immediately, or the patient can moni- tor his or her ECG and send it to the provider as necessary. Another example would be a sensor attached to the patient’s mobile device that could monitor blood glucose levels. We are just beginning to realize the vast potential of these mobile devices—and the threats they sometimes pose. Google Glass, for example, can take photos and vid- eos (Stern, 2013) without anyone knowing that this is occurring; in the healthcare environment, such a technological advancement can violate patients’ privacy and con- fidentiality. Wearable technologies provide a data-rich environment for diagnosing, addressing, and monitoring health issues. As we analyze huge patient datasets, con- cerns arise about privacy, confidentiality, and data sharing (Johns Hopkins, Berman Institute of Bioethics, n.d.). Add these evolving developments to healthcare providers’ engagement in social media use with their patients, and it becomes clear that personal and ethical dilemmas abound for nurses in the new über-connected world.

Bioethics 79

Ethical Issues and Social Media As connectivity has improved owing to emerging technologies, a rapid explosion in the phenomenon known as social media has occurred. Social media is defined as “a group of Internet-based applications that build on the ideological and technologi- cal foundations of Web 2.0 and that allow the creation and exchange of user- generated content” (Spector & Kappel, 2012, p. 1). Just as the electronic health record serves as a real-time event in recording patient–provider contact, so the use of social media represents an instantaneous form of communication. Healthcare providers— particularly nurses—can enhance the patient care delivery system, promote profes- sional collegiality, and provide timely communication and education regarding health-related matters by using this forum (National Council of State Boards of Nurs- ing [NCSBN], 2011, p. 1). In all cases, however, nurses must exercise judicious use of social media to protect patients’ rights. Nurses must understand their obligation to their chosen profession, particularly as it relates to personal behavior and the percep- tions of their image as portrayed through social media. Above all, nurses must be mindful that once communication is written and posted on the Internet, there is no way to retract what was written; it is a permanent record that can be tracked, even if the post is deleted (Englund, Chappy, Jambunathan, & Gohdes, 2012, p. 242).

Social media platforms include such electronic communication outlets as Facebook, Twitter, LinkedIn, Snapchat, and YouTube. Other widely used means of instantaneous communications include wikis, blogs, tweeting, Skype, and the “hangout” feature on Google+. Even as recently as 5 years ago, some of these means of exchanging informa- tion were unknown (Spector & Kappel, 2012, p. 1).

Use of social networking has increased dramatically among all age groups. Zephoria (2016) reported that, in 2016, Facebook had over 1.65 billion active monthly users worldwide as compared to 955 million active monthly users in 2012, and users spend an average of 20 minutes on Facebook per visit. Twitter’s influence on health care continues to grow, with Symplur (2016) reporting 1,603,327,260 tweets, including healthcare-related Tweet chats, conferences, and diseases such as breast cancer, diabetes, and irritable bowel syndrome.

The rapid growth of social media has found many healthcare professionals un- prepared to face the new challenges or to exploit the opportunities that exist with these forums. The need to maintain confidentiality presents a major obstacle to the healthcare industry’s widespread adoption of such technology; thus social networking has not yet been fully embraced by many health professionals (Anderson, 2012, p. 22). Englund and colleagues (2012) noted that undergraduate nursing students may face ambiguous and understudied professional and ethical implications when using social networking venues.

Another confounding factor is the increased use of mobile devices by health pro- fessionals as well as the public (Swartz, 2011, p. 345). Smartphones have the capa- bility to take still pictures as well as live recordings; they have found their way into treatment rooms around the globe.

As a consequence of more stringent confidentiality laws and more widespread availability and use of social and mobile media, numerous ethical and legal dilem- mas have been posed to nurses. What are not well defined are the expectations of

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healthcare providers regarding this technology. In some cases, nurses employed in the emergency department (ED) setting have been subjected to video and audio record- ings by patients and families when they perform procedures and give care during the ED visit. Nurses would be wise to inquire—before an incident occurs—about the hos- pital policy regarding audio/video recording by patients and families, as well as the state laws governing two-party consent. Such laws require consent of all parties to any recording or eavesdropping activity (Lyons & Reinisch, 2013, p. 54).

Sometimes the enthusiasm for patient care and learning can lead to ethics viola- tions. In one case, an inadvertent violation of privacy laws occurred when a nurse in a small town blogged about a child in her care whom she referred to as her “little handi- capper.” The post also noted the child’s age and the fact that the child used a wheel- chair. A complaint about this breach of confidentiality was reported to the Board of Nursing. A warning was issued to the nurse blogging this information, although a more stringent disciplinary action could have been taken (Spector & Kappel, 2012, p. 2).

In another case cited by Spector and Kappel (2012), a student nurse cared for a 3-year-old leukemia patient whom she wanted to remember after finishing her pediatric clinical experience. She took the child’s picture, and in the background of the photo the patient’s room number was clearly displayed. The child’s picture was posted on the student nurse’s Facebook page, along with her statement of how much she cared about this child and how proud she was to be a student nurse. Someone forwarded the picture to the nurse supervisor of the children’s hospital. Not only was the student expelled from the program, but the clinical site offer made by the chil- dren’s hospital to the nursing school was rescinded. In addition, the hospital faced citations for violations of the Health Insurance Portability and Accountability Act (HIPAA) owing to the student nurse’s transgression (p. 3).

Nurses sometimes use social network sites or blog about the patients they care for believing that if they omit the patient’s name, they are not violating the patient’s pri- vacy and confidentiality. “A nurse who posts about caring for an 85-year-old female in her city could cause the patient to be identified by content in the post. This action does not protect the patient” (Henderson & Dahnke, 2015, p. 63). A white paper published by the NCSBN (2011) provides a thorough discussion of the issues associ- ated with nurses’ use of social media.

Ethical Dilemmas and Morals An ethical dilemma arises when moral issues raise questions that cannot be answered with a simple, clearly defined rule, fact, or authoritative view. Morals refer to social convention about right and wrong human conduct that is so widely shared that it forms a stable (although usually incomplete) communal consensus (Beauchamp & Childress, 1994). Moral dilemmas arise with uncertainty, as is the case when some evidence a person is confronted with indicates an action is morally right and other evidence indicates that this action is morally wrong. Uncertainty is stressful and, in the face of inconclusive evidence on both sides of the dilemma, causes the person to ques- tion what he or she should do. Sometimes the individual concludes that based on his or her moral beliefs, he or she cannot act. Uncertainty also arises from unanticipated effects or unforeseeable behavioral responses to actions or the lack of action. Adding

Ethical Dilemmas and Morals 81

uncertainty to the situational factors and personal beliefs that must be considered cre- ates a need for an ethical decision-making model to help one choose the best action.

Ethical Decision Making Ethical decision making refers to the process of making informed choices about ethical dilemmas based on a set of standards differentiating right from wrong. This type of decision making reflects an understanding of the principles and standards of ethical decision making, as well as the philosophic approaches to ethical decision making, and it requires a systematic framework for addressing the complex and often contro- versial moral questions.

As the high-speed era of digital communications evolves, the rights and the needs of individuals and groups will be of the utmost concern to all healthcare profession- als. The changing meaning of communication, for example, will bring with it new concerns among healthcare professionals about protecting patients’ rights of confi- dentiality, privacy, and autonomy. Systematic and flexible ethical decision-making abilities will be essential for all healthcare professionals.

Notably, the concept of nonmaleficence (“do no harm”) will be broadened to include those individuals and groups whom one may never see in person, but with whom one will enter into a professional relationship of trust and care. Mack (2000)

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Using an online survey of 1,227 randomly selected respondents, Bodkin and Miaoulis (2007) sought to describe the characteristics of information seekers on e-health websites, the types of information they seek, and their perceptions of the quality and ethics of the websites. Of the respondents, 74% had sought health in- formation on the Web, with women accounting for 55.8% of the health informa- tion seekers. A total of 50% of the seekers were between 35 and 54 years of age. Nearly two thirds of the users began their searches using a general search engine rather than a health-specific site, unless they were seeking information related to symptoms or diseases. Top reasons for seeking information were related to dis- eases or symptoms of medical conditions, medication information, health news, health insurance, locating a doctor, and Medicare or Medicaid information. The level of education of information seekers was related to the ratings of website quality, in that more educated seekers found health information websites more understandable, but were more likely to perceive bias in the website information. The researchers also found that the ethical codes for e-health websites seem to be increasing consumers’ trust in the safety and quality of information found on the Web, but that most consumers are not comfortable purchasing health products or services online.

The full article appears in Bodkin, C., & Miaoulis, G. (2007). eHealth information quality and ethics issues: An exploratory study of consumer perceptions. International Journal of Pharmaceuti- cal and Healthcare Marketing, 1(1), 27–42. Retrieved from ABI/INFORM Global (Document ID: 1515583081).

has discussed the popularity of individuals seeking information online instead of directly from their healthcare providers and the effects this behavior has on patient–provider relationships. He is emphatic in his reminder that “organizations and individuals that provide health information on the Internet have obligations to be trustworthy, provide high-quality content, protect users’ privacy, and adhere to standards of best practices for online commerce and online professional services in healthcare” (p. 41).

Makus (2001) suggests that both autonomy and justice are enhanced with universal access to information, but that tensions may be created in patient–provider relationships as a result of this access to outside information. Healthcare workers need to realize that they are no longer the sole providers and gatekeepers of health-related information; ide- ally, they should embrace information empowerment and suggest websites to patients that contain reliable, accurate, and relevant information (Resnick, 2001).

It is clear that patients’ increasing use of the Internet for healthcare information may prompt entirely new types of ethical issues, such as who is responsible if a patient is harmed as a result of following online health advice. Derse and Miller (2008) discuss this issue extensively and conclude that a clear line separates informa- tion and practice. Practice occurs when there is direct or personal communication between the provider and the patient, when the advice is tailored to the patient’s specific health issue, and when there is a reasonable expectation that the patient will act in reliance on the information.

A summit sponsored by the Internet Healthcare Coalition (www.ihealthcoalition. org) in 2000 developed the E-Health Code of Ethics (eHealth code, n.d.), which includes eight standards for the ethical development of health-related Internet sites: (1) candor, (2) honesty, (3) quality, (4) informed consent, (5) privacy, (6) professional- ism, (7) responsible partnering, and (8) accountability. For more information about each of these standards, access the full discussion of the E-Health Code of Ethics (http://www.ihealthcoalition.org/ehealth-code-of-ethics).

It is important to realize that the standards for ethical development of health- related Internet sites are voluntary; there is no overseer perusing these sites and issuing safety alerts for users. Although some sites carry a specific symbol indicat- ing that they have been reviewed and are trustworthy (HONcode and Trust-e), the healthcare provider cannot control which information patients access or how they perceive and act related to the health information they find online. The research brief on the previous page describes one study of consumer perceptions of health information on the Web.

Theoretical Approaches to Healthcare Ethics Theoretical approaches to healthcare ethics have evolved in response to societal changes. In a 30-year retrospective article for the Journal of the American Medical Association, Pellegrino (1993) traced the evolution of healthcare ethics from the Hippocratic ethic, to principlism, to the current antiprinciplism movement.

The Hippocratic tradition emerged from relatively homogenous societies where beliefs were similar and most societal members shared common values. The emphasis was on duty, virtue, and gentlemanly conduct.

Theoretical Approaches to Healthcare Ethics 83

Principlism arose as societies became more heterogeneous and members began experiencing a diversity of incompatible beliefs and values; it emerged as a founda- tion for ethical decision making. Principles were expansive enough to be shared by all rational individuals, regardless of their background and individual beliefs. This approach continued into the 1900s and was popularized by two bioethicists, Beauchamp and Childress (1977; 1994), in the last quarter of the 20th century. Principles are considered broad guidelines that provide guidance or direction but leave substantial room for case-specific judgment. From principles, one can develop more detailed rules and policies.

Beauchamp and Childress (1994) proposed four guiding principles: (1) respect for autonomy, (2) nonmaleficence, (3) beneficence, and (4) justice.

• Autonomy refers to the individual’s freedom from controlling interferences by others and from personal limitations that prevent meaningful choices, such as adequate understanding. Two conditions are essential for autonomy: liberty, meaning the independence from controlling influences, and the individual’s capacity for intentional action.

• Nonmaleficence asserts an obligation not to inflict harm intentionally and forms the framework for the standard of due care to be met by any professional. Obligations of nonmaleficence are obligations of not inflicting harm and not imposing risks of harm. Negligence—a departure from the standard of due care toward others—includes intentionally imposing risks that are unreasonable and unintentionally but carelessly imposing risks.

• Beneficence refers to actions performed that contribute to the welfare of others. Two principles underlie beneficence: Positive beneficence requires the provision of benefits, and utility requires that benefits and drawbacks be balanced. One must avoid negative beneficence, which occurs when constraints are placed on activities that, even though they might not be unjust, could in some situations cause detriment or harm to others.

• Justice refers to fair, equitable, and appropriate treatment in light of what is due or owed to a person. Distributive justice refers to fair, equitable, and appropri- ate distribution in society determined by justified norms that structure the terms of social cooperation.

Beauchamp and Childress also suggest three types of rules for guiding actions: substantive, authority, and procedural. (Rules are more restrictive in scope than principles and are more specific in content.) Substantive rules are rules of truth tell- ing, confidentiality, privacy, and fidelity, and those pertaining to the allocation and rationing of health care, omitting treatment, physician-assisted suicide, and informed consent. Authority rules indicate who may and should perform actions. Procedural rules establish procedures to be followed.

The principlism advocated by Beauchamp and Childress has since given way to the antiprinciplism movement, which emerged in the 21st century with the expansive technological changes and the tremendous rise in ethical dilemmas accompanying these changes. Opponents of principlism include those who claim that its principles do not represent a theoretical approach as well as those who claim that its principles are too far removed from the concrete particularities of everyday human existence;

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are too conceptual, intangible, or abstract; or disregard or do not take into account a person’s psychological factors, personality, life history, sexual orientation, or religious, ethnic, and cultural background. Different approaches to making ethical decisions are next briefly explored, providing the reader with an understanding of the varied meth- ods professionals may use to arrive at an ethical decision.

The casuist approach to ethical decision making grew out of the call for more concrete methods of examining ethical dilemmas. Casuistry is a case-based ethical reasoning method that analyzes the facts of a case in a sound, logical, and ordered or structured manner. The facts are compared to decisions arising out of consensus in previous paradigmatic or model cases. One casuist proponent, Jonsen (1991), prefers particular and concrete paradigms and analogies over the universal and abstract theo- ries of principlism.

The Husted bioethical decision-making model centers on the healthcare profes- sional’s implicit agreement with the patient or client (Husted & Husted, 1995). It is based on six contemporary bioethical standards: (1) autonomy, (2) freedom, (3) veracity, (4) privacy, (5) beneficence, and (6) fidelity.

The virtue ethics approach emphasizes the virtuous character of individuals who make the choices. A virtue is any characteristic or disposition desired in others or oneself. It is derived from the Greek word aretai, meaning “excellence,” and refers to what one expects of oneself and others. Virtue ethicists emphasize the ideal situ- ation and attempt to identify and define ideals. Virtue ethics dates back to Plato and Socrates. When asked “whether virtue can be taught or whether virtue can be acquired in some other way, Socrates answers that if virtue is knowledge, then it can be taught. Thus, Socrates assumes that whatever can be known can be taught” (Scott, 2002, para. 9). According to this view, the cause of any moral weakness is not a mat- ter of character flaws but rather a matter of ignorance. In other words, a person acts immorally because the individual does not know what is really good for him or her. A person can, for example, be overpowered by immediate pleasures and forget to con- sider the long-term consequences. Plato emphasized that to lead a moral life and not succumb to immediate pleasures and gratification, one must have a moral vision. He identified four cardinal virtues: (1) wisdom, (2) courage, (3) self-control, and (4) justice.

Aristotle’s (350 BC) Nicomachean principles also contribute to virtue ethics. According to this philosopher, virtues are connected to will and motive because the intention is what determines if one is or is not acting virtuously. Ethical consider- ations, according to his eudaemonistic principles, address the question, “What is it to be an excellent person?” For Aristotle, this ultimately means acting in a temperate manner according to a rational mean between extreme possibilities.

Virtue ethics has experienced a recent resurgence in popularity (Ascension Health, 2007). Two of the most influential moral and medical authors, Pellegrino and Thomasma (1993), have maintained that virtue theory should be related to other theories within a comprehensive philosophy of the health professions. They argue that moral events are composed of four elements (the agent, the act, the cir- cumstances, and the consequences), and state that a variety of theories must be inter- related to account for different facets of moral judgment.

Care ethics is responsiveness to the needs of others that dictates providing care, preventing harm, and maintaining relationships. This viewpoint has been in existence

Theoretical Approaches to Healthcare Ethics 85

for some time. Engster (2004) stated that “Carol Gilligan’s In a Different Voice (1982) established care ethics as a major new perspective in contemporary moral and political discourse” (p. 113). The relationship between care and virtue is complex, however. Benjamin and Curtis (1992) base their framework on care ethics; they pro- pose that “critical reflection and inquiry in ethics involves the complex interplay of a variety of human faculties, ranging from empathy and moral imagination on the one hand to analytic precision and careful reasoning on the other” (p. 12). Care ethicists are less stringently guided by rules, but rather focus on the needs of others and the individual’s responsibility to meet those needs. As opposed to the aforementioned theories that are centered on the individual’s rights, an ethic of care emphasizes the personal part of an interdependent relationship that affects how decisions are made. In this theory, the specific situation and context in which the person is embedded become a part of the decision-making process.

The consensus-based approach to bioethics was proposed by Martin (1999), who claims that American bioethics harbors a variety of ethical methods that emphasize different ethical factors, including principles, circumstances, character, interpersonal needs, and personal meaning. Each method reflects an important aspect of ethical ex- perience, adds to the others, and enriches the ethical imagination. Thus working with these methods provides the challenge and the opportunity necessary for the perceptive and shrewd bioethicist to transform them into something new with value through the process of building ethical consensus. Diverse ethical insights can be integrated to support a particular bioethical decision, and that decision can be understood as a new, ethical whole.

Applying Ethics to Informatics With the Knowledge Age has come global closeness, meaning the ability to reach around the globe instantaneously through technology. Language barriers are being broken through technologically based translators that can enhance interaction and exchange of data and information. Informatics practitioners are bridging continents, and international panels, committees, and organizations are beginning to establish standards and rules for the implementation of informatics. This international perspec- tive must be taken into consideration when informatics dilemmas are examined from an ethical standpoint; it promises to influence the development of ethical approaches that begin to accept that healthcare practitioners are working within international networks and must recognize, respect, and regard the diverse political, social, and human factors within informatics ethics.

The various ethical approaches can be used to help healthcare professionals make ethical decisions in all areas of practice. The focus of this text is on informatics. Infor- matics theory and practice have continued to grow at a rapid rate and are infiltrating every area of professional life. New applications and ways of performing skills are be- ing developed daily. Therefore, education in informatics ethics is extremely important.

Typically, situations are analyzed using past experience and in collaboration with others. Each situation warrants its own deliberation and unique approach, because each individual patient seeking or receiving care has his or her own preferences,

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quality of life, and healthcare needs in a situational milieu framed by financial, pro- vider, setting, institutional, and social context issues. Clinicians must take into consid- eration all of these factors when making ethical decisions.

The use of expert systems, decision support tools, evidence-based practice, and artificial intelligence in the care of patients creates challenges in terms of who should use these tools, how they are implemented, and how they are tempered with clini- cal judgment. All clinical situations are not the same, and even though the result of interacting with these systems and tools is enhanced information and knowledge, the clinician must weigh this information in light of each patient’s unique clinical circum- stances, including that individual’s beliefs and wishes. Patients are demanding access to quality care and the information necessary to control their lives. Clinicians need to analyze and synthesize the parameters of each distinctive situation using a specific decision-making framework that helps them make the best decisions. Getting it right the first time has a tremendous impact on expected patient outcomes. The focus should remain on patient outcomes while the informatics tools available are ethically incorporated.

Facing ethical dilemmas on a daily basis and struggling with unique client situa- tions may cause many clinicians to question their own actions and the actions of their colleagues and patients. One must realize that colleagues and patients may reach very different decisions, but that does not mean anyone is wrong. Instead, all parties reach their ethical decision based on their own review of the situational facts and understanding of ethics. As one deals with diversity among patients, colleagues, and administrators, one must constantly strive to use ethical imagination to reach ethi- cally competent decisions.

Balancing the needs of society, his or her employer, and patients could cause the clinician to face ethical challenges on an everyday basis. Society expects judicious use of finite healthcare resources. Employers have their own policies, standards, and practices that can sometimes inhibit the practice of the clinician. Each patient is unique and has life experiences that affect his or her healthcare perspective, choices, motivation, and adherence. Combine all of these factors with the challenges posed by informatics, and it is clear that the evolving healthcare arena calls for an informatics- competent, politically active, consumer-oriented, business-savvy, ethical clinician to rule this ever-changing landscape known as health care.

The goal of any ethical system should be that a rational, justifiable decision is reached. Ethics is always there to help the practitioner decide what is right. Indeed, the measure of an adequate ethical system, theory, or approach is, in part, its ability to be useful in novel contexts. A comprehensive, robust theory of ethics should be up to the task of addressing a broad variety of new applications and challenges at the intersection of informatics and health care.

The information concerning an ethical dilemma must be viewed in the context of the dilemma to be useful. Bioinformatics could gather, manipulate, classify, analyze, synthesize, retrieve, and maintain databases related to ethical cases, the effective rea- soning applied to various ethical dilemmas, and the resulting ethical decisions. This input would certainly be potent—but the resolution of dilemmas cannot be achieved simply by examining relevant cases from a database. Instead, clinicians must assess

Applying Ethics to Informatics 87

each situational context and the patient’s specific situation and needs and make their ethical decisions based on all of the information they have at hand.

Ethics is exciting, and competent clinicians need to know about ethical dilemmas and solutions in their professions. Ethicists have often been thought of as experts in the arbitrary, ambiguous, and ungrounded judgments of other people. They know that they make the best decisions they can based on the situation and stakeholders at hand. Just as clinicians try to make the best healthcare decisions with and for their patients, ethically driven practitioners must do the same. Each healthcare provider must critically think through the situation to arrive at the best decision.

To make ethical decisions about informatics technologies and patients’ intimate healthcare data and information, the healthcare provider must be competent in informatics. To the extent that information technology is reshaping healthcare prac- tices or promises to improve patient care, healthcare professionals must be trained and competent in the use of these tools. This competency needs to be evaluated through instruments developed by professional groups or societies; such assessment will help with consistency and quality. For the healthcare professional to be an effective patient advocate, he or she must understand how information technology affects the patient and the subsequent delivery of care. Information science and its effects on health care are both interesting and important. It follows that information technol- ogy and its ethical, social, and legal implications should be incorporated into all levels of professional education.

The need for confidentiality was perhaps first articulated by Hippocrates; thus if anything is different in today’s environment, it is simply the ways in which confiden- tiality can be violated. Perhaps the use of computers for clinical decision support and data mining in research will raise new ethical issues. Ethical dilemmas associated with the integration of informatics must be examined to provide an ethical framework that considers all of the stakeholders. Patients’ rights must be protected in the face of a healthcare provider’s duty to his or her employer and society at large when initiat- ing care and assigning finite healthcare resources. An ethical framework is necessary to help guide healthcare providers in reference to the ethical treatment of electronic data and information during all stages of collection, storage, manipulation, and dissemination. These new approaches and means come with their own ethical dilemmas. Often they are dilemmas not yet faced owing to the cutting-edge nature of these technologies.

Just as processes and models are used to diagnose and treat patients in practice, so a model in the analysis and synthesis of ethical dilemmas or cases can also be applied. An ethical model for ethical decision making (Box 5-1) facilitates the ability to ana- lyze the dilemma and synthesize the information into a plan of action (McGonigle, 2000). The model presented here is based on the letters in the word ethical. Each let- ter guides and prompts the healthcare provider to think critically (think and rethink) through the situation presented. The model is a tool because, in the final analysis, it allows the nurse objectively to ascertain the essence of the dilemma and develop a plan of action.

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Applying Ethics to Informatics 89


• Examine the ethical dilemma (conflicting values exist). • Thoroughly comprehend the possible alternatives available. • Hypothesize ethical arguments. • Investigate, compare, and evaluate the arguments for each alternative. • Choose the alternative you would recommend. • Act on your chosen alternative. • Look at the ethical dilemma and examine the outcomes while reflecting on the

ethical decision.


Examine the ethical dilemma: • Use your problem-solving, decision-making, and critical-thinking skills. • What is the dilemma you are analyzing? Collect as much information

about the dilemma as you can, making sure to gather the relevant facts that clearly identify the dilemma. You should be able to describe the di- lemma you are analyzing in detail.

• Ascertain exactly what must be decided. • Who should be involved in the decision-making process for this specific

case? • Who are the interested players or stakeholders? • Reflect on the viewpoints of these key players and their value systems. • What do you think each of these stakeholders would like you to decide as

a plan of action for this dilemma? • How can you generate the greatest good?

Thoroughly comprehend the possible alternatives available: • Use your problem-solving, decision-making, and critical-thinking skills. • Create a list of the possible alternatives. Be creative when developing your

alternatives. Be open minded; there is more than one way to reach a goal. Compel yourself to discern at least three alternatives.

• Clarify the alternatives available and predict the associated consequences— good and bad—of each potential alternative or intervention.

• For each alternative, ask the following questions: - Do any of the principles or rules, such as legal, professional, or organi-

zational, automatically nullify this alternative? - If this alternative is chosen, what do you predict as the best-case and

worst-case scenarios? - Do the best-case outcomes outweigh the worst-case outcomes? - Could you live with the worst-case scenario? - Will anyone be harmed? If so, how will they be harmed? - Does the benefit obtained from this alternative overcome the risk of

potential harm that it could cause to anyone?

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Hypothesize ethical arguments: • Use your problem-solving, decision-making, and critical-thinking skills. • Determine which of the five approaches apply to this dilemma. • Identify the moral principles that can be brought into play to support a

conclusion as to what ought to be done ethically in this case or similar cases.

• Ascertain whether the approaches generate converging or diverging con- clusions about what ought to be done.

Investigate, compare, and evaluate the arguments for each alternative: • Use your problem-solving, decision-making, and critical-thinking skills. • Appraise the relevant facts and assumptions prudently.

- Is there ambiguous information that must be evaluated? - Are there any unjustifiable factual or illogical assumptions or debat-

able conceptual issues that must be explored? • Rate the ethical reasoning and arguments for each alternative in terms of

their relative significance. - 4 = extreme significance - 3 = major significance - 2 = significant - 1 = minor significance

• Compare and contrast the alternatives available with the values of the key players involved.

• Reflect on these alternatives: - Does each alternative consider all of the key players? - Does each alternative take into account and reflect an interest in the

concerns and welfare of all of the key players? - Which alternative will produce the greatest good or the least amount

of harm for the greatest number of people? • Refer to your professional codes of ethical conduct. Do they support your

reasoning? Choose the alternative you would recommend: • Use your problem-solving, decision-making, and critical-thinking skills. • Make a decision about the best alternative available.

- Remember the Golden Rule: Does your decision treat others as you would want to be treated?

- Does your decision take into account and reflect an interest in the con- cerns and welfare of all of the key players?

- Does your decision maximize the benefit and minimize the risk for everyone involved?

• Become your own critic; challenge your decision as you think others might. Use the ethical arguments you predict they would use and defend your decision. - Would you be secure enough in your ethical decision-making process to

see it aired on national television or sent out globally over the Internet?

- Are you secure enough with this ethical decision that you could have allowed your loved ones to observe your decision-making process, your decision, and its outcomes?

Act on your chosen alternative: • Use your problem-solving, decision-making, and critical-thinking skills. • Formulate an implementation plan delineating the execution of the decision.

- This plan should be designed to maximize the benefits and minimize the risks.

- This plan must take into account all of the resources necessary for implementation, including personnel and money.

• Implement the plan. Look at the ethical dilemma and examine the outcomes while reflecting on

your ethical decision: • Use your problem-solving, decision-making, and critical-thinking skills. • Monitor the implementation plan and its outcomes. It is extremely

important to reflect on specific case decisions and evaluate their outcomes to develop your ethical decision-making ability.

• If new information becomes available, the plan must be reevaluated. • Monitor and revise the plan as necessary.

The ethical model for ethical decision making was developed by Dr. Dee McGonigle and is the property of Educational Advancement Associates (EAA). The permission for its use in this text has been granted by Mr. Craig R. Goshow, Vice President, EAA.

Case Analysis Demonstration The following case study is intended to help readers think through how to apply the ethical model. Review the model and then read through the case. Try to apply the model to this case or follow along as the model is implemented. Readers are chal- lenged to determine their decision in this case and then compare and contrast their response with the decision the authors reached.

Allison is a charge nurse on a busy medical–surgical unit. She is expecting the clinical instructor from the local university at 2:00 pm to review and discuss potential patient assignments for the nursing students scheduled for the following day. Just as the university professor arrives, one of the patients on the unit develops a crisis requiring Allison’s attention. To expedite the student nurse assignments for the following day, Allison gives her electronic medical record access password to the instructor.

Examine the Ethical Dilemma Allison made a commitment to meet with the university instructor to develop student assignments at 2:00 pm. The patient emergency that developed prevented Allison from living up to that commitment. Allison had an obligation to provide patient care

Case Analysis Demonstration 91

during the emergency and a competing obligation to the professor. She solved the dilemma of competing obligations by providing her electronic medical record access password to the university professor.

By sharing her password, Allison most likely violated hospital policy related to the security of healthcare information. She may also have violated the American Nurses Association code of ethics, which states that nurses must judiciously protect informa- tion of a confidential nature. Because the university professor was also a nurse and had a legitimate interest in the protected healthcare information, there might not be a code of ethics violation.

Thoroughly Comprehend the Possible Alternatives Available The possible alternatives available include the following: (1) Allison could have asked the professor to wait until the patient crisis was resolved; (2) Allison could have del- egated another staff member to assist the university professor; or (3) Allison could have logged on to the system for the professor.

Hypothesize Ethical Arguments The utilitarian approach applies to this situation. An ethical action is one that pro- vides the greatest good for the greatest number; the underlying principles in this perspective are beneficence and nonmaleficence. The rights to be considered are as follows: right of the individual to choose for himself or herself (autonomy); right to truth (veracity); right of privacy (the ethical right to privacy avoids conflict and, like all rights, promotes harmony); right not to be injured; and right to what has been promised (fidelity).

Does the action respect the moral rights of everyone? The principles to consider are autonomy, veracity, and fidelity.

As for the fairness or justice, how fair is an action? Does it treat everyone in the same way, or does it show favoritism and discrimination? The principles to consider are justice and distributive justice.

Thinking about the common good assumes one’s own good is inextricably linked to good of the community; community members are bound by pursuit of common values and goals and ensure that the social policies, social systems, institutions, and environments on which one depends are beneficial to all. Examples of such outcomes are affordable health care, effective public safety, a just legal system, and an unpol- luted environment. The principle of distributive justice is considered.

Virtue assumes that one should strive toward certain ideals that provide for the full development of humanity. Virtues are attitudes or character traits that enable one to be and to act in ways that develop the highest potential; examples include honesty, courage, compassion, generosity, fidelity, integrity, fairness, self-control, and prudence. Like habits, virtues become a characteristic of the person. The virtuous person is the ethical person. Ask yourself, what kind of person should I be? What will promote the development of character within myself and my community? The principles consid- ered are fidelity, veracity, beneficence, nonmaleficence, justice, and distributive justice.

In this case, there is a clear violation of an institutional policy designed to pro- tect the privacy and confidentiality of medical records. However, the professor had a

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legitimate interest in the information and a legitimate right to the information. Allison trusted that the professor would not use the system password to obtain information outside the scope of the legitimate interest. However, Allison cannot be sure that the professor would not access inappropriate information. Further, Allison is responsible for how her access to the electronic system is used. Balancing the rights of everyone— the professor’s right to the information, the patients’ rights to expect that their infor- mation is safeguarded, and the right of the patient in crisis to expect the best possible care—is important and is the crux of the dilemma. Does the patient care obligation outweigh the obligation to the professor? Yes, probably. Allison did the right thing by caring for the patient in crisis. By giving out her system access password, Allison also compromised the rights of the other patients on the unit to expect that their confiden- tiality and privacy would be safeguarded.

Virtue ethics suggests that individuals use power to bring about human benefit. One must consider the needs of others and the responsibility to meet those needs. Allison must simultaneously provide care, prevent harm, and maintain profes- sional relationships.

Allison may want to effect a long-term change in hospital policy for the common good. It is reasonable to assume that this event was not an isolated incident and that the problem may recur in the future. Can the institutional policy be amended to pro- vide professors with access to the medical records system? As suggested in the HIPAA administrative guidelines, the professor could receive the same staff training regarding appropriate and inappropriate use of access and sign the agreement to safeguard the records. If the institution has tracking software, the professor’s access could be moni- tored to watch for inappropriate use.

Identify the moral principles that can be brought into play to support a conclusion as to what ought to be done ethically in this case or similar cases. The International Council of Nurses (2006) code of ethics states that “The nurse holds in confidence personal information and uses judgment in sharing this information” (p. 4). The code also states, “The nurse uses judgment in relation to individual competence when accepting and delegating responsibilities” (p. 5). Both of these statements apply to the current situation.

Ascertain whether the approaches generate converging or diverging conclusions about what ought to be done. From the analysis, it is clear that the best immediate solution is to delegate assisting the professor with assignments to another nurse on the unit.

Investigate, Compare, and Evaluate the Arguments for Each Alternative Review and think through the items listed in Table 5-1.

Choose the Alternative You Would Recommend The best immediate solution is to delegate another staff member to assist the profes- sor. The best long-term solution is to change the hospital policy to include access for professors, as described previously.

Case Analysis Demonstration 93

Act on Your Chosen Alternative Allison should delegate another staff member to assist the professor in making assignments.

Look at the Ethical Dilemma and Examine the Outcomes While Reflecting on the Ethical Decision As already indicated in the alternative analyses, delegation may not be an ideal solution because the staff nurse who is assigned to assist the professor may not possess the same extensive information about all of the patients as the charge nurse. It is, however, the best immediate solution to the dilemma and is certainly safer than compromising the integrity of the hospital’s computer system. As noted previously, Allison may want to pursue a long-term solution to a potentially recurring problem by helping the professor gain legitimate access to the computer system with the professor’s own password. The system administrator would then have the ability to track who used the system and which types of information were accessed during use.

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Table 5-1 Detailed Analysis of Alternative Actions

Alternative Good Consequences

Bad Consequences

Do Any Rules Nullify Expected Outcome

Potential Benefit > Harm

1. Wait until crisis was resolved

No policy violation

Patient rights safeguarded

Not the best use of the professor’s time

No Best: Crisis will require a short time

Worst: Crisis may take a long time

Patient rights protected

Collegial relationship jeopardized

Patient rights may take precedence

2. Delegate to another staff member

No policy violated

Other staff may be equally busy or might not be as familiar with all patients

No Best: Assignments will be completed

Worst: May not have benefit of expert advice

Confidentiality of record is assured

May compromise student learning

Patient rights may take precedence

3. Log on to the system for the professor

Professor can begin making assignments

May still be a violation of policy regarding system access

Rules regarding access to medical record

Best: Assignments can be completed

Worst: Abuse of access to information

Potential compromise of records

Patient in crisis is cared for

This case analysis demonstration provides the authors’ perspective on this case and the ethical decision made. If your decision did not match this perspective, what was the basis for the difference of opinion? If you worked through the model, you might have reached a different decision based on your individual background and perspective. This does not make the decision right or wrong. A decision should reflect the best decision one can make given review, reflection, and critical thinking about this specific situation.

Six additional cases are provided in the online learner’s manual for review. Apply the model to each case study, and discuss these cases with colleagues or classmates.

New Frontiers in Ethical Issues The expanding use of new information technologies in health care will bring about new and challenging ethical issues. Consider that patients and healthcare providers no longer have to be in the same place for a quality interaction. How, then, does one deal with licensing issues if the electronic consultation takes place across a state line? Derse and Miller (2008) describe a second-opinion medical consultation on the Internet where the information was provided to the referring physician and not to the patient, thus avoiding the licensing issue. In essence, provider-to-provider consulta- tion does not constitute practicing in a state in which you are not licensed. As new technologies for healthcare delivery are developed, new ethical challenges may arise. It is important for all healthcare providers to be aware of the code of ethics for their specific practices, and to understand the laws governing their practice and private health information.

Consider also the ethical issues created by genomic databases or by sharing of information in a health information exchange to promote population health. Alpert (2008) asks, “Is it wise to put genomic sequence data into electronic medical records that are poorly protected, that cannot adhere well to Fair Information Practice Prin- ciples for privacy, and that can potentially be seen by tens of thousands of people/ entities, when it is clear that we do not understand the functionality of the genome and likely will not for several years?” (p. 382).

Further, how does one really obtain informed consent for such data collection, when how the data will ultimately be used is not known, but clearly that applica- tion will be important to health research uses that go beyond the immediate medical care of the patient? Angst (2009) asks whether the public good outweighs individual interests in such a case because the information contained in these databases is im- portant to developing new understandings and creating new knowledge by matching data in aggregated pools: “Thus, science adds meaning and context to data, but to what extent do we agree to make the data available such that this discovery process can take place, and are the impacts of discovery great enough to justify the risks?” (p. 172). Further, if a voluntary system where patients can opt out of such data col- lection is adopted, then are healthcare disparities related to incomplete electronic health records created?

In an ideal world, healthcare professionals must not be affected by conflicting loyalties; nothing should interfere with judicious, ethical decision making. As the

New Frontiers in Ethical Issues 95

technologically charged waters of health care are navigated, one must hone a solid foundation of ethical decision making and practice it consistently.

Summary As science and technology advance, and policy makers and healthcare providers continue to shape healthcare practices including information management, it is paramount that ethical decisions are made. Healthcare professionals are typically honest, trustworthy, and ethical, and they understand that they are duty bound to focus on the needs and rights of their patients. At the same time, their day-to-day work is conducted in a world of changing healthcare landscapes populated by new technologies, diverse patients, varied healthcare settings, and changing policies set by their employers, insurance companies, and providers. The technologies them- selves are not the problem, but the misuse of the technology can cause harm to our patients. If we use them to the patient’s advantage while protecting the patient, they can be beneficial tools in accessing our technologically savvy patients to garner the data and information necessary to address their healthcare needs, including patient education, while impacting public health and enhancing our relationship with our patients. Healthcare professionals need to juggle all of these balls simultaneously, and so the ethical considerations must be at the forefront, a task that often results in far too many gray areas or ethical decision-making dilemmas with no clear cor- rect course of action. Patients rely on the ethical competence of their healthcare providers, believing that their situation is unique and will be respected and evalu- ated based on their own needs, abilities, and limitations. The healthcare profes- sional cannot allow conflicting loyalties to interfere with judicious, ethical decision making. Just as in the opening example of the Apollo mission, it is uncertain where this technologically heightened information era will lead, but if a solid foundation of ethical decision making is relied upon, duties and rights will be judiciously and ethically fulfilled.

96 CHAPTER 5 Ethical Applications of Informatics


1. Identify moral dilemmas in healthcare informatics that would best be approached with the use of an ethical decision-making framework, such as the use of smartphones to interact with patients as well as to monitor and assess patient health.

2. Discuss the evolving healthcare ethics traditions within their social and histori- cal contexts.

3. Differentiate among the theoretical approaches to healthcare ethics as they relate to the theorists’ perspectives of individuals and their relationships.

4. Select one of the healthcare ethics theories and support its use in examining ethical issues in healthcare informatics.

5. Select one of the healthcare ethics theories and argue against its use in examining ethical issues in healthcare informatics.

References Alpert, S. (2008). Privacy issues in clinical genomic medicine, or Marcus Welby, M.D., meets the

$1000 genome. Cambridge Quarterly of Healthcare Ethics, 17(4), 373–384. Retrieved from Health Module (Document ID: 1880623501).

Anderson, K. (2012). Social media a new way to care and communicate. Australian Nursing Journal, 20(3), 22–25.

Angst, C. (2009). Protect my privacy or support the common-good? Ethical questions about electronic health information exchanges. Journal of Business Ethics, 90(suppl), 169–178. Retrieved from ABI/INFORM Global (Document ID: 2051417481).

Aristotle. (350 BC). Nichomachean ethics. Book I. (W. D. Ross, Trans.). Retrieved from http://www .constitution.org/ari/ethic_00.htm

Ascension Health. (2007). Healthcare Ethics: Virtue ethics. Retrieved from http://www .ascensionhealth.org/ethics/public/issues/virtue.asp

Beauchamp, T. L., & Childress, J. F. (1977). Principles of biomedical ethics. New York, NY: Oxford University Press.

Beauchamp, T. L., & Childress, J. F. (1994). Principles of biomedical ethics (4th ed.). New York, NY: Oxford University Press.

Benjamin, M., & Curtis, J. (1992). Ethics in nursing (3rd ed.). New York, NY: Oxford University Press.

Derse, A., & Miller, T. (2008). Net effect: Professional and ethical challenges of medicine online. Cambridge Quarterly of Healthcare Ethics, 17(4), 453–464. Retrieved from Health Module (Document ID: 1540615461).

eHealth code. (n.d.). Retrieved from http://www.ihealthcoalition.org/ehealth-code Englund, H., Chappy, S., Jambunathan, J., & Gohdes, E. (2012, November/December). Ethical

reasoning and online social media. Nurse Educator, 37, 242–247. http://dx.doi.org/10.1097 /NNE.0b013e31826f2c04

Engster, D. (2004). Care ethics and natural law theory: Toward an institutional political theory of caring. The Journal of Politics, 66(1), 113–135.

Henderson, M., & Dahnke, M. (2015). The ethical use of social media in nursing practice. MEDSURG Nursing, 24(1), 62–64.

Howard, R. (Director). (1995). Apollo 13 [Motion picture]. Universal City, CA: MCA Universal Studios.

Husted, G. L., & Husted, J. H. (1995). Ethical decision-making in nursing (2nd ed.). New York, NY: Mosby.

International Council of Nurses (ICN). (2006). The ICN code of ethics for nurses. Retrieved from http://www.icn.ch/icncode.pdf

Johns Hopkins, Berman Institute of Bioethics. (n.d.). A blueprint for 21st century nursing ethics. Retrieved from http://www.bioethicsinstitute.org/nursing-ethics-summit-report/literature-review

Jonsen, A. R. (1991). Casuistry as methodology in clinical ethics. Theoretical Medicine, 12, 295–307.

Lyons, R., & Reinisch, C. (2013). The legal and ethical implications of social media in the emergency department. Advanced Emergency Nursing Journal, 35(1), 53–56. http://dx.doi. org/10.1097/TME.0b013e31827a4926

Mack, J. (2000). Patient empowerment, not economics, is driving e-health: Privacy and ethics issues need attention too! Frontiers of Health Services Management, 17(1), 39–43; discussion 49–51. Retrieved from ABI/INFORM Global (Document ID: 59722384).

Makus, R. (2001). Ethics and Internet healthcare: An ontological reflection. Cambridge Quarterly of Healthcare Ethics, 10(2), 127–136. Retrieved from Health Module (Document ID: 1409693941).

References 97

Martin, P. A. (1999). Bioethics and the whole: Pluralism, consensus, and the transmutation of bioethical methods into gold. Journal of Law, Medicine & Ethics, 27(4), 316–327.

McGonigle, D. (2000). The ethical model for ethical decision making. Inside Case Management, 7(8), 1–5.

National Council of State Boards of Nursing (NCSBN). (2011). White paper: A nurse’s guide to the use of social media. Retrieved from https://www.ncsbn.org/Social_Media.pdf

Pellegrino, E. D. (1993). The metamorphosis of medical ethics: A thirty-year retrospective. Journal of the American Medical Association, 269, 1158–1162.

Pellegrino, E., & Thomasma, D. (1993). The virtues in medical practice. New York, NY: Oxford University Press.

Resnik, D. (2001). Patient access to medical information in the computer age: Ethical concerns and issues. Cambridge Quarterly of Healthcare Ethics, 10(2), 147–154; discussion 154–156. Retrieved from Health Module (Document ID: 1409693961).

Scott, A. (2002). Plato’s Meno. Retrieved from http://www.angelfire.com/md2/timewarp/plato.html Spector, N., & Kappel, D. M. (2012). Guidelines for using electronic and social media: The

regulatory perspective. Online Journal of Nursing, 17(3). Retrieved from http://www.nursingworld .org/MainMenuCategories/ANAMarketplace/ANAPeriodicals/OJIN/TableofContents/Vol-17 -2012/No3-Sept-2012/Guidelines-for-Electronic-and-Social-Media.htm

Stern, J. (2013). Google Glass: What you can and can’t do with Google’s wearable computer. ABC News. Retrieved from http://abcnews.go.com/Technology/google-glass-googles -wearable-gadget/story?id=19091948

Swartz, M. K. (2011, November/December). The potential for social media. Journal of Pediatric Health Care, 25, 345.

Symplur. (2016). Why the healthcare Hashtag project? Retrieved from http://www.symplur.com /healthcare-hashtags

Velasquez, M., Andre, C., Shanks, T., & Myer, M. (for the Markkula Center for Applied Ethics). (1987). What is ethics? Retrieved from http://www.scu.edu/SCU/Centers/Ethics /practicing/decision/whatisethics.shtml

Zephoria. (2016). Top 20 valuable Facebook statistics. Retrieved from https://zephoria.com /top-15-valuable-facebook-statistics

98 CHAPTER 5 Ethical Applications of Informatics

section ii

Perspectives on Nursing Informatics Chapter 6 History and Evolution of Nursing Informatics

Chapter 7 Nursing Informatics as a Specialty

Chapter 8 Legislative Aspects of Nursing Informatics: HITECH and HIPAA

Nursing informatics (NI) is the synthesis of nursing science, information science, computer science, and cognitive science for the purpose of managing and enhanc- ing healthcare data, information, knowledge, and wisdom to improve patient care and the nursing profession. In the Building Blocks of Nursing Informatics section, the reader learned about the four sciences of NI, also referred to as the four building blocks, and the ethical application of these sciences to manage patient information. Nursing knowledge workers must be able to understand the evolving specialty of NI to harness and use the tools available for managing the vast amount of healthcare data and information. It is essential that NI capabilities be appreciated, promoted, expanded, and advanced to facilitate the work of the nurse, improve patient care, and enhance the nursing profession.

This section presents the perspectives of nursing experts on NI. The History and Evolution of Nursing Informatics chapter begins this exploration by providing the historical development and evolution of NI. This transitions into the Nursing Infor- matics as a Specialty chapter, where the reader learns about NI roles, competencies, and skills. The Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapter considers the evolving NI needs of nurses and nurse informaticists based on the current regulations impacting the healthcare arena.

In the History and Evolution of Nursing Informatics chapter, interrelationships among major NI concepts are discussed. As data are transformed into informa- tion and information into knowledge, increasing complexity and interrelationships ensue. The boundaries between concepts can become blurred, and feedback loops from one concept level to another evolve. Structured languages and human– computer interaction concepts, which are critical elements for NI, are noted in this chapter. Taxonomies and other current structured languages for nursing are listed. Human–computer interaction concepts are briefly defined and discussed because they are critical to the success of informatics solutions. Importantly, the construct of decision making is added to the traditional nursing metaparadigms: nurse, per- son, health, and environment. Decision making is not only at the crux of nursing practice in all settings and roles, but it is a fundamental concern of NI. The work of nursing is centered on the concepts of NI: data, information, knowledge, and wisdom. Information technology (IT) per se is not the focus; it is the information that the technology conveys that is central. Moreover, NI is no longer the domain of experts in the IT field. More interestingly, one does not need technology to perform informatics. The centerpiece of informatics is the manipulation of data, information, and knowledge, especially related to decision making in any aspect of nursing or in any setting. In a way, nurses are all already informatics nurses. Note that the core concepts and competencies of informatics are particularly well suited to a model of interprofessional education. Ideally, when educational programs are emulating clinical settings, informatics knowledge should be integrated with the processes of interprofessional teams and decision making. Because simulation laboratories are becoming increasingly common fixtures in the delivery of health- related professional education, they provide a perfect opportunity to incorporate

100 seCtIoN II Perspectives on Nursing Informatics

the electronic health records applications. The learning laboratory for nursing education will then more closely approximate the IT-enabled clinical settings that are emerging in the real world. A presumption is often made that future graduates will be more computer literate than nurses currently in practice. Although this may be true, computer literacy or comfort does not equate to an understanding of the facilitative and transformative role of information technology. It is essential that the future curricula of basic nursing programs embed the concepts of the role of IT in supporting clinical care delivery. The need for standardizing nursing terminology is also discussed in this chapter as a way to improve the clinical support functions of the electronic health record. The healthcare industry employs the largest number of knowledge workers—a fact that has resulted in the realization that healthcare administrators must begin to change the way they look at their employees. Nurses and physicians are bright, highly skilled, and dedicated to giving the best patient care. Administrators who tap into this wealth of knowledge find that patient care becomes safer and more efficient.

The Nursing Informatics as a Specialty chapter discusses NI as a relatively new nursing specialty that combines the building block sciences covered earlier in the text. Combining these sciences results in nurses being able to care for their patients effectively and safely because the information that they need is readily available. Nurses have been actively involved in NI since computers were introduced into health care. With the advent of electronic health records, it became apparent that nursing needed to develop its own language for this evolving field. NI was instru- mental in assisting in nursing language development. NI is governed by standards established by the American Nurses Association and is a very diverse field, which results in many nurse informaticist specialists becoming focused on one segment of NI. Although NI is a recognized specialty area of practice, in the future all nurses will be expected to have some knowledge of the field. NI competencies have been developed to ensure that all entry-level nurses are ready to enter a field that is be- coming more technologically advanced. The competencies may also be used to de- termine the educational needs of currently practicing nurses as well as Level 4 nurse informatics specialists. Nurse informatics specialists no longer have to enter the field solely through on-the-job exposure, but can now obtain an advanced degree in NI at many well-established universities throughout the United States. NI has grown tremendously as a specialty since its inception and is predicted to continue growing.

The Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapter provides insights into HIPAA rules and an overview of the rules associated with technology implementation as defined by the HITECH Act. Equally important in in- formatics practice is a thorough understanding of current legislation and regulations that shape 21st century practice. The information provided in this text reflects cur- rent rules that were in effect at the time of publication. The reader should follow the rules development and evolution of informatics legislation at the U.S. Department of Health and Human Services website (www.hhs.gov) to obtain the most current infor- mation related to health information management.

seCtIoN II Perspectives on Nursing Informatics 101

There is an emerging global focus on information technology to support clinical care and on the potential benefits for clinicians and patients. In the future, nurses will likely have sufficient computing power at their disposal to aggregate and transform additional multidimensional data and information sources (e.g., historical, multisen- sory, experiential, genetic) into a clinical information system to engage with individu- als, families, and groups in ways not yet imagined. Every nurse’s practice will make contributions to new nursing knowledge in these dynamically interactive clinical information system environments. With the right tools to support the management of data, complex information processing, and ready access to knowledge, the core con- cepts and competencies associated with informatics will be embedded in the practice of every nurse, whether administrator, researcher, educator, or practitioner. Informa- tion technology is not a panacea, but it provides the profession with unprecedented capacity to generate and disseminate new knowledge more rapidly.

The material within this text is placed within the context of the Foundation of Knowledge model (Figure II-1) to meet the needs of healthcare delivery systems, orga- nizations, patients, and nurses. Through involvement in NI and learning about this evolving specialty, one will be able to use the current theories, architecture, and tools, while beginning to challenge what is known. This questioning and search for what

Figure II-1 Foundation of Knowledge Model Designed by Alicia Mastrian.

KA - Knowledge acquisition KD - Knowledge dissemination KG - Knowledge generation KP - Knowledge processing



Information Information



Bytes Bytes



Bits Data



Bytes Bytes Bits

Bits Data Information






102 seCtIoN II Perspectives on Nursing Informatics

could be will provide the basis for the future landscape of nursing. By using the Foun- dation of Knowledge model as an organizing framework for this text, the authors have attempted to capture this process.

In this section, the reader learns about NI. Those readers who are beginning their education will consciously focus on input and knowledge acquisition, trying to glean as much information and knowledge as possible. As these readers become more com- fortable in their clinical setting and with nursing science, they will begin to take over some of the other knowledge functions. Experienced nurses, also known as “seasoned nurses,” question what is known and search for ways to enhance their knowledge and the knowledge of others. What is not available must be created. It is through these leaders, researchers, or clinicians that new knowledge is generated and disseminated and nursing science is advanced. Sometimes, however, to keep up with the explosion of information in nursing and health care, one must continue to rely on the knowl- edge generated and disseminated by others. In this sense, nurses are committed to lifelong learning and the use of knowledge in the practice of nursing science. How nurses interact within their environment and apply what is learned depends on their placement in the Foundation of Knowledge model.

Readers of this section are challenged to ask how they can (1) apply knowledge gained from the practice setting to benefit patients and enhance their practice, (2) help colleagues and patients understand and use current technology, and (3) use wisdom to help create the theories, tools, and knowledge of the future.

seCtIoN II Perspectives on Nursing Informatics 103

Key terms » Accessibility » Cognitive activity » Data » Data gatherer » Enumerative

approach » Expert systems

» Industrial Age » Information » Information Age » Information user » International

Classification of Nursing Practice

» Knowledge » Knowledge

builder » Knowledge user » Knowledge worker » Ontological


» Reusability » Standardized Nurs-

ing Terminology » Technologist » Terminology » Ubiquity » Wisdom

1. Trace the evolution of nursing informatics from concept to specialty practice.

2. Relate nursing informatics metastructures, con- cepts, and tools to the knowledge work of nursing.

3. Explore the quest for consistent terminology in nursing and describe terminology approaches that

accurately capture and codify the contributions of nursing to health care.

4. Explore the concept of nurses as knowledge workers.

5. Explore how nurses can create and derive clinical knowledge from information systems.


Introduction The information and knowledge informing the 21st century of healthcare delivery have been growing at an unprecedented pace in recent years. Clini- cal research in particular has propelled the understanding of the efficacy of various clinical practices, treatment regimens, and interventions. Extended and expanded access to clinical research findings and decision support tools has been significantly influenced by the advent of computerization and the Internet. Indeed, the conduct of research itself has been accelerated by virtue of ubiquitous computing. Working in environments of increas- ingly complex clinical care and contending with the management of large volumes of data and information, all nurses need to avail themselves of the technological tools that can support quality practice that is optimally safe, informed, and knowledge based. Although the increased deployment of in- formation technologies within healthcare settings presumes that nurses and other health professionals are proficient in the use of computing devices, the processes and potential outcomes associated with informatics are yet to be fully realized or understood. Nurses need to participate in the creation of those possibilities.

Health service organizations, societies, and governments throughout the industrialized world are committed to ensuring that healthcare delivery is safer, knowledge based, cost effective, seamless, and timely. Beyond these deliverables, there are expectations of improved efficiency and quality and of the active engagement of consumers in their care. In particular, given the evolving emphasis on such issues as chronic disease management and aging at home, informatics tools need to include the use of technologies to empower citizens to manage their own health and wellness more effectively.

This chapter explores the history and evolution of nursing informatics and defines and addresses the goal of informatics as it relates to nursing practice. The ways in which nursing informatics supports the creation of a culture of knowledge-based nursing practice that is enabled and advanced through the use of information and communication technologies are

History and evolution of Nursing Informatics Kathleen Mastrian and Dee McGonigle

With contributions by Ramona Nelson, Nancy Staggers, Lynn M. Nagle, and Nicholas Hardiker



described. The chapter also addresses some of the challenges associated with the at- tainment of this knowledge-based culture, as well as the opportunities for nurses to create and derive knowledge from emerging health information technologies. Finally, the chapter provides a contemplative view of the future for nurses and informatics.

the evolution of a specialty Nurses have historically gathered and recorded data, albeit in a paper record. For example, nurses gather atomic-level data (e.g., blood pressure, pulse, blood glucose, pallor), aggregate data to derive information (e.g., impending shock), and apply knowledge (e.g., lowering the head of the bed to minimize the potentially deleteri- ous effects of impending shock). Over the years, these data have been recorded into individuals’ hard-copy health records, thereby chronicling findings, actions, and out- comes; these data and information were then forever lost unless manually extracted for research purposes. As computers were introduced into health care, and data and information were recorded electronically, a nursing specialty was born.

Florence Nightingale has been credited as one of the first statisticians to col- lect and use data to change the way she cared for her patients. While serving in the Crimean War, she began to gather data regarding the conditions in which patients were living and the diseases they contracted and from which they died. These data were later used to improve patient conditions at both city and military hospitals (O’Connor & Robertson, 2003). There is no doubt that nursing experiences build knowledge and skill in nursing practice, but paper-based documentation has hindered the ability to share knowledge and to aggregate experiences to build new knowledge.

Nursing informatics pioneers recognized early on that computers had the potential to fundamentally change health care and they became actively involved in shaping how computers were used in health care. For more specific information on nursing informatics pioneers, and to view video recordings of the contributions of each in the nursing informatics history project, please visit this website: https://www.amia.org/ working-groups/nursing-informatics/history-project/video-library-1

According to Ozbolt and Saba (2008), one very early pioneer, Harriet Werley, a nurse researcher at Walter Reed Army Research Institute, consulted with IBM in the late 1950s to explore computer use in health care. Ms. Werley recognized the need for a minimum set of data to be collected from every patient, so that comparisons could be made, and thus set the stage for the development of informatics. As computers be- came more commonplace in the 1970s and 1980s, more nurses became involved with developing approaches to use computers in health care. It is important to note that this was also the time that nurse leaders were writing about the need for and develop- ing terminologies to represent patient data and nursing contributions to health care, were beginning to conduct informatics research, and were advocating for informatics education in nursing curricula (Ozbolt & Saba, 2008).

In 1989, Graves and Cocoran offered what is widely viewed as the seminal defini- tion of nursing informatics (NI). They defined NI as: “a combination of computer science, information science, and nursing science designed to assist in the manage- ment and processing of nursing data, information, and knowledge to support the practice of nursing and the delivery of nursing care” (p. 227). In this same article,

106 CHAPeR 6 History and Evolution of Nursing Informatics

acknowledging the 1986 work of Blum, Graves and Cocoran provided the definitions and descriptions of the concepts of data (discrete entities described objectively with- out interpretation), information (data that are interpreted, organized, or structured), and knowledge (information that is synthesized so that relationships are identified and formalized) as these terms apply to the science and practice of NI. They also described what is meant by management and processing. “The management compo- nent of informatics is the functional ability to collect, aggregate, organize, move, and re-present information in an economical, efficient way that is useful to the users of the system. . . . In practice, processing is considered as a transformation of data or infor- mation from one form to another form, usually at a more complex state of organiza- tion or meaning. There is a progression of transformation of data into information and of information into knowledge” (p. 227). We will return to a discussion of these concepts later in the chapter. For now, we continue our exploration of the evolution of informatics as a specialty.

In the 1990s, the American Medical Informatics Association was founded with a nursing informatics work group, the American Nurses Association (ANA) recognized nursing informatics as a specialty, ANA published two documents related to informat- ics practice, and the first informatics certification was established (Ozbolt & Saba, 2008). As nursing informatics pioneers and emerging leaders continued to champion the use of computers in health care, the need for computer-friendly terminologies to represent the work of nursing was increasingly apparent. Several different terminol- ogy schemes were developed during this time, and there were also international efforts at developing a standardized nursing terminology to capture and codify the contribu- tions of nursing to health care. At this same time, healthcare organizations were beginning to implement electronic information systems. There was little coordination of these various efforts and approaches. As Ozbolt and Saba (2008) explain, “Faced with the bewildering array of choices and the licensing fees required for the use of NANDA [North American Nursing Diagnosis Association (as it was known until 2002)], NIC [Nursing Interventions Classification], NOC [Nursing Outcomes Classi- fication], and SNOMED [Systematized Nomenclature of Medicine], many health care organizations adopting nursing information systems opted to use their own or ven- dor-provided, non-standard terms. This approach allowed entry of data via familiar terms, but because the terms were not consistent in definition or usage, investigators could not retrieve meaningful data to analyze for quality improvement or research” (p. 202). We will discuss this issue in more detail later in the chapter.

President Bush’s call for electronic health records in 2004 further stimulated the development of nursing informatics, informatics competency identification, and infor- matics education reform, and spawned several national and international informatics organizations. “While nursing informatics leaders work to transform nursing educa- tion and practice, nursing informatics scientists are creating the knowledge and tools that will enable the transformation. As research in nursing terminology and knowl- edge representation moves from creation to implementation and use, other domains of research reflect the maturation of nursing informatics as a science” (Ozbolt & Saba, 2008 p. 204). In this profound statement, we see the clear connection between nursing science and nursing informatics. That is, knowledge creation in nursing is de- pendent on knowledge representation in the information management tools that are

The Evolution of a Specialty 107

central to nursing informatics. As the NI pioneers recognized these important connec- tions and synergies, both nursing as a science and nursing informatics as a specialty evolved. Indeed, the evolution is not complete, as you will experience as you read the subsequent chapters in this text.

As the NI specialty was evolving, informatics pioneers and other nurse leaders col- laborated on several ANA publications. As mentioned previously, NI was identified by the ANA as a specialty in 1992. In 1994, the first formal document identifying the scope of practice was published, followed by a separate standards of practice docu- ment in 1995. In 2001, a combined scope and standards document was published by the ANA, followed by a more robust scope and standards publication in 2008. Finally, in 2015 the ANA released the second edition of Nursing Informatics: Scope and Standards of Practice.

What Is Nursing Informatics? The ANA’s Nursing Informatics: Scope and Standards of Practice (2015) offers the following definition of NI:

Nursing informatics (NI) is the specialty that integrates nursing science with multiple information and analytical sciences to identify, define, manage, and communicate data, information, knowledge and wisdom in nursing practice. NI supports nurses, consumers, patients, the interprofessional healthcare team, and all other stakeholders in their decision-making in all roles and set- tings to achieve desired outcomes. This support is accomplished through the use of information structures, information processes, and information tech- nology. (p. 1–2)

The definition of nursing informatics has undergone several revisions to arrive at this current form. The 1994 ANA definition of informatics indicated that informatics was the integration of nursing science, computer science, and information science, and that nursing informatics supports practice, education, research, and knowledge development (Murphy, 2010). The 2001 version incorporated mention of the support of decision making by patients and providers across all roles and settings and identi- fied information structures, processes, and IT (information technology) as central to informatics (Murphy, 2010). An important change in the 2008 definition of NI is the addition of wisdom to the key concepts of the management of data, information, and knowledge (Murphy, 2010). Finally, in the 2015 version, we note that the sciences are no longer limited to nursing science, information science, and computer science. Cognitive science is also a very important part of nursing informatics. Other sciences that may contribute to NI include library science and information management, mathematics, archival science, and the science of terminologies and taxonomies (ANA, 2015).

Let us reflect more carefully on the current definition of NI by deconstructing each of the statements contained in the ANA’s (2015) definition (statements from the defi- nition are italicized):

• Nursing informatics (NI) is the specialty that integrates nursing science with multiple information and analytical sciences to identify, define, manage, and

108 CHAPeR 6 History and Evolution of Nursing Informatics

communicate data, information, knowledge and wisdom in nursing practice. As we established previously, there are concepts drawn from several sciences that are integrated to support and contribute to NI. The contributions of these sciences become apparent in the actions of NI: identify, define, manage, and communicate. The last part of this statement contains the critical central con- cepts of NI: the data, information, knowledge, and wisdom that are integral to our practice. We will explore these central concepts in more detail in the next section.

• NI supports nurses, consumers, patients, the interprofessional healthcare team, and all other stakeholders in their decision-making in all roles and settings to achieve desired outcomes. This statement refers to the information technology (IT) tools that support our practice and help us to collaborate and communicate with other healthcare professionals, as well as the evolving trends and tools related to patient engagement in managing their own health. All of these con- tribute to better health outcomes. Examples of such tools are electronic health records, bar-code medication administration systems, clinical decision support and other expert systems, patient monitoring devices, and telehealth tools. These and other NI tools are discussed in subsequent chapters.

• This support is accomplished through the use of information structures, infor- mation processes, and information technology. This section of the definition clearly identifies the need for information technologies to provide structure to the data we collect from our patients, and allow for processing of data and information to create knowledge and support wisdom in nursing practice. Think about the fact that with the advent of clinical information systems (CISs), specifically electronic documentation and clinical decision support (CDS) applications, every nurse has the capacity to contribute to the advancement of nursing knowledge on many levels. Imagine the use of IT solutions to capture not only discrete, quantifiable data, but also the nurse’s experiential and intui- tive personal knowledge not typically documented in paper records. Further add to that mix the family history, culture, environmental and social factors, past experiences, and perspectives from patients and families, and it becomes clear that the possibilities for generating new understandings within populations and across the life span and care continuum are endless. Many of these tech- nologies are covered in subsequent chapters.

the DIKW Paradigm The conceptual framework underpinning the science and practice of NI centers on the core concepts of data, information, knowledge, and wisdom, also known as the DIKW paradigm. As an aside, it is important to note that this paradigm is not exclu- sive to nursing, and is in fact used by others who work with data and information. When we assess a patient to determine his or her nursing needs, we gather and then analyze and interpret data to form a conclusion. This is the essence of nursing science. Information is composed of data that were processed using knowledge. Knowledge is the awareness and understanding of a set of information and ways that information can be made useful to support a specific task or arrive at a decision. When we apply

The DIKW Paradigm 109

previous knowledge to data, we convert those data into information, and information into new knowledge—that is, an understanding of which interventions are appropri- ate in practice. Thus information is data made functional through the application of knowledge. Wisdom is the appropriate application of knowledge to a specific situa- tion. In the practice of nursing science, one expects actions to be ultimately directed by wisdom. Wisdom uses knowledge and experience to heighten common sense and insight to exercise sound judgment in practical matters.

Drawing on the work of Matney, Brewster, Sward, Cloyes, and Staggers (2011), Topaz (2013) provided these expanded definitions and examples of the DIKW paradigm:

• Data: The smallest components of the DIKW framework. They are commonly presented as discrete facts; product of observation with little interpretation (Matney et al., 2011). These are the discrete factors describing the patient or his/her environment. Examples include patient’s medical diagnosis (e.g. Interna- tional Statistical Classification of Diseases [ICD-9] diagnosis #428.0: Congestive heart failure, unspecified) or living status (e.g., living alone, living with family, living in a retirement community, etc.). A single piece of data, known as datum, often has little meaning in isolation.

• Information: Might be thought of as “data + meaning” (Matney et al., 2011). Information is often constructed by combining different data points into a meaningful picture, given certain context. Information is a continuum of pro- gressively developing and clustered data; it answers questions such as “who,” “what,” “where,” and “when.” For example, a combination of patient’s ICD-9 diagnosis #428.0 “Congestive heart failure, unspecified” and living status “living alone” has a certain meaning in a context of an older adult.

• Knowledge: Information that has been synthesized so that relations and in- teractions are defined and formalized; it is a build of meaningful information constructed of discrete data points (Matney et al., 2011). Knowledge is often affected by assumptions and central theories of a scientific discipline and is derived by discovering patterns of relationships between different clusters of information. Knowledge answers questions of “why” or “how.” For healthcare professionals, the combination of different information clusters, such as the ICD-9 diagnosis #428.0 “Congestive heart failure, unspecified” + living status “living alone” with an additional information that an older man (78 years old) was just discharged from hospital to home with a complicated new medication regimen (e.g., blood thinners) might indicate that this person is at a high risk for drug-related adverse effects (e.g., bleeding).

• Wisdom: An appropriate use of knowledge to manage and solve human prob- lems (ANA, 2008; Matney et al., 2011). Wisdom implies a form of ethics, or knowing why certain things or procedures should or should not be implemented in healthcare practice. In nursing, wisdom guides the nurse in recognizing the situation at hand based on patients’ values, nurse’s experience, and healthcare knowledge. Combining all these components, the nurse decides on a nursing intervention or action. Benner (2000) presents wisdom as a clinical judgment

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integrating intuition, emotions, and the senses; using the previous examples, wisdom will be displayed when the homecare nurse will consider prioritizing the elderly heart failure patient using blood thinners for an immediate interven- tion, such as a first nursing visit within the first hours of discharge from hospital to assure appropriate use of medications (para. 2).

Reflect on the examples given by Topaz and create your own application example the DIKW scenario.

In the 2015 Nursing Informatics: Scope and Standards of Practice, Ramona Nelson offers a graphic depiction of the DIKW paradigm in NI and how it relates to the evolution of information systems, decision support systems, and expert sys- tems to support clinical practice. Her model indicates that as one moves from data to information to knowledge to wisdom, there is increasing complexity (shown as the X-axis) and increasing interactions and relationships (shown as the Y-axis). Information systems are shown at the intersection of data and information, deci- sion support systems are depicted at the intersection of information and knowledge and expert systems, the most complex of the systems, reside at the intersection of knowledge and wisdom (Figure 6-1). The development of informatics tools to

Increasing Complexity

Increasing Interactions and Interrelationships

Constant Flux

Data Naming,

collecting, and organizing Information


Expert system

Decision support system

Wisdom Understanding, applying, and applying with compassion

Knowledge Interpreting,

integrating, and understanding

Information Organizing, and


Figure 6-1 The Relationship of Data, Information, Knowledge, and Wisdom Copyright Ramona Nelson. Used with the permission of Ramona Nelson, President, Ramona Nelson Consulting at [email protected]. All rights reserved.

The DIKW Paradigm 111

support nursing practice will continue to evolve as we develop more and better un- derstanding of these complex relationships. “The addition of wisdom raises new and important research questions, challenging the profession to develop tools and pro- cesses for classifying, measuring, and encoding wisdom as it relates to nursing and informatics education. Research in these directions will help clarify the relationship between wisdom and the intuitive thinking of expert nurses. Such research will be invaluable in building information systems to better support healthcare practitioners in decision-making” (ANA, 2015, p.6).

Central to the development of robust expert systems is the agreement on and use of standard terminologies that accurately codify and capture the nature of nursing in these electronic systems. Consider that physician contributions to the health of a patient have been codified for some time, i.e., ICD-10. What if we were able to code and thus capture nursing contributions in a similar way? This would help to highlight the specific nursing contributions to patient outcomes.

Capturing and Codifying the Work of Nursing There are major efforts under way—internationally through the International Council of Nurses’ (2013) International Classification of Nursing Practice (ICNP) and in many other initiatives among and within countries—in which nurses are attempting to standardize the language of nursing practice (Hannah, White, Nagle, & Pringle, 2009). These efforts are particularly important in the face of the development of EHRs and HIE (health information exchanges) stimulated by the HITECH Act of 2009. The capacity to encourage and enforce consistent nomenclatures that reflect the practice of nurses is now possible. Standardized language gives both the nursing profession and healthcare delivery systems the capability to capture, codify, retrieve, and analyze the impact of nursing care on client outcomes. For example, with the use and documentation of standardized client assessments, including risk measures, interventions based on best practices, and consistently measured outcomes within different care settings and across the continuum of care, there will be an ability to demonstrate more clearly the contributions and impact of nursing care through the analysis of EHR outputs. Additionally, clinical outcomes can be further understood in the context of care environments, particularly implications related to the availability of human and material resources to support care delivery. The standardization of clinical inputs and outputs into EHRs will eventually provide a rich knowledge base from which practice and research can be enhanced, and will inform better administrative and policy decisions (Nagle, White, & Pringle, 2010). Rutherford (2008) echoed these same sentiments:

A standardized nursing language should be defined so that nursing care can be communicated accurately among nurses and other health care providers. Once standardized, a term can be measured and coded. Measurement of the nursing care through a standardized vocabulary by way of an ED [electronic documentation] will lead to the development of large databases. From these databases, evidence-based standards can be developed to validate the contri- bution of nurses to patient outcomes. (para. 5)

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Thede and Schwiran (2011) identified the benefits of using standardized terminol- ogy as (1) better communication among nurse and other healthcare providers, (2) increased visibility of nursing interventions, (3) improved patient care, (4) enhanced data collection to evaluate nursing care outcomes, (5) greater adherence to standards of care, and (6) facilitation of assessment of nursing competency (para. 2).

Think about this. Some EHRs measure height in feet and inches, others in centi- meters. Weight may be measured in pounds or kilograms. If we want to compare patient data from multiple EHRs in several different healthcare institutions to develop a model to predict the onset of Type II diabetes, these disparate measures will not translate well. Some EHRs force data collection into coded database fields, and these data are more easily analyzed for trends than that same data recorded as free text. Clinicians used to recording data (charting) as text may resist the use of the coded data fields typically presented as dropdown menus in the EHR. As Skrocki (2013) pointed out, “Data interoperability is hindered when clinicians utilize free text documentation. Although text data can be searched with a specific word or word phases, it does not allow for optimal data sharing. When an organization transfers data to another orga- nization, standardized codified data allows for better data interpretation” (p. 77).

Although significant progress has been made in this standardization work, it is still evolving. Box 6-1 discusses standardizing terminologies in nursing; it was contributed by Nicholas Hardiker (2011), a leader in the development of standardized languages that support clinical applications of information and communication technology.


Nicholas Hardiker Agreement on the consistent use of a term, such as “impaired physical mobility,” allows that term to be used for a number of purposes: to provide continuity of care from care provider to care provider, to ensure care quality by facilitating comparisons between care providers, or to identify trends through data aggrega- tion. Since the early 1970s, there has been a concerted effort to promote consis- tency in nursing terminology. This work continues today, driven by the following increasing demands placed on health-related information and knowledge: • Accessibility: It should be easy to access the information and knowledge needed

to deliver care or manage a health service. • Ubiquity: With changing models of healthcare delivery, information and knowl-

edge should be available anywhere. • Longevity: Information should be usable beyond the immediate clinical

encounter. • Reusability: Information should be useful for a range of purposes.

Without consistent terminology, nursing runs the risk of becoming invisible; it will remain difficult to quantify nursing, the unique contribution and impact of nursing will go unrecognized, and the nursing component of electronic health record systems will remain at best rudimentary. Not least, without consistent terminology, the nursing knowledge base will suffer in terms of development and in terms of access, thereby delaying the integration of evidence-based health care into nursing practice.

Capturing and Codifying the Work of Nursing 113

External pressures merely compound this problem. For example, in the United States, the Health Information Technology for Economic and Clinical Health (HITECH) Act, signed in January 2009, provides a financial incentive for the use of electronic health records; similar steps are being taken in other regions. The HITECH Act mandates that EHRs are used in a meaningful way; achieving this goal will be problematic without consistent terminology. Finally, the current and future landscape of information and communication technologies (e.g., connec- tion anywhere, borderless communication, Web-based applications, collaborative working, disintermediation and reintermediation, consumerization, ubiquitous advanced digital content [van Eecke, da Fonseca Pinto, & Egyedi, 2007]) and their inevitable infiltration into health care will only serve to reinforce the need for consistent nursing terminology while providing an additional sense of urgency.

This box explains what is meant by a standardized nursing terminology and lists several examples. It describes in detail the different approaches taken in the development of two example terminologies. It presents, in the form of an interna- tional technical standard, a means of ensuring consistency among the plethora of contemporary standardized nursing terminologies, with a view toward harmoni- zation and possible convergence. Finally, it provides a rationale for the shared de- velopment of models of terminology use—models that embody both clinical and pragmatic knowledge to ensure that contemporary nursing record systems reflect the best available evidence and fit comfortably with routine practice.


A term at its simplest level is a word or phrase used to describe something con- crete (e.g., leg) or abstract (e.g., plan). A nursing terminology is a body of the terms used in nursing. Many nursing terminologies exist, both formal and infor- mal. Nursing terminologies allow nurses to consistently capture, represent, access, and communicate nursing data, information, and knowledge. A standardized nursing terminology is a nursing terminology that is in some way approved by an appropriate authority (de jure standardization) or by general consent (de facto standardization).

In North America, one such authority is the ANA (2007), which operates a process of de jure standardization through its Committee for Nursing Practice Information Infrastructure (CNPII). The ANA-approved list of nursing languages is presented in Box 6-2.

CNPII has also recognized two data element sets: the Nursing Minimum Data Set (NMDS) and the Nursing Management Minimum Data Set (NMMDS). Work on a standardized data element set for nursing, which in the United States began in the 1980s with the NMDS (Werley & Lang, 1988), provided an additional catalyst for the development of many of the aforementioned nursing terminologies that could provide values (e.g., chronic pain) for particular data elements in the NMDS (e.g., nursing diagnosis). The data element sets provide a framework for the uniform collection and management of nursing data; the use of a standardized nursing terminology to represent those data serves further to enhance consistency.

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From relatively humble beginnings, nursing terminologies have evolved signifi- cantly over the past several decades in line with best practices in terminology work. The enumerative approach consists of simple lists of words or phrases repre- sented in a list or a simple hierarchy. In the nursing diagnosis terminology system of the North American Nursing Diagnosis Association (NANDA), a nursing di- agnosis has an associated name or label and a textual definition (NANDA Inter- national, 2008). Each nursing diagnosis may have a set of defining characteristics and related or risk factors. These additional features do not constitute part of the core terminology but instead are intended to be used as an aid to diagnosis. What an enumerative approach to standardizing terminology may lack in terms of hi- erarchical sophistication, it makes up for in terms of simplicity and potential ease of implementation and use.

In contrast, the ontological approach is compositional in nature and provides a partial representation of the entities within a domain and the relationships that hold between them. The evolution of this approach to terminology standard- ization has been facilitated by advances in knowledge representation (e.g., the refinement of the description logic that underpins many contemporary ontolo- gies) and in their accompanying technologies (e.g., automated reasoners that can check consistency and identify equivalence) as well as the subsumption (i.e., sub- class–superclass) relationships within those ontologies.

ICNP version 2 is an example of an ontology. ICNP is described as a unified nursing language system. It seeks to provide a resource that can be used to de- velop local terminologies and to facilitate cross-mapping between terminologies to compare and combine data from different sources; the existence of a number of overlapping but inconsistent standardized nursing terminologies is problem- atic in terms of data comparison and aggregation. The core of ICNP is repre- sented in the Web ontology language (OWL), a recommendation of the World Wide Web Consortium (W3C), and a de facto standard language for representing ontologies (McGuiness & van Harmelen, 2004). Because it is underpinned by description logic, OWL permits the use of automated reasoners that can check consistency, identify equivalence, and support classification within the ICNP ontology.

The results of contemporary terminology work are encouraging. Nevertheless, further work is needed to harmonize standardized nursing terminologies and to scale up and mainstream the development and implementation of models of ter- minology use.

In an ideal world, one would see standardized nursing terminologies and the structures and systems that support their implementation and use merely as means to an end—that is, as tools to support good nursing practice and good patient care. Standardized nursing terminologies are important, but they do not obviate the need to think and work creatively, to do right by the people in our care, and to continue to advance nursing.

Capturing and Codifying the Work of Nursing 115


1. NANDA: Nursing Diagnoses, Definitions, and Classification, 1992; website: www.nanda.org

2. Nursing Interventions Classification System (NIC), 1992; website: nursing .uiowa.edu/cncce/nursing-interventions-classification-overview

3. Clinical Care Classification (CCC), 1992; formerly Home Health Care Classification (HHCC); website: www.sabacare.com

4. Omaha System, 1992; website: www.omahasystem.org 5. Nursing Outcomes Classification (NOC), 1997; Sue Moorehead, PhD, RN,

Center Director; website: nursing.uiowa.edu/cncce/nursing-outcomes- classification-overview

6. Nursing Management Minimum Data Set (NMMDS), 1998; website: www.nursing.umn.edu/sites/nursing.umn.edu/files/nmds-monograph.pdf

7. PeriOperative Nursing Data Set (PNDS), 1999; website: www.aorn.org 8. SNOMED CT, 1999; website: www.ihtsdo.org/snomed-ct 9. Nursing Minimum Data Set (NMDS), 1999; website: www.nursing.umn

.edu/sites/nursing.umn.edu/files/usa-nmds.pdf 10. International Classification for Nursing Practice (ICNP), 2000; website:

www.icn.ch/icnp.htm 11. ABC Codes, 2000; website: www.abccodes.com 12. Logical Observation Identifiers Names and Codes (LOINC), 2002;

website: www.loinc.org


American Nurses Association (ANA). (2007). Nursing practice information infrastructure. Retrieved from http://www.nursingworld.org/MainMenuCategories/Policy-Advocacy /Positions-and-Resolutions/ANAPositionStatements/Position-Statements-Alphabetically /Privacyand Confidentiality.html

McGuiness, D. L., & van Harmelen, F. (Eds.). (2004). OWL Web ontology language overview. World Wide Web Consortium. Retrieved from http://www.w3.org/TR/owl-features

NANDA International. (2008). Nursing diagnoses: Definitions and classification 2009– 2011 edition. Indianapolis, IN: Wiley-Blackwell.

van Eecke, P., da Fonseca Pinto, P., & Egyedi, T., for the European Commission. (2007). EU study on the specific policy needs for ICT standardisation [Final report]. Retrieved from http://ec.europa.eu/idabc/en/document/7040/254.html

Werley, H. H., & Lang, N. M. (Eds.). (1988). Identification of the Nursing Minimum Data Set. New York, NY: Springer.

At least two decades of work has been directed toward articulating standard- ized data elements that reflect nursing practice. The nursing profession has been steadily moving toward consensus on the adoption of data standards. In fact, sev- eral “consensus conferences” have been hosted in recent years by the University

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of Minnesota, with the goal of developing “a national action plan and harmonize existing and new efforts of multiple individuals and organizations to expedite in- tegration of standardized nursing data within EHRs and ensure their availability in clinical data repositories for secondary use” (Westra et al., 2015 para. 3). Con- sider that as clinical information systems are widely implemented, as standards for nursing documentation and reporting are adopted, and as healthcare IT solutions continue to evolve, the synthesis of findings from a variety of methods and world- views becomes much more feasible. As we move toward a standard terminology to capture the work of nursing, we also will have the ability to mine electronic record data to tease out best practices and promote care improvements. Information tech- nology is not a panacea for all of the challenges found in health care, but it will provide the nursing profession with an unprecedented capacity to generate and disseminate new knowledge at rapid speed, thus supporting the knowledge work of nursing.

the Nurse as a Knowledge Worker As we have already established, all nurses use data and information. This information is then converted to knowledge. The nurse then acts on this knowledge by initiating a plan of care, updating an existing one, or maintaining status quo. Does this use of knowledge make the nurse a knowledge worker?

The term knowledge worker was first coined by Peter Drucker in his 1959 book, Landmarks of Tomorrow (Drucker, 1994). Knowledge work is defined as nonrepetitive, nonroutine work that entails a significant amount of cognitive activity (Sorrells-Jones & Weaver, 1999a). Drucker (1994) describes a knowledge worker as one who has advanced formal education and is able to apply theoretical and analytical knowledge. According to Drucker, the knowledge worker must be a continuous learner and a specialist in a field. McCormick (2009) estimates that a knowledge worker spends at least 50% of his or her work time searching for and evaluating information.

According to Androwich (2010), it is important to understand that there is a dual role for accessing and using information (content) in health care. In the first instance, when the nurse is caring for an individual patient, evidence-based information (con- tent) and patient data need to be available at the point of care to inform the present patient encounter. In the second instance, patient data that are entered by the nurse in the process of documentation need to be entered in such a manner that they are able to be aggregated to inform future patient encounters.

The world is transitioning from the Industrial Age to the Information Age (Snyder- Halpern, Corcoran-Perry, & Narayan, 2001; Sorrells-Jones & Weaver, 1999a). In the early 1900s, the workforce consisted predominantly of farmers. After World War I, the workforce began to become predominantly industrial. This transition occurred when many farmers and domestic help moved to the cities to take jobs at factories. Today, the industrial worker is slowly being replaced by the technologist (Drucker, 1994); the technologist is adept at using both mind and hand. Many industrial

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workers are finding it increasingly more difficult to obtain jobs because they do not have the educational base or mindset required of knowledge workers (Drucker, 1994). The technologist is no longer trained on the job, as industrial workers tradi- tionally were, which can cause significant problems for the industrial worker who does not have the education required to transition to a knowledge worker position (Drucker, 1994; Sorrells-Jones & Weaver, 1999a).

Knowledge workers are innovators, and the work they produce is the founda- tion for organizational sustainability and growth. Knowledge workers are special- ized, have advanced education, and typically have a high degree of autonomy and control over their own work environments (Davenport, Thomas, & Cantrell, 2002; Sorrells-Jones & Weaver, 1999a). Such individuals are most efficient when they are working in a multidisciplinary team. These teams are typically composed of members with complementary knowledge bases. The team members possess problem-solving and decision-making skills and advanced interpersonal skills. All members of the team are considered equal and are there to contribute their expertise. Leadership shifts and changes as the team tackles different parts of the project, with the topic expert taking the lead. A well-functioning team can consistently outperform an individual (Sorrells-Jones & Weaver, 1999b). Many of these teams become focused and passion- ate about the project on which they are working.

A key impediment to team effectiveness is a lack of understanding among team members and a lack of respect for one another’s knowledge and experience (Sorrells-Jones & Weaver, 1999a). Another barrier to efficiency within the multidis- ciplinary team is the individual knowledge worker who does not want to give up his or her own identity even though he or she may be swayed by other professional opin- ions. Professionals have a more difficult time adjusting to working in a team than do nonprofessionals. Professionals fail very few times in their lives, which often results in their not being able to learn from their failures (Sorrells-Jones & Weaver, 1999b). Knowledge workers also tend to be resistant to change, and as a result they dig in their heels and refuse to adapt to changes that management has implemented to im- prove the work process or workflow (Davenport et al., 2002).

Companies that employ knowledge workers have been forced to change their management structures to better support these employees. Management no longer commands, but rather seeks to inspire workers to produce the best product (Drucker, 1992). Companies that rely on knowledge workers have come to the realization that the machines are unproductive without the knowledge of those workers. Loyalty is no longer purchased with a paycheck but is earned by giving knowledge workers the ability to use their knowledge effectively and innovatively (Drucker, 1992). In turn, the physical environment and workplace arrangements have been adjusted to maxi- mize the workflow of the knowledge workers (Davenport et al., 2002). Many of these changes have occurred in the business world but have been slow to be adopted in health care.

Right now, health care is in the process of transitioning from the Industrial Age to the Information Age. This transition has proved challenging because of the suc- cess of healthcare institutions that have enjoyed using current management methods. Its history of success will make it difficult for the healthcare industry to abandon the

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old so as to learn the new. A new philosophy recognizing that employees are mature, self-reliant, independent-thinking adults who function as partners in carrying out the work of the organization is needed. The organization needs to view (knowledge worker) employees as an asset and supply the resources, tools, information, and power they need to self-manage their work. Innovation needs to be supported, espe- cially when it meets the customers’ needs, desires, and wishes (Weaver & Sorrells- Jones, 1999).

Nursing entails a significant amount of knowledge and nonknowledge work. Knowledge work includes such duties as interpreting trends in laboratories and symp- toms. Nonknowledge work includes such tasks as calling the laboratory to check on laboratory results or making beds. Nurses, on a daily basis, rely on their extensive clinical information and specialized knowledge to implement and evaluate the pro- cesses and outcomes related to patient care (Snyder-Halpern et al., 2001).

Snyder-Halpern and colleagues (2001) have identified four tasks associated with human information processing: (1) data gathering, (2) information use, (3) creative ap- plication of knowledge to clinical practice, and (4) generation of new knowledge. These four tasks are associated with four roles that nursing takes on as a knowledge worker: data gatherer, information user, knowledge user, and knowledge builder, respectively.

Nurses are data gatherers by nature. They collect and record objective clinical data on a daily basis. These items include such things as patient history information, vital signs, and patient assessment data. Nurses as data gatherers transition to infor- mation users when they begin to interpret the data that they have collected and recorded. Nurses as information users then structure the clinical data into informa- tion that can be used to guide patient care decisions (Snyder-Halpern et al., 2001). An example of this is when the nurse notices that the patient’s blood pressure is elevated. Information users transition to knowledge users when they begin to notice trends in a patient’s clinical data and determine whether the clinical data fall within or outside of the normal data range. Nurses transition from knowledge users to knowledge builders when they examine clinical data and trends across groups of patients. These trends are interpreted and compared to current scientific data to determine whether these data would improve the nursing knowledge domain. An example of the transi- tion of a nurse as knowledge user to a nurse as knowledge builder is an observation of medication compliance rates over a specified time period for patients diagnosed with chronic high blood pressure, with the nurse then comparing these rates to evidence-based literature to determine if this information improves the nursing knowledge base (Snyder-Halpern et al., 2001).

Snyder-Halpern and colleagues (2001) found that as nurses assumed each of these roles, they required different types of decision support processes to support their knowledge needs. The data gatherer requires a system that captures and stores data accurately and reliably and allows the data to be readily accessed. Most current healthcare decision support systems (DSSs) support the nurse in this role. The infor- mation user role requires a system that can transform clinical data into a format that allows for easy recognition of patterns and trends. These systems recognize the trend and display it for the nurse, who in turn uses this information to adjust the plan of care for the patient. The information user role is generally well supported by current

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DSSs. The knowledge user role is the least supported role, and many systems are currently looking at ways to support nurses in this role. One advantage of these DSSs is their ability to bring knowledge to nurses so that they do not have to retrieve the information themselves, which allows them to adjust a patient’s plan of care in a more efficient and timely manner. The knowledge builder role is typically seen in conjunction with the nurse researcher role and quality management roles. These roles typically look at aggregated data that have been captured over time and from numerous patients, with these data then being compared to clinical variables and interventions; this analysis results in the development of new domain knowledge (Snyder-Halpern et al., 2001).

Most of the available DSS tools for nursing practice, although promising, are simplistic and in early development. Typically, DSS includes such tools as (1) com- puterized alerts and reminders (e.g., medication due, patient has an allergy, potas- sium level abnormal), (2) clinical guidelines (e.g., best practice for prevention of skin breakdown), (3) online information retrieval (e.g., CINAHL, drug information), (4) clinical order sets and protocols, and (5) online access to organizational policies and procedures. In the future, these tools may be expanded to include applications with embedded case-based reasoning.

In the context of nursing practice supported by CISs, nurses will eventually have access to evidence and knowledge derived from large aggregates of clinical data, including nursing interventions and resultant outcomes. Experiential evidence provides practice guidelines and directives to ensure concurrence with optimal clinical decisions and actions. To illustrate, consider this example: A nurse assesses a patient who has experienced a stroke for signs of skin breakdown, photographs and documents early ulcerations, and submits the photos and documentation to CIS. The nurse receives an option to review the best practices for care of the patient and to submit a request for a consult to a wound management specialist. The ongoing clinical findings, treatment, and response are logged and aggregated with similar cases, thereby contributing to the knowledge base related to nursing and care of the integumentary system.

The informational elements of CISs can also be designed to include specifics about individuals’ multicultural practices and beliefs. Consider the situation where a client voices concerns about her prescribed dietary treatment and expresses a preference for a female care provider. With a query to the CIS for the client’s history and sociocul- tural background, the nurse obtains explanations for these requests that derive from the patient’s religious and cultural background and makes a notation to highlight and carry this information forward in the electronic record for any future admissions. Future systems may also be designed to provide access to standards of ethical practice and online access to experts in the field of moral reasoning to guide clinical interac- tions and decision making.

Through each and every instance of interacting with the CIS, nurses add to these repositories of knowledge by chronicling their daily clinical challenges and queries. The continued expansion and aggregation of knowledge about clients and popula- tions; their personal, cultural, physical, and clinical presentations; and individuals’ experiences and the guidance received from others enhance the delivery of personal- ized, knowledge-based care.

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Graves and Corcoran (1989) have suggested that nursing knowledge is “simulta- neously the laws and relationships that exist between the elements that describe the phenomena of concern in nursing (factual knowledge) and the laws or rules that the nurse uses to combine the facts to make clinical nursing decisions” (p. 230). In their view, not only does knowledge support decision making, but it also leads to new discoveries. Thus one might think about the future creation of nursing knowledge as being the discovery of new laws and relationships that can continue to advance nursing practice.

New technologies have made the capture of multifaceted data and information possible through the use of such technologies as digital imaging (e.g., photography to support wound management). Now included as part of the clinical record, such im- ages add a new dimension to the assessment, monitoring, and treatment of illness and the maintenance of wellness. Beyond the use of computer keyboards, input devices are being integrated with CISs and used to gather data and information for the fol- lowing clinical and administrative purposes:

• Biometrics (e.g., facial recognition, security) • Voice and video recordings (e.g., client interviews and observations, diagnostic

procedures, ultrasounds) • Voice-to-text files (e.g., voice recognition for documentation) • Medical devices, (e.g., infusion pumps, ventilators, hemodynamic monitors) • Bar-code and radio-frequency identification (RFID) technologies (e.g., medica-

tion administration) • Telehomecare monitoring (e.g., for use in diabetes and other chronic disease


These are but a few of the emerging capabilities that allow for numerous data inputs to be transposed, combined, analyzed, and displayed to provide informa- tion and views of clinical situations currently not possible in a world dominated by hard-copy documentation. Through the application of information and communica- tion technologies to support the capture and processing (i.e., interpretation, organi- zation, and structuring) of all relevant clinical data, relationships can be identified and formalized into new knowledge. This transformational process is at the core of generating new nursing knowledge at a rate never experienced before; in the con- text of current research paradigms, the same relationships would likely take years to uncover.

As CISs advance, nurses will eventually become generators of new knowl- edge by virtue of designs that embed machine learning and case-based reasoning methods within their core functionality. This functionality will become possible only with national and international adoption of standardized nursing language, as previously described. Imagine the power of having access to systems that ag- gregate the same data elements and information garnered from multiple clinical situations and provide a probability estimate of the likely outcome for individuals of a certain age, with a specific diagnosis and comorbid conditions, medication profile, symptoms, and interventions. How much more rapidly would an under- standing of the efficacy of clinical interventions be elucidated? Historically, some knowledge might have taken years of research to discover (e.g., that long-standing

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practices are sometimes more harmful than beneficial). A case in point is the long-standing practice of instilling endotracheal tubes with normal saline before suctioning (O’Neal, Grap, Thompson, & Dudley, 2001). Based on the evidence gathered through several studies, the potentially deleterious effects of this practice have become widely recognized. Conceivably, a meta-analysis approach to clinical studies will be expedited by convergence of large clinical data repositories across care settings, thereby making available to practitioners the collective contribu- tions of health professionals and longitudinal outcomes for individuals, families, and populations.

Nurses need to be engaged in the design of CIS tools that support access to and the generation of nursing knowledge. As we have emphasized, the adoption of clini- cal data standards is of particular importance to the future design of CIS tools. We are also beginning to see the development and use of expert systems that implement knowledge automatically without human intervention. For example, an insulin pump that senses the patient’s blood glucose level and administers insulin based on those data is a form of expert system. The expert system differs from decision support tools in that the decision support tools require the human to act on the information pro- vided, whereas the expert system intervenes automatically based on an algorithm that directs the intervention. Consider that as CISs are widely implemented, as standards for nursing documentation and reporting are adopted, and as healthcare IT solutions continue to evolve, the synthesis of findings from a variety of methods and world- views becomes much more feasible.

BoX 6-3 CAse stUDY: CAstING to tHe FUtURe

In the year 2025, nursing practice enabled by technology has created a profes- sional culture of reflection, critical inquiry, and interprofessional collaboration. Nurses use technology at the point of care in all clinical settings (e.g., primary care, acute care, community, and long-term care) to inform their clinical deci- sions and effect the best possible outcomes for their clients. Information is gath- ered and retrieved via human–technology biometric interfaces including voice, visual, sensory, gustatory, and auditory interfaces, which continuously monitor physiologic parameters for potentially harmful imbalances. Longitudinal records are maintained for all citizens from their initial prenatal assessment to death; all lifelong records are aggregated into the knowledge bases of expert systems. These systems provide the basis of the artificial intelligence being embedded in emerging technologies. Smart technologies and invisible computing are ubiqui- tous in all sectors where care is delivered. Clients and families are empowered to review and contribute actively to their record of health and wellness. Invasive diagnostic techniques are obsolete, nanotechnology therapeutics are the norm, and robotics supplement or replace much of the traditional work of all health professions. Nurses provide expertise to citizens to help them effectively manage their health and wellness life plans, and navigate access to appropriate informa- tion and services.

122 CHAPeR 6 History and Evolution of Nursing Informatics

the Future The future landscape is yet to be fully understood, as technology continues to evolve with a rapidity and unfolding that is rich with promise and potential peril. Box 6-3 helps us to imagine what future practice might entail. It is anticipated that computing power will be capable of aggregating and transforming additional multidimensional data and information sources (e.g., historical, multisensory, experiential, and genetic sources) into CIS. With the availability of such rich repositories, further opportunities will open up to enhance the training of health professionals, advance the design and application of CDSs, deliver care that is informed by the most current evidence, and engage with individuals and families in ways yet unimagined.

The basic education of all health professions will evolve over the next decade to incorporate core informatics competencies. In general, the clinical care environments will be connected, and information will be integrated across disciplines to the benefit of care providers and citizens alike. The future of health care will be highly dependent on the use of CISs and CDSs to achieve the global aspiration of safer, quality care for all citizens.

The ideal is a nursing practice that has wholly integrated informatics and nursing education and that is driven by the use of information and knowledge from a myriad of sources, creating practitioners whose way of being is grounded in informatics. Nursing research is dynamic and an enterprise in which all nurses are engaged by virtue of their use of technologies to gather and analyze findings that inform specific clinical situations. In every practice setting, the contributions of nurses to health and well-being of citizens will be highly respected and parallel, if not exceed, the preemi- nence granted physicians.

summary In this chapter, we have traced the development of informatics as a specialty, defined nursing informatics, and explored the DIKW paradigm central to informatics. We also explored the need for and the development of standardized terminologies to capture and codify the work of nursing and how informatics supports the knowledge work of nursing. This chapter advanced the view that every nurse’s practice will make contributions to new nursing knowledge in dynamically interactive CIS environ- ments. The core concepts associated with informatics will become embedded in the practice of every nurse, whether administrator, researcher, educator, or practitioner. Informatics will be prominent in the knowledge work of nurses, yet it will be a sub- tlety because of its eventual fulsome integration with clinical care processes. Clinical care will be substantially supported by the capacity and promise of technology today and tomorrow.

Most importantly, readers need to contemplate a future without being limited by the world of practice as it is known today. Information technology is not a panacea for all of the challenges found in health care, but it will provide the nursing profes- sion with an unprecedented capacity to generate and disseminate new knowledge at rapid speed. Realizing these possibilities necessitates that all nurses understand and leverage the informatician within and contribute to the future.

Summary 123

References American Nurses Association (ANA). (2008). Nursing informatics: Scope and standards of

practice. Springfield, MD: Nursesbooks.org. American Nurses Association (ANA). (2015). Nursing informatics: Scope and standards of

practice (2nd ed.). Silver Spring, MD: Nursesbooks.org. Androwich, I. (2010, June). Paper presented at Delaware Valley Nursing Informatics Annual

Meeting, Malvern, PA. Benner, P. (2000). The wisdom of our practice. The American Journal of Nursing, 100(10),

99–101, 103, 105. Blum, B. (1986). Clinical information systems. New York, NY: Springer-Verlag. Davenport, T. H., Thomas, R., & Cantrell, S. (2002). The mysterious art and science of

knowledge worker performance. MIT Sloan Management Review, 44(1), 23–30. Drucker, P. F. (1992). The new society of organizations. Harvard Business Review, 70(5),

95–104. Drucker, P. F. (1994). The age of social transformation. Atlantic Monthly, 274(5), 52–80. Graves, J., & Corcoran, S. (1989). The study of nursing informatics. Image, 21(4), 227–230. Hannah, K. J., White, P., Nagle, L. M., & Pringle, D. M. (2009). Standardizing nursing

information in Canada for inclusion in electronic health records: C-HOBIC. Journal of the American Medical Informatics Association, 16(4), 524–530.

Hardiker, N. (2011). Developing standardized terminologies to support nursing practice. In D. McGonigle & K. Mastrian (Eds.), Nursing informatics and the foundation of knowledge (2nd ed., pp. 111–120). Sudbury, MA: Jones & Bartlett Learning.

Matney, S., Brewster, P., Sward, K., Cloyes, K., & Staggers, N. (2011). Philosophical approaches to the data–information–knowledge–wisdom framework. Advances in Nursing Science, 34(1), 6–18.

McCormick, J. (2009, May 14). Preparing for the future of knowledge work: A day in the life of the knowledge worker. Infomanagment Direct Online. Retrieved from http://www .information-management.com/infodirect/2009_121/knowledge_worker_information_ management_ecm_content_management-10015405-1.html

Murphy, J. (2010). Nursing informatics: The intersection of nursing, computer, and information sciences. Nursing Economic$, 28(3), 204–207.

Nagle, L. M., White, P., & Pringle, D. (2010). Realizing the benefits of standardized measures of clinical outcomes. Electronic Healthcare, 9(2), e3–e9.


1. How is the concept of wisdom in NI like or unlike professional nursing judg- ment? Can any aspect of nursing wisdom be automated?

2. How can a single agreed-upon model of terminology use (with linkages to a single terminology) help to integrate knowledge into routine clinical practice?

3. Can you create examples of how expert systems (not decision support systems but true expert systems) can be used to support nursing practice?

4. How would you incorporate the data-to-wisdom continuum into a job descrip- tion for nurse?

5. What are the possibilities to accelerate the generation and uptake of new nurs- ing knowledge?

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O’Connor, J. J., & Robertson, E. F. (2003). Florence Nightingale. Retrieved from http://www -history.mcs.st-andrews.ac.uk/history/Printonly/Nightingale.html

O’Neal, P. V., Grap, M. J., Thompson, C., & Dudley, W. (2001). Level of dyspnea experienced in mechanically ventilated adults with and without saline instillation prior to endotracheal suctioning. Intensive Critical Care Nursing, 17(6), 356–363.

Ozbolt, J., & Saba, V. (2008). A brief history of nursing informatics in the United States of America. Nursing Outlook, 56(5), 199–205.

Rutherford, M. (2008). Standardized nursing language: What does it mean for nursing practice? OJIN: The Online Journal of Issues in Nursing, 13(1). doi:10.3912/OJIN .Vol13No01PPT05

Skrocki, M. (2013). Standardization needs for effective interoperability. Transactions of the International Conference on Health Information Technology Advancement. Paper 32, 76–83. Retrieved from http://scholarworks.wmich.edu/ichita_transactions/32

Snyder-Halpern, R., Corcoran-Perry, S., & Narayan, S. (2001). Developing clinical practice environments supporting the knowledge work of nurses. Computers in Nursing, 19(1), 17–26.

Sorrells-Jones, J., & Weaver, D. (1999a). Knowledge workers and knowledge-intense organizations, Part 1: A promising framework for nursing and healthcare. Journal of Nursing Administration, 29(7/8), 12–18.

Sorrells-Jones, J., & Weaver, D. (1999b). Knowledge workers and knowledge-intense organizations, Part 3: Implications for preparing healthcare professionals. Journal of Nursing Administration, 29(10), 14–21.

Thede, L., Schwiran, P. (2011). Informatics: The standardized nursing terminologies: A national survey of nurses’ experiences and sttitudes—Survey I*. OJIN: The Online Journal of Issues in Nursing, 16(2). doi: 10.3912/OJIN.Vol16No02InfoCol01

Topaz, M. (2013). Invited editorial: The hitchhiker’s guide to nursing informatics theory: Using the Data-Knowledge-Information-Wisdom framework to guide informatics research. Online Journal of Nursing Informatics (OJNI), 17(3). Retrieved from http://ojni.org/issues/?p=2852

Weaver, D., & Sorrells-Jones, J. (1999). Knowledge workers and knowledge-intense organizations, Part 2: Designing and managing for productivity. Journal of Nursing Administration, 29(9), 19–25.

Westra, B., Latimer, G., Matney, S., Park, J., Sensmeier, J., Simpson, R., . . . Delaney, C. (2015). A national action plan for sharable and comparable nursing data to support practice and translational research for transforming health care. Journal of the American Medical Informatics Assocociation, 22(3), 600–607. doi:10.1093/jamia/ocu011

References 125

Key Terms » Advocate/policy

developer » Certification » Consultant » Data » Decision support/

outcomes manager

» Educator » Entrepreneur » Informatics » Informatics

innovator » Informatics nurse


» Information » Knowledge » Knowledge worker » Medical

informatics » Nursing informatics


» Product developer » Project manager » Researcher » TIGER initiative

1. Describe the nursing informatics specialty. 2. Explore the scope and standards of nursing

informatics practice.

3. Assess the evolving roles and competencies of nursing informatics practice.

4. Appreciate the future of nursing informatics in our rich, technology-laden healthcare environments.


Introduction In the previous chapter, you reviewed the history and evolution of nursing informatics, and the ways that all nurses use informatics for practice. In this chapter, we will focus on nursing informatics as a specialty. Nursing informatics (NI) is an established, yet ever-evolving, specialty. Those choosing NI as a career find it full of numerous and varied opportunities. Previously, most nurse informaticists entered the field by showing an understanding and enthusiasm for working with computers. Now, however, nurses have many educational opportunities available to become formally trained in the field of NI to become an informatics nurse specialist (INS). We will explore the scope and standards of NI; NI roles, education, and specialization; rewards of working in the field; and organizations and professional journals of the INS.

Nursing Contributions to Healthcare Informatics Nursing has been involved in the purchase, design, and implementation of information systems (ISs) since the 1970s (Saba & McCormick, 2006). One of the first health IS vendors studied how nurses managed patient care and realized that nursing activity was the core of patient activity and needed to be the foundation of the health or clinical IS. Nursing infor- maticists have been instrumental in developing, critiquing, and promoting standard nursing terminologies to be used in the health IS. Nursing is involved heavily in the design of educational materials for practicing nurses, student nurses, other healthcare workers, and patients. Computers have revolutionized the way individuals access information and have revolutionized educational and social networking processes.

Nursing Informatics as a Specialty Dee McGonigle, Kathleen Mastrian, Julie A. Kenney, and Ida Androwich



Scope and Standards NI is important to nursing and health care because it focuses on representing nursing data, information, and knowledge. As identified in the earlier edition of the Nursing Informatics: Scope and Standards of Practice, NI meets the following needs for health informatics (American Nurses Association [ANA], 2008; Brennan, 1994):

• Provides a nursing perspective • Showcases nursing values and beliefs • Provides a foundation for nurses in NI • Produces unique knowledge • Distinguishes groups of practitioners • Emphasizes the interest for nursing • Provides needed nursing language and word context

In 2008, the ANA published a revised scope and standards of nursing informat- ics practice. This publication included the most recent INS standards of practice and the INS standards of professional performance, and addressed the who, what, when, where, how, why, and functional roles of INS practice. There were three overarching standards of practice (ANA, 2008, p. 33):

1. Incorporate theories, principles, and concepts from appropriate sciences into informatics practice.

2. Integrate ergonomics and human–computer interaction (HCI) principles into informatics solution design, development, selection, implementation, and evaluation.

3. Systematically determine the social, legal, and ethical impact of an informatics solution within nursing and health care.

The standards of practice and professional performance for an INS are listed in Box 7-1.

In 2015, the second edition of the ANA’s Nursing Informatics: Scope and Standards of Practice was released. The ANA described the functional areas of nursing informatics as follows (p. 19):

• Administration, leadership, and management • Systems analysis and design • Compliance and integrity management • Consultation • Coordination, facilitation, and integration • Development of systems, products, and resources • Educational and professional development • Genetics and genomics • Information management/operational architecture • Policy development and advocacy • Quality and performance improvement • Research and evaluation • Safety, security, and environmental health

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As INSs assume their roles, it is evident that typical roles cover more than one functional area and that our “informatics solutions are more closely integrated with the delivery of care” (ANA, 2015, p. 36). The ANA also denoted telehealth as an integrated functional area that is a dynamic health information technology. As nursing, information, computer, and cognitive sciences continue to evolve, so will NI functions. With the rapid advancements we have already seen in the previous decade, we know that the INSs of the future will be assuming roles and working in areas that we have not imagined yet.

Nursing Informatics Roles NI has become a viable and essential nursing specialty with the introduction of com- puters and the EHR to health care. Many nurses entered the NI field because of their

Nursing Informatics Roles 129



Standard 1: Assessment Standard 2: Diagnosis, Problems, and Issues Identification Standard 3: Outcomes Identification Standard 4: Planning Standard 5: Implementation Standard 5A: Coordination of Activities Standard 5B: Health Teaching and Health Promotion Standard 5C: Consultation Standard 6: Evaluation


Standard 7: Ethics Standard 8: Education Standard 9: Evidence-Based Practice and Research Standard 10: Quality of Practice Standard 11: Communication Standard 12: Leadership Standard 13: Collaboration Standard 14: Professional Practice Evaluation Standard 15: Resource Utilization Standard 16: Environmental Health

Data from American Nurses Association (ANA). (2015). Nursing informatics: Scope and standards of practice (2nd ed.). Silver Spring, MD: Nursesbooks.org.

natural curiosity and their dedication to being lifelong learners. Others who entered this field might have done so by accident: Perhaps they were comfortable working with computers and their coworkers used them as a resource for computer-related questions. The introduction of the EHR has forced all clinicians to learn to use this new technology and incorporate it into their already busy days. According to one estimate, nurses spend as little as 10–15% of their days with their patients and as much as 28–50% of their day documenting (Healthcare Information and Manage- ment Systems Society [HIMSS] Nursing Informatics Awareness Task Force, 2007; Munyisia, Yu, & Hailey, 2014). Assisting nurses to incorporate this new technology into their daily workflow is one of many challenges that the INS may tackle. Even though INSs appear to work behind the scenes, INSs impact the health and clinical outcomes of patients.

The INS may take on numerous roles; refer to Figure 7-1. For example, one position that INSs fill quite well is the role of the project manager, as a result of their ability to simultaneously manage multiple complex situations. Because of the breadth of the NI field, however, many INSs find that they need to fur- ther specialize. The following list includes some typical INS positions. It is far from comprehensive, because this field changes rapidly, as does technology (ANA, 2015; Thede, 2003).

• Project Manager. In the project manager role, the INS is responsible for the planning and implementation of informatics projects. The INS uses communica- tion, change management, process analysis, risk assessment, scope definition, and team building. This role acts as the liaison among clinicians, management, IS, stakeholders, vendors, and all other interested parties.

• Consultant. The INS who takes on the consultant role provides expert advice, opinions, and recommendations based on his or her area of expertise. Flex- ibility, good communication skills, excellent interpersonal skills, and extensive clinical and informatics knowledge are highly desirable skill sets needed by the NI consultant.

• Educator. The success or failure of an informatics solution can be directly related to the education and training that were provided for end users. The INS who chooses the educator role develops and implements educational materials and sessions and provides education about the system to new or current employees during a system implementation or an upgrade.

• Researcher. The researcher role entails conducting research (especially data mining) to create new informatics and clinical knowledge. Research may range from basic informatics research to developing clinical decision support tools for nurses.

• Product Developer. An INS in the product developer role participates in the design, production, and marketing of new informatics solutions. An understanding of business and nursing is essential in this role.

• Decision Support/Outcomes Manager. Nurses assuming the role of decision support/outcomes manager use tools to maintain data integrity and reliability.

130 CHAPTER 7 Nursing Informatics as a Specialty

Contributing to the development of a nursing knowledge base is an integral component of this role.

• Advocate/Policy Developer. INSs are key to advocating for the patients and healthcare systems and developing the infrastructure of health policy. Policy development on a local, national, and international level is an integral part of the advocate/policy developer role.

• Clinical Analyst/System Specialist. INSs may work at varying levels and serve as a link between nursing and information services in healthcare organizations.

• Entrepreneur. Those nurses involved in the entrepreneur role combine their pas- sion, skills, and knowledge to develop marketable business ideas by analyzing nursing information needs and developing and marketing solutions.

Specialty Education and Certification Many nurses who entered into NI did so without any formal education in this field. In many cases, these nurses served as the unit resource for computer or program questions. Often, they acquired their skills through on-the-job training or by attending classes. Although this pathway to the NI field is still available today, more formal ways of acquiring these skills exist. The informatics nurse has a bachelor of science degree in nursing and additional knowledge and expertise in the informatics field (ANA, 2015). The INS holds an advanced degree or a post-master’s certificate and is prepared to assume roles requiring this advanced knowledge. INSs may attend informatics confer- ences and obtain contact hours or continuing education units.

Specialty Education and Certification 131

Figure 7-1 NI Roles

Competencies needed

Expertise needed


• Technical • Utility • Leadership

• Users • Modifiers • Innovators

• Project manager

• Consultant

• Educator

• Researcher

• Product developer

• Decision support

• Outcomes manager

• Advocate

• Policy developer

• Clinical analyst

• Entrepreneur

Box 7-2 lists some of the pioneering colleges and universities that offer advanced degrees or certificates in NI. This is not a comprehensive list; new programs are continually being developed. Local colleges and universities should be researched to see which may have informatics programs.

132 CHAPTER 7 Nursing Informatics as a Specialty



Chamberlin College of Nursing: www.chamberlain.edu/admissions/graduate/ Master-of-Science-in-Nursing/informatics-track

Duke University: http://nursing.duke.edu/academics/programs/msn/health -informatics-major

Excelsior College: www.excelsior.edu/nursing-masters-informatics-faq Loyola University Chicago: www.luc.edu/hsm New York University: http://nursing.nyu.edu/academics/masters Northeastern University: www.healthinformatics.neu.edu University of Alabama at Birmingham: www.uab.edu/nursing/home/msn/

nursing-informatics-major University of Colorado at Denver: www.ucdenver.edu/academics/colleges/

nursing/programs-admissions/masters-programs/ms-program/specialties/ healthcareinformatics/Pages/default.aspx

University of Iowa: http://informatics.grad.uiowa.edu/health-informatics /curriculum

University of Kansas: http://nursing.kumc.edu/academics/master-of-science /nursing-informatics.html

University of Maryland: www.nursing.umaryland.edu/academics/grad/specialties/ni University of North Carolina at Chapel Hill: http://nursing.unc.edu/

academics/graduate-practice-programs/master_of_science_in_nursing/ health-care-systems-msn/

University of Pittsburgh: www.nursing.pitt.edu/degree-programs/master-science- nursing-msn/msn-program-majors/nursing-informatics/nursing

University of Utah: http://nursing.utah.edu/programs/msnursinginformatics.php University of Washington: www.son.washington.edu/portals/cipct Vanderbilt University: www.nursing.vanderbilt.edu/msn/ni.html


Chamberlain College of Nursing: www.chamberlain.edu/admissions/graduate/ graduate-certificate-programs

Indiana University: nursing.iupui.edu/continuing/informatics.shtml Loyola University Chicago: www.luc.edu/media/lucedu/nursing/pdfs/

Informatics%20Certificate.pdf Northeastern University: www.ccis.northeastern.edu/program/health-informatics-

grad-certificate/ Penn State University: www.worldcampus.psu.edu/degrees-and-certificates

/nursing-informatics-certificate/overview University of Iowa: informatics.grad.uiowa.edu/health-informatics/curriculum

Nurses who choose to specialize in NI have two certification options available to them. The first is obtained through the American Nurses Credentialing Center (ANCC). The ANCC’s examination is specific for the informatics nurse. The applicant must be a licensed registered nurse with at least 2 years of recent experience and have a baccalaureate degree in nursing. The applicant must have completed 30 con- tact hours of continuing education in informatics. The applicant must meet one of the following criteria: (1) 2,000 hours practicing as an informatics nurse, (2) 1,000 hours practicing as an informatics nurse and 12 semester hours of graduate academic credit toward an NI degree, or (3) completion of an NI degree that included at least 200 supervised practicum hours. For further information on this certification exami- nation, visit www.nursecredentialing.org/Certification/NurseSpecialties/Informatics. This website includes the aforementioned criteria and provides further information about test eligibility, fees, examination content, examination locations, study materi- als, and practice tests.

The second certification examination is sponsored by the Healthcare Information and Management Systems Society (HIMSS). Candidates who successfully pass this examination are designated as certified professionals in healthcare information and management systems. The HIMSS examination is open to any candidate who is involved in healthcare informatics. Candidates must hold positions in the following fields: administration/management, clinical IS, e-health, IS, or management engi- neering. Candidates may include any of the following: chief executive officers, chief information officers, chief operating officers, senior executives, senior managers, IS technical staff, physicians, nurses, consultants, attorneys, financial advisors, technol- ogy vendors, academicians, management engineers, and students. Candidates must meet the following criteria to be eligible to sit for the examination: a baccalaureate degree plus 5 years of associated information and management systems experience, with 3 of those years being in health care; or a graduate degree plus 3 years of associated infor- mation and management systems experience, with 2 of those years being in health care. The information discussed in this text and additional information about the examina- tion can be found by visiting http://www.himss.org/ASP/certification_cphims.asp.

Nursing Informatics Competencies One challenge that has been identified in the literature and continues to plague health care is the vast differences in computer literacy and information management skills that healthcare workers possess (Gassert, 2008; McNeil, Elfrink, Beyea, Pierce, & Bickford, 2006; Topkaya & Kaya, 2014). Gassert (2008) felt that new graduates were not adequately literate. Barton (2005) believed that new nurses should have the following critical skills: use e-mail, operate Windows applications, search databases, and know how to work with the institution-specific nursing software used for chart- ing and medication administration. These skills should not be limited to just new nurses, but instead should be required of all nurses and healthcare workers.

Staggers, Gassert, and Curran (2001) advocated that nursing students and prac- ticing nurses should be educated on core NI competencies. Although information technology and informatics concepts certainly need to be incorporated into nursing school curricula, progress in this area has been slow. In the 1980s, a nursing group

Nursing Informatics Competencies 133

of the International Medical Informatics Association convened to develop the first level of nursing competencies. While developing these competencies, the nursing group found that nurses fell in to one of the following three categories: (1) user, (2) developer, or (3) expert. These categories have since been expanded.

Staggers and colleagues (2001) decided that the NI competencies developed in the 1980s were inadequate and needed to be updated. These authors reviewed 35 NI competency articles and 14 job descriptions, which resulted in 1,159 items that were sorted into three broad categories: (1) computer skills, (2) informatics knowledge, and (3) informatics skills. These items were then placed in a database, where redun- dant items were removed. When this process was completed, 313 items remained.

When these items were then further subdivided, Staggers and colleagues, along with the American Medical Informatics Association (AMIA) work group, realized that these competencies were not universal to all nurses; thus, before it could be determined if the competency was an NI competency, nursing skill levels needed to be defined. The group determined that practicing nurses could be classified into four categories: (1) beginning nurse, (2) experienced nurse, (3) informatics nurse specialist, and (4) informatics innovator. Each of these skill levels needed to be defined before Staggers and colleagues (2001) could determine which level was the most appropri- ate for that skill set. Table 7-1 provides the definition criteria for each skill level. Once the levels were defined, the group determined that 305 items were NI competencies and placed them into appropriate categories.

Staggers, Gassert, and Curran (2002) conducted the seminal work in this area, a Delphi study to validate the placement of the competencies into the correct skill level. Of the 305 original competencies identified, 281 achieved an 80% approval rating for both importance as a competency and placement in the correct practice level. The authors stressed that this is a comprehensive list; thus, for a nurse to enter a particu- lar skill level, he or she need not have mastered every item listed for that skill level. For a list of competencies by skill level, see Table 7-2.

In 2004, a group of nurses came together after attending a national informatics conference to ensure that nursing was equally recognized in the national informat- ics movement. This so-called Technology Informatics Guiding Education Reform (TIGER) team determined that using informatics was a core competency for all healthcare workers. They also determined that many nurses lack information tech- nology skills, which limits their ability to access evidence-based information that could otherwise be incorporated into their daily practice. This group is currently working on a plan to include informatics courses in all levels of nursing education; when that effort is complete, they will examine how to get the information out to practicing nurses who are not currently enrolled in an academic program (HIMSS, 2016). Many of the items identified by the TIGER team as lacking in both nursing students and practicing nurses are items that Staggers et al. (2002) determined to be NI competencies. To learn more about the TIGER initiative, visit http://www.himss.org/ professionaldevelopment/tiger-initiative.

Through the work of Hunter et al. (2011; 2013; 2015) and McGonigle et al. (2014; 2015), the competencies for nursing informatics practice Levels 1 through 4 have been further refined with two self-assessment tools developed. Hunter and colleagues focused on the Level 1 and Level 2 competencies and developed the self-assessment of

134 CHAPTER 7 Nursing Informatics as a Specialty

competencies TANIC tool, Tiger-based Assessment of Nursing Informatics Competen- cies. McGonigle and colleagues focused on the competencies related to the advanced levels 3 and 4, developing the self-assessment of competencies NICA L3/L4 tool, Nurs- ing Informatics Competency Assessment Level 3/Level 4 (ANA, 2015, p. 43).

Nursing Informatics Competencies 135

Beginning Nurse

• Has basic computer technology skills and information management skills

• Uses institution’s information systems and the contained information to manage patients

Experienced Nurse

• Proficient in a specialty

• Highly skilled in using computer technology skills and information management skills to support his or her specialty area of practice

• Pulls trends out of data and makes judgments based on this information

• Uses current systems, but will collaborate with informatics nurse specialist regarding concerns or suggestions provided by staff

Informatics Nurse Specialist

• RN with advanced education who possesses additional knowledge and skills specific to computer technology and information management

• Focuses on nursing’s information needs, which include education, administration, research, and clinical practice

• Application and integration of the core informatics sciences: information, computer, and nursing science

• Uses critical thinking, process skills, data management skills, systems life cycle development, and computer skills

Informatics Innovator

• Conducts informatics research and generates informatics theory

• Vision of what is possible

• Keen sense of timing to make things happen

• Creative in developing solutions

• Leads the advancement of informatics practice and research

• Sophisticated level of skills and understanding in computer technology and information management

• Cognizant of the interdependence of systems, disciplines, and outcomes and is able to finesse situations to obtain the best outcome

Reproduced from Staggers, N., Gassert, C., & Curran, C. (2001). Informatics competencies for nurses at four levels of practice. Journal of Nursing Education, 40(7), 303–316. With permission of SLACK Incorporated.

TABLE 7-1 Definitions of Four Levels of Practicing Nurses

136 CHAPTER 7 Nursing Informatics as a Specialty

Based on research conducted by Hunter, McGonigle, and Hebda (2013), the online self-assessment instrument, TIGER- based Assessment of Nursing Informatics Competencies (TANIC) was developed. This instrument assesses the Level I: Beginning Nurse and Level 2: Experienced Nurse competencies.

Level 1: Beginning Nurse

• Start the computer and log on securely to access select applications/software

• Access and send email

• Collect and enter patient data into the information system

Level 2: Experienced Nurse

• Identify the risks and limitations of surfing the Internet to locate evidence-based practice information

• Gather data to draw and synthesize conclusions

• Explain how to sustain the integrity of information resources

Based on the research conducted by McGonigle, Hunter, Hebda, and Hill (2014), the online self-assessment instrument, Nursing Informatics Competency Assessment – Level 3/Level 4 (NICA L3/L4) was developed. This instrument assesses the Level 3: Informatics Nurse Specialist and Level 4: Informatics Innovator competencies.

Level 3: Informatics Nurse Specialist

• Fluent in nursing informatics and nursing terminologies

• Applies aspects of human technology interface to screen, device, and software design

• Teach nurses how to locate, access, retrieve, and evaluate information

Level 4: Informatics Innovator

• Analyze systems

• Transform software programs to support data analysis and aggregation

• Lead research efforts to determine and address application needs


Hill, T., McGonigle, D., Hunter, K., Sipes, C. & Hebda, T. (2014). An instrument for assessing advanced nursing informatics competencies. Journal of Nursing Education and Practice, 4(7), 104–112.

Hunter, K., McGonigle, D. & Hebda, T. (2011, December). Operationalizing TIGER NI competencies for online assessment of perceived competency. TIGER Initiative Foundation Newsletter. Retrieved from http://www.thetigerinitiative.org [must subscribe to access]

Hunter, K., McGonigle, D., & Hebda, T. (2013). TIGER-based measurement of nursing informatics competencies: The development and implementation of an online tool for self-assessment. Journal of Nursing Education and Practice, 3(12), 70–80. doi: 10.5430/jnep.v3n12p70

Table 7-2 Nursing Informatics Compentencies by Skill Level

It is critical that nurses and INSs can demonstrate competence. As there were many definitions of the term competency, the authors of these tools first had to define the term competency. Hunter et al. (2013) concluded that

Competency, then, is a concept applicable to multiple situations. At its most basic, competency denotes having the knowledge, skills, and abil- ity to perform or do a specific task, act, or job. Depending on the context, competency can refer to adequate or expert performance. For this research, competency was used to mean adequate knowledge, skills, and ability. Nursing-informatics competency was defined as adequate knowledge, skills, and ability to perform specific informatics tasks. (p. 71)

The teams began instrument development by synthesizing both seminal and cur- rent literature to construct instrument items; they reviewed, formatted, and initiated instrument testing with a Delphi study and then piloted the resulting instrument with experts. Cronbach’s alpha values were calculated. The TANIC Cronbach was 0.944 for clinical information management, 0.948 for computer skills, and 0.980 for in- formation literacy. The NICA L3/L4 reliability estimates were as follows: computer skills, 0.909; informatics knowledge, 0.982; and informatics skills, 0.992. The Cron- bach’s reliability estimates for each tool showed strong internal consistency reliability.

The TANIC self-assessment instrument has four parts, including questions about demographics and the self-assessment, consisting of 85 items covering basic computer literacy, clinical information management, and information literacy. The NICA L3/L4 self-assessment instrument also has four parts: questions about demographics and the 178 item self-assessment, consisting of computer skills, informatics knowledge, and informatics skills.

These tools and those that will follow are extremely important because they help each of us identify our own level of comfort with technology and our self-confidence in our ability to perform these skills/tasks. Nurse educators in all practice settings and school-based programs must help their nurses or nursing students recognize deficits in their current knowledge and skills. The nurse educa- tors can facilitate the professional development of their nurses or nursing students through the identification of courses or skill-based labs that will help them turn their deficits into strengths.

Nursing Informatics Competencies 137

Hunter, K., McGonigle, D., Hebda, T., Sipes, C., Hill, T., & Lamblin, J. (2015). TIGER-based assessment of nursing informatics competencies (TANIC). In A. Rocha, S. Correia, S. Costanza, & L. Reis (Eds.). New contributions in information systems and technologies: Volume 1 (advances in intelligent systems and computing) (pp. 171–177). Basel, Switzerland: Springer. DOI: 10.1007/978-3-319-16486-1_7

McGonigle, D., Hunter, K., Hebda, T., & Hill, T. (2014). Self-assessment of Level 3 and Level 4 NI competencies tool development. Retrieved from http://www.himss.org/file/1307246/download?token=cNOya_Lm

McGonigle, D., Hunter, K., Hebda, T., Sipes, C., Hill, T., & Lamblin, J. (2015). Nursing informatics competencies assessment Level 3 and Level 4 (NICA L3/L4). In A. Rocha, S. Correia, S. Costanza, & L. Reis (Eds.). New contributions in information systems and technologies: Volume 1 (advances in intelligent systems and computing) (pp. 209–214). Basel, Switzerland: Springer. DOI: 10.1007/978-3-319-16486-1_21

Rewards of NI Practice NI is a nursing specialty that does not focus on direct patient care but instead focuses on enhancing patient care and safety and improving the workflow and work pro- cesses of nurses and other healthcare workers. The INS is instrumental in designing the electronic healthcare records that healthcare workers use on a daily basis. This nurse is also responsible for designing tools that allow healthcare workers to access patient information more efficiently than they have been able to do so in the past. Watching these changes take place brings great satisfaction to the INS.

Change is a factor that an INS deals with on a daily basis. This dynamic nature of the position is probably its most difficult aspect, because people deal with change differently. Understanding change theory and processes and appreciating how change affects people assist the INS in developing strategies to encourage healthcare workers to accept changes and become proficient in informatics solutions that have been implemented. Seeing the change adopted with a minimal amount of discord is very rewarding to the INS.

The INS also participates in informatics organizations that allow INSs to network and share experiences with one another. Such interactions allow INSs to bring these new solutions back to their respective organizations and improve informatics trouble spots. Attending professional conferences allows the INS to stay abreast of changes in the industry. Continuing education may help the INS to improve a process or work- flow within the hospital or to change the way a system upgrade is rolled out.

NI Organizations and Journals One of the first informatics organizations founded was HIMSS. HIMSS, which cele- brated its 55th year in 2016, was launched in 1961 and now has offices throughout the United States and Europe. HIMSS currently represents more than 20,000 indi- viduals and 300 corporations. This organization supports both local and national chapters. It has many associated work groups, one of which is an NI work group. HIMSS is well known for its development of industry-wide policies and its educa- tional and professional development initiatives, all of which are directed toward the goal of ensuring safe patient care. Membership in HIMSS offers many advantages for nurses, such as access to numerous weekly and monthly publications, and two scholarly journals, the Journal of Healthcare Information Management and the Online Journal of Nursing Informatics. HIMSS offers many educational programs, including virtual expos, which allow participants to experience the expo without having to travel. These educational opportunities allow participants to network with colleagues and peers, which is a valuable asset in this field. HIMSS also peri- odically conducts NI workforce surveys. It is interesting to review the most current survey results and compare them to your setting and role.

The American Medical Informatics Association (AMIA) was founded in 1990 when three health informatics associations merged. AMIA currently has more than 3,000 members who reside in 42 countries. This organization focuses on the develop- ment and application of biomedical and healthcare informatics. Members include physicians, nurses, dentists, pharmacists, health information technology professionals, and biomedical engineers. AMIA offers many benefits to its members, such as weekly and monthly publications and a scholarly journal, JAMIA—The Journal of the

138 CHAPTER 7 Nursing Informatics as a Specialty

American Medical Informatics Association. Members may join a working group that is specific to their specialty, including an NI work group. AMIA offers multiple educa- tional opportunities and many opportunities for networking with colleagues.

The American Nursing Informatics Association (ANIA) was established in 1992 to provide an opportunity for southern California informatics nurses to meet. It has since grown to a national organization whose members include healthcare profes- sionals who work with clinical IS, educational applications, data collection/research applications, administrative/DSS, and those who have an interest in the field of NI. In 2009, ANIA merged with the Capital Area Roundtable on Informatics in Nursing (CARING). Membership benefits include the following:

• Access to a network of more than 3,200 informatics professionals in 50 states and 30 countries

• Active email list • Quarterly newsletter indexed in CINAHL and Thomson • Job bank with employee-paid postings • Reduced rate at the ANIA Annual Conference • Reduced rate for CIN: Computers, Informatics, Nursing • ANIA Online Library of on-demand and webinar education activities • Membership in the Alliance for Nursing Informatics • Web-based meetings • In-person meetings and conferences held nationally and worldwide

The Alliance for Nursing Informatics (ANI) is a coalition of NI groups that represents more than 3,000 nurses and 20 distinct NI groups in the United States. Its membership represents local, national, and international NI members and groups. These individual groups have developed organizational structures and have established programs and publications. ANI functions as the link between NI organizations and the general nursing and healthcare communities and serves as the united voice of NI.

These groups have been instrumental in establishing the informatics community. Box 7-3 lists some of these organizations and their publications, but many other infor- matics groups exist.

The Future of Nursing Informatics NI is still in its infancy, as is the technology that the INS uses on a daily basis. NI will continue to influence development of the EHR; in turn, the EHR will continue to improve and will one day accurately capture the care nurses give to their patients. This is a formidable challenge because much of the care provided by nurses is intan- gible in nature. In the future, the EHR will provide data to the INS that can then be used to improve nursing workflow and to determine whether current practices are the most efficient and beneficial to the patient.

Nursing and health care are on a roller-coaster ride that will undoubtedly prove very interesting. New technology is being introduced at a breakneck speed, and nurs- ing and health care must be ready to ride this roller coaster. Programs need to be developed to keep nurses and healthcare workers abreast of the new technological changes as they occur, and educating new and current nurses presents a significant challenge to the INS. Therefore, the INS’s future looks very promising and rewarding.

The Future of Nursing Informatics 139

According to the ANA (2015), five trends will influence the future of nursing informatics:

1. Changing practice roles in nursing 2. Increasing informatics competence requirements for all nurses 3. Rapidly evolving technology 4. Regulatory changes and quality standards that include healthcare consumers as

partners in healthcare models 5. Care delivery models and innovation (p. 52)

As the future becomes yesterday, people are waking up to the fact that we need the healthcare team prepared with informatics competencies. All healthcare providers should receive education on informatics because they need basic informatics skills, such as the ability to use search engines to find information about a specific topic. Consequently, all healthcare providers need to be able to attend classes to improve their computer skills and knowledge. Those entering the nursing field need a general knowledge of computer

140 CHAPTER 7 Nursing Informatics as a Specialty


Alliance for Nursing Informatics Website: www.allianceni.org

American Health Information Management Association Website: www.ahima.org Journal: Journal of AHIMA & Perspectives in Health Information Manage-

ment (online)

American Medical Informatics Association Website: www.amia.org Journal: JAMIA—Journal of the American Medical Informatics Association NI website: www.amia.org/programs/working-groups/nursing-informatics

American Nursing Informatics Association (includes Capital Area Roundtable on Informatics in Nursing [CARING])

Website: www.ania.org Resources link: www.ania.org/Resources.htm Journal: CIN: Computers, Informatics, Nursing

Health Information and Management Systems Society Website: www.himss.org Chapter websites: www.himss.org/ASP/chaptersHome.asp Journal: The Journal of Healthcare Information Management NI website: www.himss.org/asp/topics_nursingInformatics.asp

International Medical Informatics Association Website: www.imia.org Journal: International Journal of Medical Informatics NI website: www.imia.org/ni

Online Journal of Nursing Informatics Website: www.himss.org/ojni

capabilities. Many new trends—such as Web 2.0, increased attention to evidence-based practice, and a better understanding of genomics—will impact care delivery in the 21st century, and NI nurses need to be prepared to lead these efforts to improve care and help nurses have a voice in the informatics skills they need, as well as in the advances and tools they use, including the EHR (Bakken, Stone, & Larson, 2008; Lavin, Harper, & Barr, 2015).

Change plays a significant part in health care today, and those interested in NI must embrace change. They must also be good at enticing others to embrace change. Nevertheless, NI candidates must realize that change is often accompanied by resis- tance. For their part, INSs must be ready to leave the bedside, because nurses entering into this field will no longer be giving hands-on care. NI is a very challenging but very rewarding field. In an ideal world, all healthcare agencies will employ at least one INS, and all nurses will embrace the knowledge worker title.

Summary Nursing informatics is a specialty that integrates nursing science, computer science, and information science to manage and communicate data, information, knowledge, and wisdom in nursing practice. Our definition: the synthesis of nursing science, information science, computer science, and cognitive science for the purpose of man- aging, disseminating, and enhancing healthcare data, information, knowledge, and wisdom to improve collaboration and decision making, provide high-quality patient care, and advance the profession of nursing. Informatics practices support nurses as they seek to care for their patients effectively and safely, by making the information that they need more readily available. Nurses have been actively involved in this field since computers were introduced to health care. With the advent of the EHR, it became apparent that nursing needed to develop its own terminology related to the new technology and its applications; NI has been instrumental in this process.

Today, the healthcare industry employs the largest number of knowledge workers in the world. Nurses, as knowledge workers in technology-laden healthcare facilities, must continuously improve their informatics competencies. The INS is instrumental in leading the advancement of informatics concepts and tools in all settings and across all specialties. NI is a specialty governed by standards that have been established by the ANA. Because NI is a very diverse field, many INSs eventually specialize in one segment of the field. While NI is an established specialty, the core NI principles are utilized by all nurses. Nurs- ing informatics competencies have been developed to encompass all levels of practice and ensure that entry-level nurses are ready to enter the more technologically advanced field of nursing, as well as establish advanced competencies for the INS’s specialty practice. These competencies may be used to determine the educational needs of current staff members.

The growth of the NI field has resulted in the formation of numerous NI orga- nizations or subgroups of the medical informatics organizations. Nurses no longer have to enter the field by chance but can obtain an advanced degree in NI at many well-established universities. In addition, INSs may continue their learning by attend- ing the numerous conferences offered.

NI has grown tremendously as a specialty since its inception and has the expectation of continued growth. The NI specialty not only engages nurses and patients, but also en- gages data to improve patient outcomes, enhance patient care, and advance the science of nursing. It will be interesting to see where NI and INSs take health care in the future.

Summary 141

References Alliance for Nursing Informatics. (2013). Homepage. Retrieved from http://www.allianceni.org American Medical Informatics Association. (2013). Homepage. Retrieved from http://www

.amia.org American Nurses Association (ANA). (2008). Nursing informatics: Scope and standard of practice.

Silver Spring, MD: Nursesbooks.org. American Nurses Association (ANA). (2015). Nursing informatics: Scope and standard of practice

(2nd ed.). Silver Spring, MD: Nursesbooks.org. Androwich, I. (2010, June). Paper presented at Delaware Valley Nursing Informatics Annual

Meeting, Malvern, PA. Bakken, S., Stone, P., & Larson, E. (2008). A nursing informatics research agenda for 2008–2018:

Contextual influences and key components. Nursing Outlook, 56(5), 206–214. Barton, A. J. (2005). Cultivating informatics competencies in a community of practice. Nursing

Administration Quarterly, 29(4), 323–328. Brennan, P. F. (1994). On the relevance of discipline to informatics. Journal of the American

Medical Informatics Association, 1(2), 200–201. Davenport, T. H., Thomas, R., & Cantrell, S. (2002). The mysterious art and science of

knowledge worker performance. MIT Sloan Management Review, 44(1), 23–30. Drucker, P. F. (1992). The new society of organizations. Harvard Business Review, 70(5), 95–104. Drucker, P. F. (1994). The age of social transformation. Atlantic Monthly, 274(5), 52–80. Gassert, C. (2008). Technology and informatics competencies. Nursing Clinics of North America,

43(4), 507–521. doi: 10.1016/j.cnur.2008.06.005 Health Information and Management Systems Society (HIMSS). (2006). HIMSS dictionary of

healthcare information technology terms, acronyms and organizations. Chicago, IL: Author. Health Information and Management Systems Society (HIMSS). (2013). Homepage. Retrieved

from http://www.himss.org Health Information and Management Systems Society (HIMSS). (2016). The TIGER initiative.

Retrieved from http://www.himss.org/professionaldevelopment/tiger-initiative HIMSS Nursing Informatics Awareness Task Force. (2007). An emerging giant: Nursing

informatics. Nursing Management, 13(10), 38–42. Hunter, K., McGonigle, D. & Hebda, T. (2011, December). Operationalizing TIGER NI

competencies for online assessment of perceived competency. TIGER Initiative Foundation Newsletter. Retrieved from http://www.thetigerinitiative.org

Hunter, K., McGonigle, D., & Hebda, T. (2013). TIGER-based measurement of nursing informatics competencies: The development and implementation of an online tool for self-assessment. Journal of Nursing Education and Practice (JNEP), 3(12), 70–80. doi: 10.5430/jnep.v3n12p70

Hunter, K., McGonigle, D., Hebda, T., Sipes, C., Hill, T., & Lamblin, J. (2015). TIGER-Based assessment of nursing informatics competencies (TANIC). In A. Rocha, S. Correia, S. Costanza,

142 CHAPTER 7 Nursing Informatics as a Specialty


1. A hospital is seeking to update its EHR. It has been suggested that an INS be hired. This position does not involve direct patient care and the administration is struggling with how to justify the position. How can this position be justified?

2. It is important that all nurses be informatics competent at all levels. In particular, at which levels should the INS be able to exhibit competency? Provide several examples of the knowledge and skills that an INS might demonstrate.

3. How does nursing move from measuring the tasks completed to measuring the final outcome of the patient? How can the INS help us reach this goal?

& L. Reis (Eds.), New contributions in information systems and technologies: Volume 1 (Advances in intelligent systems and computing) (pp. 171–177). Basel, Switzerland: Springer. DOI: 10.1007/978-3-319-16486-1_7

Lavin, M., Harper, E., & Barr, N. (2015). Health information technology, patient safety, and professional nursing care documentation in acute care settings. OJIN The Online Journal of Issues in Nursing, 20(2). doi: 10.3912/OJIN.Vol20No02PPT04

McCormick, J. (2009, May 14). Preparing for the future of knowledge work: A day in the life of the knowledge worker. Infomanagment Direct Online. Retrieved from http://www .information-management.com/infodirect/2009_121/knowledge_worker_information _management_ecm_content_management-10015405-1.html

McGonigle, D., Hunter, K., Hebda, T., & Hill, T. (2014). Self-assessment of Level 3 and Level 4 NI competencies tool development. Retrieved from http://www.himss.org/file/1307246 /download?token=cNOya_Lm

McGonigle, D., Hunter, K., Hebda, T., Sipes, C., Hill, T., & Lamblin, J. (2015).  Nursing informatics competencies assessment Level 3 and Level 4 (NICA L3/L4). In A. Rocha, S. Correia, S. Costanza, & L. Reis (Eds.), New contributions in information systems and technologies: Volume 1 (Advances in intelligent systems and computing) (pp. 209–214). Basel, Switzerland: Springer. DOI: 10.1007/978-3-319-16486-1_21

McNeil, B. J., Elfrink, V., Beyea, S. C., Pierce, S., & Bickford, C. J. (2006). Computer literacy study: Report of qualitative findings. Professional Nursing, 22(1), 52–59.

Merriam-Webster Online. (2011). Worker. Retrieved from http://mw1.merriam-webster.com /dictionary/worker

Munyisia, E., Yu, P., & Hailey, D. (2014). The effect of an electronic health record system on nursing staff time in a nursing home: A longitudinal study. Australasian Medical Journal, 7(7), 285–293. http//dx.doi.org/10.4066/AMJ.2014.2072

O’Connor, J. J., & Robertson, E. F. (2003). Florence Nightingale. Retrieved from http://www -history.mcs.st-andrews.ac.uk/history/Printonly/Nightingale.html

Saba, V. K., & McCormick, K. A. (Eds.). (2006). Essentials of nursing informatics (4th ed.). New York, NY: McGraw-Hill.

Snyder-Halpern, R., Corcoran-Perry, S., & Narayan, S. (2001). Developing clinical practice environments supporting the knowledge work of nurses. Computers in Nursing, 19(1), 17–26.

Sorrells-Jones, J., & Weaver, D. (1999a). Knowledge workers and knowledge-intense organizations, Part 1: A promising framework for nursing and healthcare. Journal of Nursing Administration, 29(7/8), 12–18.

Sorrells-Jones, J., & Weaver, D. (1999b). Knowledge workers and knowledge-intense organizations, Part 3: Implications for preparing healthcare professionals. Journal of Nursing Administration, 29(10), 14–21.

Staggers, N., Gassert, C., & Curran, C. (2001). Informatics competencies for nurses at four levels of practice. Journal of Nursing Education, 40(7), 303–316.

Staggers, N., Gassert, C., & Curran, C. (2002). A Delphi study to determine informatics competencies for nurses at four levels of practice. Nursing Research, 51(6), 383–390.

Thede, L. Q. (2003). Informatics and nursing: Opportunities and challenges (2nd ed.). Philadelphia, PA: Lippincott Williams & Wilkins.

Topkaya, S. & Kaya, N. (2014). Nurses’ computer literacy and attitudes towards the use of computers in health care. Retrieved from http://nursing-informatics.com/niassess/ Topkaya_2014.pdf

Weaver, D., & Sorrells-Jones, J. (1999). Knowledge workers and knowledge-intense organizations, Part 2: Designing and managing for productivity. Journal of Nursing Administration, 29(9), 19–25.

Wickramasinghe, N., & Ginzberg, M. J. (2001). Integrating knowledge workers and the organization: Role of IT. International Journal of Health Care Quality Assurance, 14(6), 245–253.

References 143

Objectives 1. Explore the Health Insurance Portability and

Accountability Act (HIPAA) of 1996. 2. Describe the purposes of the Health Information

Technology for Economic and Clinical Health (HITECH) Act of 2009.

3. Explore how the HITECH Act is enhancing the security and privacy protections of HIPAA.

4. Determine how the HITECH Act and its impact on HIPAA apply to nursing practice.

5. Identify informatics technologies likely to be legislated in the future.

Key Terms » Access » Agency for

Healthcare Research and Quality

» American National Standards Institute

» American Recovery and Reinvestment Act

» Centers for Medicare and Medicaid Services

» Certified EHR technology

» Civil monetary penalties

» Compliance » Confidentiality » Consequences » Electronic health

records » Enterprise

integration » Entities » Gramm-Leach-Bliley

Act » Health disparities » Health information

technology » Health Insurance

Portability and Accountability Act

» Health Level Seven

» Healthcare- associated infections

» International Standards Organization

» Meaningful use » National Institute

of Standards and Technology

» Office of Civil Rights » Office of the Na-

tional Coordinator for Health Informa- tion Technology

» Open Systems Interconnection

» Patient-centered care

» Policies » Privacy » Protected health

information » Qualified electronic

health record » Rights » Sarbanes-Oxley Act » Security » Standards » Standards-

developing organizations

» Treatment/ payment/operations

Introduction Two key pieces of legislation have shaped the nursing informatics land- scape: the Health Insurance Portability and Accountability Act (HIPAA) of 1996 and the Health Information Technology for Economic and Clinical Health Act (HITECH) of 2009. This chapter presents an overview of the HITECH Act, including the Medicare and Medicaid health information technology (HIT) provisions of the law. Nurses need to be familiar with the goals and purposes of this law, know how it enhances the security and privacy protections of the Health Insurance Portability and Accountability Act (HIPAA) of 1996, and appreciate how it otherwise affects nursing practice in the emerging electronic health records age. The concepts of “meaning- ful use” and “certified EHR technology” also are explored in this chapter, as well as potential future legislation regulating medical devices and apps and the movement toward payment based on quality. Figure 8-1 provides a snapshot of the legislation affecting the informatics landscape.

HIPAA Came First HIPAA was signed into law by President Bill Clinton in 1996. Hellerstein (1999) summarized the intent of the act as follows: to curtail healthcare fraud and abuse, enforce standards for health information, guarantee the security and privacy of health information, and ensure health insurance portability for employed persons. Consequences were put into place for institutions and individuals who violate the requirements of this act. For this text, we concentrate on the health information security and privacy aspects of HIPAA, which are outlined as follows:

The privacy provisions of the federal law, the Health Insurance Portability and Accountability Act of 1996 (HIPAA), apply to health information created or maintained by healthcare providers

Legislative Aspects of Nursing Informatics: HITECH and HIPAA Kathleen M. Gialanella, Kathleen Mastrian, and Dee McGonigle



who engage in certain electronic transactions, health plans, and healthcare clearinghouses. The U.S. Department of Health and Human Services (USDHHS) issued the regulation, “Standards for Privacy of Individually Identifiable Health Information,” applicable to entities covered by HIPAA. The Office for Civil Rights (OCR) is the Departmental component responsible for implementing and enforcing the privacy regulation. (U.S. Department of Health and Human Services, 2015, para. 5–6)

The need and means to guarantee the security and privacy of health information was the focus of numerous debates. Comprehensive standards for the implementa- tion of this portion of the Act eventually were finalized, but the process to adopt final standards took years. In August 1998, the USDHHS released a set of proposed rules addressing health information management. Proposed rules specific to health information privacy and security were released in November 1999. The purpose of the proposed rules was to balance patients’ rights to privacy and providers’ needs for access to information (Hellerstein, 2000).

Hellerstein (2000) summarized the proposed privacy rules. The rules do the following:

• Define protected health information as “information relating to one’s physical or mental health, the provision of one’s health care, or the payment for that health care, that has been maintained or transmitted electronically and that can be reasonably identified with the individual it applies to” (Hellerstein, 2000, p. 2). Figure 8-2 depicts the types of information protected under HIPAA.

• Propose that authorization by patients for release of information is not nec- essary when the release of information is directly related to treatment and payment for treatment. Specific patient authorization is not required for research, medical or police emergencies, legal proceedings, and collection of data for public health concerns. All other releases of health information require a specific form for each release and only information pertinent to the issue at

Figure 8-1 Health Informatics Regulations


Meaningful Use 2010

HIPAA 1996

MACRA 2015

146 CHAPTER 8 Legislative Aspects of Nursing Informatics: HITECH and HIPAA

hand is allowed to be released. All releases of information must be formally documented and accessible to the patient on request.

• Establish patient ownership of the healthcare record and allow for patient-initiated corrections and amendments.

• Mandate administrative requirements for the protection of healthcare informa- tion. All healthcare organizations are required to have a privacy official and an office to receive privacy violation complaints. A specific training program for employees that includes a certification of completion and a signed statement by all employees that they will uphold privacy procedures must be developed and implemented. All employees must re-sign the agreement to uphold privacy every 3 years. Sanctions for violations of policy must be clearly defined and applied.

• Mandate that all outside entities that conduct business with healthcare organi- zations (e.g., attorneys, consultants, auditors) must meet the same standards as the organization for information protection and security.

• Allow protected health information to be released without authorization for research studies. Patients may not access their information in blinded research studies because this access may affect the reliability of the study outcomes.

• Propose that protected health information may be deidentified before release in such a manner that the identity of the patient is protected. The healthcare orga- nization may code the deidentification so that the information can be reidenti- fied once it has been returned.

• Apply only to health information maintained or transmitted by electronic means.

As concerns mounted and deadlines loomed, the healthcare arena prepared to comply with the requirements of the law. The administrative simplification portion

Protected Health Information

Medical History; Lab or Test ResultsDemographics

Insurance Information

Figure 8-2 What Is Protected Health Information?

HIPAA Came First 147

of this law was intended to decrease the financial and administrative burdens by standardizing the electronic transmission of certain administrative and financial trans- actions. This section also addressed the security and privacy of healthcare data and information for the covered entities of healthcare providers who transmit any health information in electronic form in connection with a covered transaction, health plans, and healthcare clearinghouses (Centers for Medicare & Medicaid Services, 2014).

The privacy requirements, which went into effect on April 14, 2003, limited the release of protected health information without the patient’s knowledge and consent. Covered entities must comply with the requirements. Notably, they must dedicate a privacy officer, adopt and implement privacy procedures, educate their personnel, and secure their electronic patient records. Most individuals are familiar with the need to notify patients of their privacy rights, having signed forms on interacting with healthcare providers.

According to the USDHHS (2002), the privacy rule provides certain rights to patients: the right to request restrictions to access of the health record; the right to request an alternative method of communication with a provider; the right to receive a paper copy of the notice of privacy practices; the right to file a complaint if the patient believes his or her privacy rights were violated; the right to inspect and copy one’s health record; the right to request an amendment to the health record; and the right to see an account of disclosures of one’s health record. This places the burden of maintaining privacy and accuracy on the healthcare system, rather than the patient.

On October 16, 2003, the electronic transaction and code set standards became effective. At the time, they did not require electronic transmission, but rather man- dated that if transactions were conducted electronically, they must comply with the required federal standards for electronically filed healthcare claims. “The Secretary has made the Centers for Medicare & Medicaid Services (CMS) responsible for en- forcing the electronic transactions and code sets provisions of the law” (“Guidance on Compliance with HIPAA Transactions and Code Sets,” 2003, para. 3).

The security requirements went into effect on April 21, 2005, and required the cov- ered entities to put safeguards that protect the confidentiality, integrity, and availability of protected health information when stored and transmitted electronically into place.

The safeguards that were addressed were administrative, physical, and technical. The administrative safeguards refer to the documented formal policies and proce- dures that are used to manage and execute the security measures. They govern the protection of healthcare data and information and the conduct of the personnel. The physical safeguards refer to the policies and procedures that must be in place to limit physical access to electronic information systems. Technical safeguards are the poli- cies and procedures used to control access to healthcare data and information. Safe- guards need to be in place to control access whether the data and information are at rest, residing on a machine or storage medium, being processed, or in transmission, such as being backed up to storage or disseminated across a network.

Overview of the HITECH Act The federal Health Information Technology for Economic and Clinical Health Act of 2009 (HITECH Act; Leyva & Leyva, 2011), enacted February 17, 2009, is part of the

148 CHAPTER 8 Legislative Aspects of Nursing Informatics: HITECH and HIPAA

American Recovery and Reinvestment Act (ARRA). The ARRA, also known as the “Stim- ulus” law, was enacted to stimulate various sectors of the U.S. economy during the most severe recession this country had experienced since the Great Depression of the late 1920s and early 1930s. The health information technology (HIT) industry was one area where lawmakers saw an opportunity to stimulate the economy and improve the delivery of health care at the same time. This explains why the title of the HITECH Act contains the phrase “for Economic and Clinical Health.”

The ARRA is a lengthy piece of legislation that is organized into two major sec- tions: Division A and Division B. Each division contains several titles. Title XIII of Division A of the ARRA is the HITECH Act. It addresses the development, adoption, and implementation of HIT policies and standards and provides enhanced privacy and security protections for patient information—an area of the law that is of paramount concern in nursing informatics. Title IV of Division B of the ARRA is considered part of the HITECH Act. It addressed Medicare and Medicaid HIT and provided signifi- cant financial incentives to healthcare professionals and hospitals that adopted and engaged in the “meaningful use” of electronic health records (EHRs) technology.

At the time the HITECH Act was enacted, it was estimated that less than 8% of U.S. hospitals used a basic EHR system in at least one of their clinical units, and less than 2% of U.S. hospitals had an EHR system in all of their clinical settings (Ashish, 2009). Not surprisingly, the cost of an EHR system has been a major barrier to widespread adop- tion of this technology in most healthcare facilities. The HITECH Act sought to change that situation by providing each person in the United States with an EHR. In addition, a nationwide HIT infrastructure would be developed so that access to a person’s EHR will be readily available to every healthcare provider who treats the patient, no matter where the patient may be located at the time treatment is rendered. According to the Office of the National Coordinator for Health Information Technology (2015), three out of four hospitals now have at least a basic EHR with clinician notes, and for larger acute care hospitals, nearly 97% have EHR technology certified by USDHHS.

Definitions The HITECH Act includes some important definitions that anyone involved in nursing informatics should know:

• “Certified EHR Technology”: An EHR that meets specific governmental standards for the type of record involved, whether it is an ambulatory EHR used by office-based healthcare practitioners or an inpatient EHR used by hospitals. The specific standards that are to be met for any such EHRs are set forth in federal regulations.

• “Enterprise Integration”: The electronic linkage of healthcare providers, health plans, the government, and other interested parties to enable the electronic exchange and use of health information among all the components in the health care infrastructure.

• “Healthcare Provider”: Hospitals, skilled nursing facilities, nursing homes, long- term care facilities, home health agencies, hemodialysis centers, clinics, community mental health centers, ambulatory surgery centers, group practices, pharmacies and pharmacists, laboratories, physicians, and therapists, among others.

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• “Health Information Technology” (HIT): “Hardware, software, integrated technologies or related licenses, intellectual property, upgrades, or packaged solutions sold as services that are designed for or support the use by healthcare entities or patients for the electronic creation, maintenance, access, or exchange of health information.”

• “Qualified Electronic Health Record”: “An electronic record of health-related infor- mation on an individual.” A “qualified” EHR contains a patient’s demographic and clinical health information, including the medical history and a list of health problems, and is capable of providing support for clinical decisions and entry of physician orders. It must also have the capacity “to capture and query information relevant to health care quality” and “exchange electronic health information with, and integrate such information from other sources” (Readthestimulus.org, 2009, pp. 32–35).

Purposes The HITECH Act established the Office of the National Coordinator for Health Information Technology (ONC) within the USDHHS. The ONC is headed by the national coordina- tor, who is responsible for overseeing the development of a nationwide HIT infrastruc- ture that supports the use and exchange of information to achieve the following goals:

1. Improve healthcare quality by enhancing coordination of services between and among the various healthcare providers a patient may have, fostering more ap- propriate healthcare decisions at the time and place of delivery of services, and preventing medical errors and advancing the delivery of patient-centered care

2. Reduce the cost of health care by addressing inefficiencies, such as duplication of services within the healthcare delivery system, and by reducing the number of medical errors

3. Improve people’s health by promoting prevention, early detection, and man- agement of chronic diseases

4. Protect public health by fostering early detection and rapid response to infec- tious diseases, bioterrorism, and other situations that could have a widespread impact on the health status of many individuals

5. Facilitate clinical research 6. Reduce health disparities 7. Better secure patient health information

Improving healthcare quality has been an ongoing challenge in the United States. According to the Agency for Healthcare Research and Quality (AHRQ), quality health care is care that is “safe, timely, patient centered, efficient, and equitable” (AHRQ, 2009, p. 1). AHRQ, an agency within USDHHS, has been releasing a national healthcare quality report (NHQR) every year since 2003. Access the most recent report at www. ahrq.gov/research/findings/nhqrdr/index.html. The NHQR emphasized the need for HIT to support the goal of improving quality of care.

Providers need reliable information about their performance to guide improvement activities. Realistically, HIT infrastructure is needed to ensure that relevant data are collected regularly, systematically, and unobtrusively while protecting patient privacy and confidentiality . . . . Systems need to

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generate information that can be understood by end users and that are in- teroperable across different institutions’ data platforms... Quality improve- ment typically requires examining patterns of care across panels of patients rather than one patient at a time . . . Ideally, performance measures should be calculated automatically from health records in a format that can be eas- ily shared and compared across all providers involved with a patient’s care. (AHRQ, 2009, p. 13)

The prevalence of healthcare-associated infections serves as an excellent example of how use of EHR technology and a nationwide HIT infrastructure can play a signifi- cant role in addressing healthcare quality issues. According to the NHQR, “wound infections are a common occurrence following surgery, but hospitals can reduce the risk of these health care–associated infections by making sure patients receive an appropriate antibiotic within an hour before their procedures” (AHRQ, 2009, p. 110). The Centers for Medicare and Medicaid Services (CMS) already has the capacity to track Medicare patients who receive this prophylactic treatment and the rate of post- surgical wound infections for those patients who do and do not receive the treat- ment. Imagine being able to track this issue for all surgical patients and developing evidence-based care plans to ensure that all patients within the infrastructure receive the same quality of care. This is just one of many examples in which the end result of EHR adoption is better patient outcomes.

EHR technology also will make it easier for all providers involved in a patient’s care to readily access that patient’s complete and current healthcare record, thereby allowing providers to make well-informed, efficient, and effective decisions about a patient’s care at the time those decisions need to be made. This is of tremendous benefit to the patient and promotes a higher level of patient-centered care. It also al- lows effective coordination of care between and among all providers involved in the patient’s care, including doctors, nurses, therapists, nutritionists, hospitals, nursing homes, rehabilitation facilities, home health agencies, laboratories, and other diagnos- tic centers, thereby assuring the continuum of patient care.

Such an integrated system would have clear benefits for patients and providers alike. For example, imagine how much easier it would be for a patient with a rare form of cancer to obtain a second oncologist’s opinion before beginning a course of treatment. The patient’s complete record, including the results of numerous diagnostic tests conducted at multiple sites, such as blood tests, biopsies, radiographs, and scans, would be readily available to the second oncologist. Imagine how much easier it would be for a patient with end-stage renal disease, who is receiving outpatient hemo- dialysis several times a week, to receive appropriate treatment if he or she is suddenly hospitalized or would like to take a vacation out of state. Imagine how much easier it would be for nurses to complete a medication reconciliation for a newly admitted patient. The possibilities are endless, and the savings realized from enhancing quality, avoiding duplication of services, and streamlining delivery of patient care are obvious.

Reducing healthcare errors has been another ongoing challenge in the United States. Healthcare providers strive to meet the standard of care and avoid harm to patients. Patients have a right to receive appropriate care, but that does not always happen. Ten years ago, the Institute of Medicine’s Committee on the Quality of Health Care in America undertook a comprehensive literature review and summarized

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the results of more than 40 studies about healthcare errors in its seminal report, To Err Is Human: Building a Safer Health System (Institute of Medicine, 2000). That report concluded that approximately 44,000–98,000 people in the United States die each year as a result of healthcare errors. Many thousands more who do not die are seriously injured from such errors. In addition to the human pain and suffering as- sociated with healthcare errors, the monetary costs of these errors are substantial. Al- though some progress in reducing healthcare errors has been made since the release of To Err Is Human, substantial work remains to be done. It is anticipated that a nation- wide HIT infrastructure will contribute to a reduction in healthcare errors by provid- ing mechanisms to assist with the prevention of errors and to provide timely warnings of the possibility of a repetitive error that may affect many patients.

Containing and reducing healthcare costs in the United States, where more than $2 trillion is spent on health care each year (Keehan, Sisko, & Truffler, 2008), is another daunting challenge. Using EHR technology and a nationwide HIT infrastruc- ture to improve quality and reduce errors within the healthcare delivery system is one way to address this challenge. Imagine the billions of dollars that could be saved just by reducing the estimated 1.7 million cases of healthcare-associated infections con- tracted by patients in U.S. hospitals each year (AHRQ, 2009, p. 108).

Promoting prevention, early detection, and management of chronic diseases is another purpose of the HITECH Act. The delivery of health care in the United States traditionally has been based on a disease model rather than a wellness model. Having an EHR for each individual could help with the necessary transition as providers and their patients become more aware of the variables that positively or negatively impact health. The ability to identify appropriate choices to promote wellness and either pre- vent illness and injury or detect and manage chronic diseases sooner will be enhanced.

Chronic diseases are of major concern to this country, not only because of the impact they have on individuals, but also because of the tremendous cost associated with providing treatment for patients with these conditions. Adult-onset diabetes, for example, has reached epidemic proportions. A national HIT infrastructure will help providers better identify those patients who are at risk for developing this disease and provide treatment strategies to avoid it. For those patients who develop type 2 diabetes, their providers will be able to diagnose the condition much sooner and manage it more effectively because of the vast resources that a national HIT infrastructure can provide.

Improving public health is another purpose of the HITECH Act. The recent Zika virus challenge is illustrative of how a national HIT infrastructure can protect public health by fostering early detection and rapid response to infectious diseases, bioter- rorism, and other situations that could have a widespread impact on the health status of many individuals and groups.

The impact that a national HIT infrastructure will have on clinical research is self-evident. Once the infrastructure becomes operational, the amount of data that will become readily available for clinical research will increase exponentially com- pared to what is available today. The ability of researchers to conduct studies and provide clinicians with the most current evidence-based practice will be of tremen- dous benefit to patients everywhere.

Reducing health disparities is another purpose of the HITECH Act. According to the AHRQ (2013), “Health care disparities are differences or gaps in the care

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experienced by one population compared with another population” (p. 1). Detailed information about healthcare disparities can be found at the website for the Office of Minority Health and Health Disparities at www.cdc.gov/omhd. The AHRQ rou- tinely examines the issue of disparities in health care and reports its findings to the public. The National Healthcare Disparities Report of 2012 confirms that some Americans continue to receive inferior care because of such factors as race, ethnic- ity, and socioeconomic status (AHRQ, 2013). This report found disparities in the following areas:

• Across all dimensions of healthcare quality: Effectiveness, patient safety, timeli- ness, and patient centeredness

• Across all dimensions of access to care: Facilitators and barriers to care and health care utilization

• Across many levels and types of care: Preventive care, treatment of acute condi- tions, and management of chronic diseases

• Across many clinical conditions: Cancer, diabetes, end-stage renal disease, heart disease, HIV disease, mental health and substance abuse, and respiratory diseases

• Across many care settings: Primary care, home health care, hospice care, emer- gency department, hospitals, and nursing homes

• Within many subpopulations: Women, children, older adults, residents of rural areas, and individuals with disabilities and other special healthcare needs (AHRQ, 2013, pp. H1–H4)

All patients, regardless of race, ethnicity, or socioeconomic status, should receive care that is effective, safe, and timely. When the national HIT infrastructure contemplated by the HITECH Act is fully implemented, such disparities are bound to decrease. The ability to monitor for disparities and promote the delivery of appropriate care to all patients will be enhanced. Clinicians will be prompted to base their treatments on appropriate factors and avoid biased care.

Perhaps the most important task facing the national coordinator during the development and implementation of a nationwide HIT infrastructure is ensuring the security of the patient health information within that system. The ability to secure and protect confidential patient information has always been of paramount impor- tance to clinicians, who view this consideration as an ethical and legal obligation of practice. Patients value their privacy and they have a right to expect that their confi- dential health information will be properly safeguarded. Nurses have been complying with the regulatory requirements of HIPAA for years, and the HITECH Act has enhanced the security and privacy protections each patient has a right to expect under HIPAA. The specific changes are discussed in greater detail later in this chapter.

How a National HIT Infrastructure Is Being Developed Developing a national HIT infrastructure is an enormous and extremely complex undertaking that requires extensive financial, technologic, and human resources. The HITECH Act established the ONC, as noted earlier, and the USDHHS appointed a national coordinator, who is responsible for the development of the infrastructure.

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The HITECH Act also established two committees within the ONC: the HIT Policy Committee and the HIT Standards Committee.

The Policy Committee is responsible for making recommendations to the coordi- nator about how to implement the requirements of the HITECH Act, such as the tech- nologies to use in the infrastructure. The Policy Committee has a total of 20 members, one of whom must be a member from a labor organization and two of whom must be healthcare providers. At least one of the healthcare providers must be a physician. There is no specific requirement that a nurse be on the Policy Committee. A complete list of the Policy Committee members is available at www.healthit.hhs.gov.

The Standards Committee is responsible for recommending standards by which health information is to be electronically exchanged. The HITECH Act does not des- ignate the number of members to be on the committee; however, its members include healthcare providers, ancillary healthcare workers, consumers of health care, and others. Again, there is no specific requirement that a nurse be on the Standards Committee, and a complete list of the Standards Committee members is available at www.healthit.gov.

The HITECH Act also made provisions to include meaningful public input in the development of a national HIT infrastructure. Both the Policy Committee and the Standards Committee hold public meetings, and anyone interested in this process can participate. A schedule of meetings, committee agendas, and the transcripts of past meeting are posted at www.healthit.gov.

The national coordinator has several duties. He or she decides whether to endorse the recommendations of the Policy and Standards Committees and acts as a liaison among the committees and various federal agencies involved in the process of developing a national HIT infrastructure. He or she consults with these other agencies, including the National Institute of Standards and Technology, and along with those agencies updates the Federal HIT Strategic Plan (U.S. Department of Commerce, 2011). The initial Federal HIT Strategic Plan was published in June 2008, before the enactment of the HITECH Act, and the plan has been updated frequently to reflect evolving IT strategies. The most current plan can be accessed at www.healthit.gov/policy-researchers-implementers/ health-it-strategic-planning.

The HITECH Act also provides significant monetary incentives for providers who engaged in meaningful use of HIT. “Meaningful use” was defined as “using electronic health records (EHRs) in a meaningful manner, which includes, but is not limited to electronically capturing health information in a coded format, using that information to track key clinical conditions, communicating that information to help coordinate care, and initiating the reporting of clinical quality measures and public health infor- mation” (CMS, 2010, para. 3).

Monetary incentives are available to clinicians and facilities that implement EHR systems that meet the specific standards. Providers that fail to adopt such systems within a specified time frame may be subject to significant governmental penalties.

How the HITECH Act Changed HIPAA HIPAA Privacy and Security Rules Nurses have been complying with HIPAA for years. HIPAA was enacted by the federal government for several purposes, including better portability of health insurance as a worker moved from one job to another; deterrence of fraud, abuse, and waste within

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the healthcare delivery system; and simplification of the administrative functions associ- ated with the delivery of health care, such as reimbursement claims sent to Medicare and Medicaid. Simplification of administrative functions entailed the adoption of elec- tronic transactions that included sensitive healthcare information. To protect the privacy and security of health information, two sets of federal regulations were implemented. The Privacy Rule became effective in 2003, and the Security Rule became effective in 2005. Many practitioners that refer to HIPAA are not referring to the comprehensive federal statute enacted in 1996, but rather to the Privacy Rule and the Security Rule— that is, the federal regulations that were adopted years after HIPAA became law.

Under the Privacy Rule, patients have a right to expect privacy protections that limit the use and disclosure of their health information. Under the Security Rule, pro- viders are obligated to safeguard their patients’ health information from improper use or disclosure, maintain the integrity of the information, and ensure its availabil- ity. Both rules apply to protected health information (PHI), defined as any physical or mental health information created, received, or stored by a “covered entity” that can be used to identify an individual patient, regardless of the form of the health infor- mation (i.e., it can be electronic, handwritten, or verbal) (Legal Information Institute [LII], 2013). Covered entities include hospitals and other healthcare providers that transmit any health information electronically, as well as health insurance companies and healthcare clearinghouses (LII, 2013).

Clinicians have become very knowledgeable about the requirements of the Privacy and Security Rules. They are familiar with their obligations to protect patient infor- mation and the rights afforded to their patients under these regulations. Patients are entitled to a notice of privacy practices from their healthcare provider. Inpatients are entitled to opt out of the facility’s directory, thereby protecting disclosure of informa- tion that they are even a patient in the facility. Under certain circumstances, patients must authorize disclosure of their PHI before it can be released by the provider. Patients can request and obtain access to their own healthcare records and may request that corrections and additions be made to their records. Providers must consider a patient’s request to amend a healthcare record, but they are not required to make such an amendment if the request is unwarranted. Unauthorized access or use or any loss of healthcare information must be disclosed to any patient affected by the breach. Patients may request an accounting of anyone who accessed their health- care information, and the provider is required to provide that information in a timely manner. Finally, patients have a right to complain if they perceive that the privacy or security of their healthcare information has been compromised in some way. Such complaints can be made directly to the provider or to the Office of Civil Rights (OCR).

The OCR, which is part of the USDHHS, is responsible for enforcing HIPAA. It provides significant information and guidance to clinicians who must comply with the Privacy and Security Rules. It has been tracking complaints and investigating violations since 2003. Guidance and information about the complaint process and the violations that the OCR has handled are available on its website at www.healthit .gov/providers-professionals/model-notices-privacy-practices. As an example, one such violation involved a nurse practitioner who had privileges within a healthcare system. She accessed her ex-husband’s medical records without his authorization by using the system-wide EHRs. A complaint was filed and the OCR investigated the matter. The OCR resolved the complaint with the healthcare system. As part of this resolution,

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the healthcare system curtailed the nurse practitioner’s access to its EHRs and it re- quired her to undergo remedial training. In addition, it reported the nurse practitioner to her professional board (USDHHS, Office of Civil Rights, n.d.)

Many businesses are moving to enact a “bring your own device” (BYOD) policy for employees. This policy, which helps to streamline the lives of employees by maintaining personal and business information on one device, can also result in cost savings for the organization overall. BYOD is an issue, however, when dealing with PHI. Healthcare organizations typically do not encourage use of personal devices for professional matters, and in many instances they actually have policies in place for- bidding employees from using personal devices in the workplace. According to HIT Consultant (2013), approximately 50% of healthcare organizations report that per- sonal mobile devices can be used to access the Internet within their facilities but these devices are not given access to the organization’s network. Typically, only devices that are issued by the organization, secured, and routinely audited are able to access to the network. Nurses must exercise caution when bringing their personal devices into the healthcare organization to ensure that they are not violating any specifics of the BYOD policy.

Compliance with the Privacy and Security Rules is mandatory for all covered enti- ties, and the HITECH Act extends compliance with these requirements directly to other entities that are business associates of a covered entity. Requirements include designation of privacy and information security officials to protect health information and appropriate handling of any complaints. Sanctions must be imposed if a violation of HIPAA occurs. The Privacy and Security Rules also mandate that certain physical and technical safeguards be implemented for PHI, and they require entities to conduct periodic training of all staff to ensure compliance with these safeguards. Most entities adhere to industry standards and provide their personnel with yearly training. In ad- dition, entities are to conduct regular audits to ensure compliance, and any breaches in the privacy or security of PHI must be remedied immediately. It is important to avoid a security incident as such incidents trigger certain notification requirements and may be associated with monetary penalties.

The HITECH Act Enhanced HIPAA Protections The HITECH Act has had a significant impact on HIPAA’s Privacy and Security Rules in the following ways:

• USDHHS is to provide annual guidance about how to secure health information. • Notification requirements in the event of a breach in the security of health in-

formation have been enhanced. • HIPAA requirements now apply directly to any business associates of a covered

entity. • The rules that pertain to providing an accounting to patients who want to know

who accessed their health information have changed. • Enforcement of HIPAA has been strengthened.

These measures are being implemented to provide further assurance that health in- formation will be protected as the country transitions to a nationwide HIT infrastruc- ture. Several other organizations are also involved in the privacy and security aspects of the HIT infrastructure development (Box 8-1).

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Dee McGonigle, Kathleen Mastrian, and Nedra Farcus Several other organizations have been involved in HIPAA implementation. The American National Standards Institute (ANSI) X12N and Health Level Seven (HL7) standards organizations worked together to develop an electronic standard for claims attachments to recommend to USDHHS (Spencer & Bushman, 2006, para. 2). ANSI (n.d.) was founded in 1918 and has served as the coordinator of the U.S. voluntary standards and conformity assessment system (para. 1). ANSI provides a forum where the private and public sectors can cooperatively work together toward the development of voluntary national consensus standards and the re- lated compliance programs (para. 2). HL7 (n.d.) is one of several ANSI-accredited standards-developing organizations (SDOs) operating in the healthcare arena (para. 1). It states that its mission is to provide standards for interoperability that improve care delivery, optimize workflow, reduce ambiguity, and enhance knowledge trans- fer among all stakeholders, including healthcare providers, government agencies, the vendor community, fellow SDOs, and patients (para. 5).

HL7 was initially associated with HIPAA in 1996 through the creation of a claims attachments special interest group charged with standardizing the supplemental information needed to support healthcare insurance and other e-commerce transactions. The initial deliverable of this group was six claim attachments. This special interest group is currently known as the Attachment Special Interest Group. As the attachment projects continue, they are slated to include skilled nursing facilities, home health care, preauthorization, and referrals.

The “Level Seven” in HL7’s name refers to the highest level of the International Standards Organization’s (ISO’s) communications model for Open Systems Interconnection (OSI) application level. The application level addresses definition of the data to be exchanged, the timing of the interchange, and the communication of certain errors to the application. The seventh level supports such functions as security checks, participant identification, availability checks, exchange mechanism negotiations and, most importantly, data exchange structuring (HL7, n.d., para. 5).

The OSI was an attempt to standardize networking by the ISO. HL7 addresses the distinct requirements of the systems in use in hospitals and other facilities, is more concerned with application than the other levels, and consid- ers user authentication and privacy (Webopedia, 2008). The lower levels of OSI address hardware, software, and data reformatting.

HL7’s mission is supported through two separate groups, the Extensible Markup Language (XML) special interest group and the structured documents technical committee. The XML special interest group makes recommendations on use of XML standards for all of HL7’s platform- and vendor-independent healthcare specifications (HL7, n.d., para. 21). XML began as a simplified subset of the standard generalized markup language; its major purpose is to facilitate the exchange of structured data across different information systems, especially via the Internet. It is considered an extensible language because it permits users to define their own elements, thereby supporting customization to enable purpose- specific development. The structured documents technical committee supports the

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HL7 mission through development of structured document standards for health care (para. 21). HL7 also organizes, maintains, and sustains a repository for the vocabulary terms used in its messages to provide a shared, well-defined, and un- ambiguous knowledge base of the meaning of the data transferred.

ISO (2008a) is a network of the national standards institutes of 157 coun- tries. It includes one member per country, and a central secretariat in Geneva, Switzerland, coordinates the system (para. 1). ISO is a nongovernmental organi- zation; its members are not delegations of national governments (unlike the case in the United Nations system). Nevertheless, ISO occupies a special position between the public and private sectors. On the one hand, many of its member institutes are part of the governmental structure of their countries or are man- dated by their government. On the other hand, other members have their roots uniquely in the private sector, having been set up by national partnerships of industry associations (ISO, 2008a, para. 2).

This placement enables ISO to become a bridging organization where mem- bers can reach agreement on solutions that meet both the requirements of busi- ness and the broader needs of society, consumers, and users. These international agreements become standards that use the prefix ISO followed by the number of the standard. An example is the health informatics, health cards, numbering sys- tem, and registration procedure for issuer identifiers, ISO 20302:2006; it is designed to confirm, via a numbering system and registration procedure, the iden- tities of both the healthcare application provider and the health card holder so that information may be exchanged by using cards issued for healthcare service (ISO, 2008b, para. 12). ISO provides standards for interoperability that improve care delivery, optimize workflow, reduce ambiguity, and enhance knowledge transfer among all of its stakeholders, including healthcare providers, govern- ment agencies, the vendor community, fellow SDOs, and patients. The standards are used on a voluntary basis because ISO has no power to force their enactment.

All of the organizations described here have guidelines, standards, and rules to help healthcare entities collect, store, manipulate, dispose of, and exchange secure PHI. Many SDOs work to help develop standards. HIPAA guarantees the security and privacy of health information and curtails healthcare fraud and abuse while enforcing standards for health information.


Health care was not the only focus of U.S. legislative acts. One often sees “GLBA” and “SOX” when searching for information on HIPAA. The Gramm- Leach-Bliley Act (GLBA) is federal legislation in the United States to control how financial institutions handle the private information they collect from individuals. The Sarbanes-Oxley Act (SOX) is legislation put in place to protect shareholders and the public from deceptive accounting practices in organizations.

Privacy and data regulations are also being established around the world. See a map of the world depicting the laws of various countries at this website: www .dlapiperdataprotection.com/#handbook/world-map-section. It is quite evident that privacy and security have become global concerns.

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Avoiding security incidents has become a paramount concern for healthcare orga- nizations and providers. Providers must protect their information and prevent unau- thorized persons from accessing, using, disclosing, changing, or destroying a patient’s health information, or otherwise interfering with the operations of a health informa- tion system, such as an EHR. To facilitate a provider’s ability to do this, the HITECH Act requires USDHHS to provide annual guidance to secure health information. PHI can be secured or unsecured. PHI is considered unsecured if the provider does not follow the guidance provided by USDHHS for implementing technologies and meth- odologies that make PHI “unusable, unreadable, or indecipherable to unauthorized individuals” (USDHHS, 2009). PHI can be secured through encryption, shredding and other forms of complete destruction, or electronic media sanitation. Figure 8-3 depicts some common causes of PHI vulnerabilities.

The distinction between secured and unsecured PHI is important because providers that experience a breach in the privacy or security of their PHI must adhere to certain notification requirements depending on the type of PHI affected by the breach. The HITECH Act enhanced the breach notification requirements of HIPAA. If the PHI is unsecured, the provider must take certain steps to notify those individuals who have been affected. Providers can avoid these onerous breach notification requirements if the PHI is secured in accordance with the specifications of USDHHS.

A breach is considered discovered as soon as an employee other than the indi- vidual who committed the breach knows or should have known of the breach, such as unauthorized access or even an unsuccessful attempt to access information. For example, if a nurse knows that a colleague has accessed or attempted to access the record of a patient for whom the colleague is not providing care (e.g., the nurse prac- titioner who accessed her ex-husband’s EHR, as discussed previously), the nurse’s employer is deemed to have discovered the breach as soon as the nurse learned of it. The discovery of a breach triggers the beginning of the time frame during which the provider must fulfill the notification requirements. A provider must fulfill these

Figure 8-3 Vulnerability of Private Health Information

Uninentional Breaches: Unsecured Terminals

Loss of Devices Unsecured Passwords

Outside Attacks: Hackers

Worms, Viruses, Spam Theft

Intentional Breaches: Malicious Insiders Social Media Use

Unauthorized Access

Network Issues: Unencrypted Transmissions

Firewall Failure

PHI Vulnerabilities

How the HITECH Act Changed HIPAA 159

requirements within a reasonable period of time; under no circumstances may a pro- vider take more than 60 days from discovery of the breach. It is easy to understand why providers require their employees to report knowledge of such breaches imme- diately to the privacy or information security officer. A provider’s failure to adhere to the breach notification requirements could result in OCR sanctions, including mon- etary penalties.

Whenever a breach involves unsecured PHI, covered entities are responsible for alerting each affected individual by mail, or by e-mail if preferred by the individual. If there is insufficient contact information for 10 or more patients, the provider is required to place conspicuous postings on the home page of its website or in major print or broadcast media (without identifying patients). A toll-free telephone num- ber must be provided so that affected individuals can call for information about the breach. For breaches involving unsecured PHI of more than 500 individuals, a prom- inent media outlet must also be notified. Notice must be given to USDHHS as well, and USDHHS will post the information on its public website (USDHHS, 2009). It is easy to see why providers would want to avoid these requirements by making sure their PHI is secured. Having to post such notices undermines the trust that exists be- tween healthcare providers and the patients and communities they serve.

The HITECH Act has improved the privacy and security of patient health infor- mation by applying the requirements of HIPAA directly to the business associates of covered entities. In the past, it was up to the covered entity to enter into contracts with its business associates to ensure compliance with HIPAA. Now business associates are responsible for their own compliance. An example of such a business associate is a HIT company hired by a hospital to implement or upgrade an EHR system. The technology company has access to the hospital’s EHR system and must comply with the HIPAA Privacy and Security Rules, just as covered entities must comply with these rules. This includes being subject to enforcement by the OCR for any violations.

Existing accounting rules are enhanced under the HITECH Act, giving patients the right to access their EHR and receive an accounting of all disclosures. Before the HITECH Act, HIPAA regulations provided an exception to the accounting re- quirements. Providers and other covered entities were not required to include in the accounting any disclosures that were made to facilitate treatment/payment/operations— treatment of patients, the payment for services, or the operations of the entity— a provision commonly known as the “TPO exception.” This exception ended in January 2011 for providers that recently implemented new EHR systems. For those providers with EHR systems that were implemented before the HITECH Act, the TPO exception ended in January 2014. It is easy to understand why this exception has ended. As all providers implement comprehensive EHR systems, it will be very easy to generate an electronic record with an accounting of anyone who accessed a patient’s record.

Finally, the HITECH Act strengthens the enforcement of HIPAA. USDHHS can conduct audits, which will be even easier to accomplish once a nationwide HIT infrastructure is in place. In addition, stiffer civil monetary penalties (CMP) for violations of HIPAA became effective as soon as the HITECH Act became law in February 2009. CMPs are divided into three tiers. A Tier 1 CMP, in which the cov- ered entity had no reason to know of a violation, is $100 per incident, up to a cap of

160 CHAPTER 8 Legislative Aspects of Nursing Informatics: HITECH and HIPAA

$25,000 per year. A Tier 2 CMP, in which the covered entity had reasonable cause to know of a violation, is $1,000 per incident, up to a cap of $100,000 per year. A Tier 3 CMP, in which the covered entity engaged in willful neglect that resulted in a breach, is $10,000 per incident, up to a cap of $250,000 per year. In addition, the HITECH Act gives authority to impose an additional CMP of $50,000 to $1.5 million if the covered entity does not properly correct a violation. Criminal penalties also can be imposed when warranted. It is imperative that providers avoid these penalties.

Before enactment of the HITECH Act, the federal government alone enforced HIPAA. Now, state attorneys general can play a significant role in the enforcement and prosecution of HIPAA violations. Once the HITECH Act became law, state attorneys general were authorized to pursue civil claims for HIPAA violations and collect up to $25,000 plus attorneys’ fees. As of 2012, individuals who are damaged by such viola- tions became eligible to share in any monetary awards obtained by these state officials.

Implications for Nursing Practice Being Involved and Staying Informed The development and implementation of a nationwide EHR system holds great prom- ise for nursing practice and nursing informatics. The profession of nursing will benefit from the many enhancements such an infrastructure has to offer, including the abil- ity to improve the delivery of nursing care and the quality of that care, the ability to make more efficient and timely nursing care decisions for patients, the ability to avoid errors that may harm patients, and the ability to promote health and wellness for the patients whom nurses serve. On a broader scale, nurse researchers will have the abil- ity to more readily access data that can be used to continue to foster evidence-based practice. The possibilities seem endless. For those who devote their professional careers to nursing informatics or plan to do so, the opportunities abound. Much work remains to be done as this country transitions to a nationwide HIT infrastruc- ture, and moves beyond meaningful use requirements.

All nurses need to be engaged in this process, whether they treat patients, are managers within healthcare organizations, teach, develop computer programs, or help create institutional or governmental policies. Nurses, as the end users of de- veloping technologies, cannot afford to be left behind in these exciting times. Their voices must be heard, whether it is within the facility where they work as changes to the EHR system are contemplated, or whether it is in the public policy arena. How often are nurses the last to know that a new EHR system has been adopted by their hospital? How many times have nurses been trained to use a system that would have benefited from their input before it was implemented or even purchased? Nurses often are not invited to the table when entities make decisions about informatics, so they should not be afraid to ask to be included, whether it is to be heard within the workplace or within the governmental agencies that are overseeing the changes that are taking place.

Even nurses who do not get involved in this process need to stay current with the rapid changes that are taking place. Information about federal initiatives is available from the ONC and the OCR. Both offices are housed within USDHHS and are

Implications for Nursing Practice 161


Glenn Johnson and Jeff Swain New opportunities to share information via social networks have grabbed the headlines. Since their inception in 2004, the growth in popularity of social net- working tools, such as Facebook (www.facebook.com) and Twitter (www.twitter. com), has been phenomenal. What makes these sites so attractive? Web-based applications, such as Facebook, allow users to connect and share information in ways that were not previously possible. Users develop online profiles that contain information they select to share with others. Using simple online utilities, users can easily connect and share their profiles, communicating with friends over the Internet. Virtual groups of users with similar profiles may be created, connecting users with others who have similar interests. Twitter, a micro-blogging platform, allows users to create interpersonal networks for socializing, support, and infor- mation sharing. The power of such tools as Twitter lies in their being lightweight, their limiting of updates to 140 or fewer characters, and their convenience—us- ers can update their status from any device that has an Internet connection or text messaging capabilities.

The popularity of social and mobile networking applications is one indica- tion of how new Web-based technologies are changing communication pref- erences. The Web is no longer a destination place, but instead has become a vehicle of communication where individuals use application software (“apps”), which are installed or downloaded, to connect with others. Individuals act as their own portal and can connect from anywhere with their various commu- nities. This makes it difficult to separate out various communities and social networks. Where once it was relatively easy to separate work relationships from friends and family, networked communities tend to overlap, blurring the boundaries between them. The phenomenon of overlapping networks means

excellent resources for additional information about the HITECH Act and HIPAA. Regulations to implement the HITECH Act and enhance the HIPAA protections re- quired by it are being proposed and adopted at a rapid pace. See www.healthit.gov to access the most current information.

Protecting Yourself Nurses who strive to protect the privacy and security of patient information are pro- tecting themselves from ethical lapses and violations of law. The American Nurses As- sociation’s (ANA’s) Code of Ethics for Nurses with Interpretive Statements mandates that nurses protect a patient’s rights to privacy and confidentiality.

Associated with the right to privacy, the nurse has a duty to maintain confidentiality of all patient information. Nurses who engage with social media need to be especially cognizant of the potential for breaching the confidentiality of patient information. Box 8-2 provides more information related to nurses’ use of social media. Refer also to

162 CHAPTER 8 Legislative Aspects of Nursing Informatics: HITECH and HIPAA

that the unintended audience is almost always greater than the intended one. A status update that may be construed as harmless and funny to one’s friends could be taken an entirely different way by family or colleagues. This is not to say networked communities are harmful or bad. Indeed, the benefits of such communities far exceed their negatives. However, the immediacy and the per- manence of the updates shared mean that the user must think about the im- pact beyond the intended audience in ways never before required (Johnson & Swain, 2011).

Nurses and other healthcare workers who use social media must be aware that the overlapping of networks may unintentionally create privacy and con- fidentiality breaches. Even when patients are not identified by name, general sharing of information or venting about a difficult day may constitute a privacy breach. The National Council of State Boards of Nursing (NCSBN, 2011) has collaborated with the ANA to develop specific guidelines for the use of social media by nurses. See www.ncsbn.org/Social_Media.pdf to read a white paper discussing common misconceptions about social media, consequences for breaching confidentiality using social media, guidelines for appropriate use of social media, and case scenarios with discussion.


Johnson, G., & Swain, J. (2011). Professional development and collaboration tools. In D. McGonigle & K. Mastrian (Eds.), Nursing informatics and the foundation of knowledge (2nd ed., pp. 185–195). Burlington, MA: Jones & Bartlett Learning.

National Council of State Boards of Nursing. (2011). White paper: A nurse’s guide to the use of social media. Retrieved from https://www.ncsbn.org/Social_Media.pdf

the ethical use of social media discussed in Chapter 5. The patient’s well-being could be jeopardized and the fundamental trust between patient and nurse destroyed by unnecessary access to data or by the inappropriate disclosure of identifiable patient information. The rights, well-being, and safety of the individual patient should be the primary factors in arriving at any professional judgment concerning the disposition of confidential information received from or about the patient, whether oral, written, or electronic. The standard of nursing practice and the nurse’s responsibility to provide quality care require that relevant data be shared with only those members of the health- care team who have a need to know that information. Only information pertinent to a patient’s treatment and welfare should be disclosed, and only to those directly involved with the patient’s care. When using electronic communications, special effort should be made to maintain data security (ANA, 2010, p. 6).

The similarities between these ethical obligations and the legal requirements of HIPAA and other federal and state privacy and confidentiality laws are readily ap- parent to nurses. By complying with their ethical code, nurses were complying with the Privacy and Security Rules before they were required to do so. Since the adoption of the HIPAA Privacy and Security Rules, and now with the passage of the HITECH

Implications for Nursing Practice 163

Act, it is more important than ever for nurses to understand their obligations in this area and avoid the pitfalls of violations.

In addition to the sanctions imposed by the OCR, violations can lead to disci- plinary actions by employers and professional licensing boards, as well as litigation. Such actions can have a serious negative impact on the nurse’s reputation and finan- cial well-being. If a nurse is terminated for invading a patient’s privacy or breaching the confidentiality of a patient’s information, some state laws require reporting the information to prospective employers of the nurse; other laws require reporting to the State Board of Nursing. State Boards of Nursing have the authority to publicly discipline a nurse who has engaged in professional misconduct by invading a patient’s privacy, which includes inappropriately accessing a patient’s EHR, and breaching confidentiality of patient information, such as allowing or tolerating unauthorized access to a patient’s EHR. These types of situations can also cause patients to file complaints with the OCR and lawsuits against the offenders. Nurses must be ever mindful of their obligations to report a breach in the privacy or security of PHI to their employers, even if it entails reporting a colleague.

Finally, some view the EHR as a convenient method for employers to monitor the performance of its nurses. Clearly, an EHR system provides a wealth of information that can be, and often is required to be, monitored. Audits are required to make sure that no breaches in the system’s security occur. Audits are not necessarily required to determine, for example, which nurses are failing to complete the hospital’s docu- mentation requirements in a timely fashion, which nurses are improperly altering (attempting to correct) the record, or which nurses are dispensing more pain medica- tion than the average. Nurses have been challenged by employers who allege failure to document, improper or false documentation, and suspected diversion of narcotics. These types of situations are unsettling and may be on the rise as more providers adopt or augment EHR systems. Thus it behooves every nurse who works with such a system to obtain proper training and to know the policies and procedures that pertain to its use.

Social media can and should be used in an appropriate manner by profession- als to educate and promote health behaviors in the clients they serve, communicate with clients if they choose this method of communication, and network with other professionals by sharing information (deidentified) and knowledge. As Gagnon and Sabus (2015) suggest, “the reach of social media for health and wellness presents exciting opportunities for the health care professional with a well-executed social media presence. Social media give health care providers a far-reaching platform on which to contribute high-quality online content and amplify positive and accurate health care information and messages. It also provides a forum for correcting misin- formation and addressing misconceptions” (p. 410). They advocated for healthcare professionals to practice digital professionalism, and for social media use to be one of the professional competencies for health professional education. Bazan (2015) sug- gested that social media can be used to consult with other healthcare providers, such as in a professional Facebook group using direct private messaging between the two providers, but cautioned that posting to the main social site cannot contain any hint of PHI. He also shared information about a progressive practice that communicates

164 CHAPTER 8 Legislative Aspects of Nursing Informatics: HITECH and HIPAA

with patients via private messaging on Facebook. Remember that everything you do electronically leaves a digital footprint! Proceed with caution and be certain that your digital interactions comply completely with professional ethics, laws, and organiza- tional policies.

Future Regulations CMS recently released new legislation, the Medicare Access and CHIP Reau- thorization Act of 2015 (MACRA; USDHHS, 2016). Although this legislation primarily affects provider payments, all members of the healthcare team will have a hand in ensuring quality care. The final implementation guidelines have yet to be released, but this new legislation is expected to replace the former CMS meaningful use guidelines. For more information on this program, refer to the chapter on Workflow and Meaningful Use.

The U.S. Food and Drug Administration (FDA), a division of USDHHS, is responsible for regulating medical devices to ensure public safety. In 2015, the FDA released a guidance document for manufacturers, developers, and FDA staff related to mobile medical applications. At the current time, the most common types of these applications, or apps, are not regulated by the FDA because they are not defined as medical devices. An app is defined as a medical device and may be subject to regula- tion by the FDA if “the intended use of a mobile app is for the diagnosis of a disease or other conditions, or the cure, mitigation, treatment, or prevention of disease, or if it is intended to affect the structure or function of the body of man” (FDA, 2015, p. 8). The guidance document also provides a list of examples of apps that are not currently viewed as medical devices, such as apps that help users organize personal medical data, track fitness, or self-manage a disease. If, however, the mobile app is an accessory to a regulated medical device, then it is also considered a medical device and is subject to FDA oversight. We need to be aware that as these mobile apps be- come more sophisticated in the future, they may indeed be subject to more stringent oversight by the FDA to ensure consumer safety.

Summary The HITECH Act and the HIPAA Privacy and Security Rules are intended to enhance the rights of individuals. These laws provide patients with greater access and control over their PHI. They can control its uses, dissemination, and disclosures. Covered entities must establish not only a required level of security for PHI, but also sanctions for employees who violate the organization’s privacy policies and administrative pro- cesses for responding to patient requests regarding their information. Therefore, they must be able to track the PHI, note access from the perspective of which information was accessed and by whom, and identify any disclosures. Finally, readers should rec- ognize that there is global awareness of the need for privacy protections for personal information or PHI. Over the next few years, international efforts will accelerate, en- hancing international data exchange.

Summary 165

References Agency for Healthcare Research and Quality (AHRQ). (2009). National healthcare quality report

(NHQR) 2009: Crossing the quality chasm: A new healthcare system for the 21st century. Washington, DC: National Academies Press.

Agency for Healthcare Research and Quality (AHRQ). (2013). 2012 national healthcare disparities report. Retrieved from https://archive.ahrq.gov/research/findings/nhqrdr/nhdr12

American National Standards Institute (ANSI). (n.d.). ANSI: A historical overview. Retrieved from http://ansi.org/about_ansi/introduction/history.aspx?menuid=1

American Nurses Association (ANA). (2010). Code of ethics for nurses with interpretive statements. Retrieved from http://www.nursingworld.org/codeofethics

Ashish, J. (2009). Use of electronic health records in U.S. hospitals. New England Journal of Medicine, 360(16), 1628–1638.

Bazan, J. (2015). HIPAA in the age of social media. Optometry Times, 7(2), 16–18. Centers for Medicare and Medicaid Services (CMS). (2010). Meaningful use. Retrieved from

https://www.cms.gov/EHRIncentivePrograms/30_Meaningful_Use.asp Centers for Medicare and Medicaid Services (CMS). (2014). Introduction to administrative

simplification. Retrieved from https://www.cms.gov/eHealth/downloads/eHealthU _IntroAdminSimp.pdf

Egger, E. (2000). HIPAA offers hospitals the good, the bad, and the ugly. Health Care Strategic Management, 18(4), 1, 21–23.

Food and Drug Administration (FDA). (2015). Mobile medical applications. Guidance for industry and food and drug administration staff. Retrieved from http://www.fda.gov/downloads /MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/UCM263366.pdf

Gagnon, K., & Sabus, C. (2015). Professionalism in a Digital Age: Opportunities and considerations for using social media in health care. Physical Therapy, 95(3), 406–414. doi:10.2522/ptj.20130227

Guidance on compliance with HIPAA transactions and code sets. (2003). Retrieved from https://www.ihs.gov/hipaa/documents/guidance-final.pdf


1. One of the largest problems with healthcare information security has always been inappropriate use by authorized users. How do HIPAA and the HITECH Act help to curb this problem?

2. How do you envision Health Level Seven, HIPAA, and the HITECH Act evolv- ing in the next decade?

3. If you were the privacy officer in your organization, how would you address the following? a. Tracking each point of access of the patient’s database, including who

entered the data. b. Encouraging employees to report privacy and security breaches. c. The healthcare professionals are using smartphones, iPads, and other

mobile devices. How do you address privacy when data can literally walk out of your setting?

d. You observe one of the healthcare professionals using his smartphone to take pictures of a patient. He sees you and says, in front of the patient, “I am not capturing her face!” How do you respond to this situation?

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Health Level Seven (HL7). (n.d.). What is HL7? Retrieved from http://www.hl7.org Hellerstein, D. (1999). HIPAA’s impact on healthcare. Health Management Technology, 20(3), 10. Hellerstein, D. (2000). HIPAA and health information privacy rules: Almost there. Health

Management Technology, 21(4), 26. HIT Consultant. (2013). 3 Do’s and don’ts of effective HIPAA compliance for BYOD & mHealth.

Retrieved from http://hitconsultant.net/2013/06/11/3-dos-and-donts-of-effective-hipaa -compliance-for-byod-mhealth

Institute of Medicine. (2000). To err is human: Building a safer health system. Washington, DC: National Academies Press.

International Standards Organization (ISO). (2008a). About ISO. Retrieved from http://www .iso.org/iso/about.htm

International Standards Organization (ISO). (2008b). Health informatics. Retrieved from http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=35376

Keehan, S., Sisko, A.Q., & Truffler, C. (2008). Health spending projections through 2017: The baby-boom generation is coming to Medicare. Health Affairs, 27(2), 145–155.

Legal Information Institute (LII). (2013). 45 CFR 160.103—Definitions. Retrieved from https:// www.law.cornell.edu/cfr/text/45/160.103

Leyva, C., & Leyva, D. (2011). HITECH Act. Retrieved from http://www.hipaasurvivalguide .com/hitech-act-text.php

Office of the National Coordinator for Health Information Technology. (2015, June). Non-federal acute care hospital electronic health record adoption, Health IT Quick-Stat #47. Retrieved from http://dashboard.healthit.gov/quickstats/pages/FIG-Hospital-EHR-Adoption.php

Privacy Commissioner. (2007). Health information privacy code. Retrieved from http://www .privacy.org.nz/health-information-privacy-code

Readthestimulus.org. (2009). Committee print, January 16, 2009. Retrieved from http:// govinfo.sla.org/2009/02/08/readthestimulusorg

Savage, M. (2006). Security news: Perfect HIPAA security impossible, experts say. TechTarget. Retrieved from http://searchsecurity.techtarget.com/originalContent/0,289142,sid14 _gci1268986,00.html

Spencer, J., & Bushman, M. (2006). HIPAAdvisory: The next HIPAA frontier: Claims attachments. Retrieved from http://www.worldprivacyforum.org/wp-content /uploads/2006/02/WPF_HHS_NPRM_HIPAAclaims_fs.pdf

U.S. Department of Commerce. (2011). National Institute of Standards and Technology (NIST). Retrieved from http://www.nist.gov/index.html

U.S. Department of Health and Human Services (HHS). (2002). Federal Register, Part V, Department of Health and Human Services: Standards for privacy of individually identifiable health information; Final rule. Retrieved from https://aspe.hhs.gov/report/standards-privacy -individually-identifiable-health-information-final-privacy-rule-preamble

U.S. Department of Health and Human Services (HHS). (2009). Federal Register: 45 CFR Parts 160 and 164: Breach Notification for Unsecured Protected Health Information; Interim.

U.S. Department of Health and Human Services (HHS). (2015). The privacy act. Retrieved from http://www.hhs.gov/foia/privacy

U.S. Department of Health and Human Services (HHS). (2016). Administration takes first step to implement legislation modernizing how Medicare pays physicians for quality. Retrieved from http://www.hhs.gov/about/news/2016/04/27/administration-takes-first-step-implement -legislation-modernizing-how-medicare-pays-physicians.html

U.S. Department of Health and Human Services (HHS), Office of Civil Rights (OCR). (n.d.). Health information privacy: All case examples. Retrieved from http://www.hhs.gov/ocr/privacy /hipaa/enforcement/examples/allcases.html#case1

Webopedia. (2008). The 7 layers of the OSI model. Retrieved from http://www.webopedia.com /quick_ref/OSI_Layers.asp

References 167

section III

Nursing Informatics Administrative Applications: Precare and Care Support Chapter 9 Systems Development Life Cycle: Nursing Informatics and Organizational

Decision Making

Chapter 10 Administrative Information Systems

Chapter 11 The Human–Technology Interface

Chapter 12 Electronic Security

Chapter 13 Workflow and Beyond Meaningful Use

Nursing informatics (NI) and information technology (IT) have invaded nursing, and some nurses are happy with the capabilities afforded by this specialty. Others, however, remain convinced that the changes wrought by IT are nothing more than a nuisance. In the past, nursing administrators have found the implementation of technology tools to be an expensive venture with minimal rewards. This disap- pointment is likely related to their lack of knowledge about NI, which caused nursing administrators to listen to vendors or other colleagues; in essence, it was decision making based on limited and biased information. There were at least two reasons for the experience of limited rewards. First, nurses were rarely included in the testing and implementation of products designed for nurses and nursing tasks. Second, the new products they purchased had to interface with old, legacy sys- tems that were not at all compatible or seemed compatible until the glitches arose. These glitches caused frustration for clinicians and administrators alike. They purchased tools that should have made the nurses happy, but instead all they did was grumble.

The good news is that approaches have changed as a result of the difficult les- sons learned from the early forays into technology tools. Nursing personnel are involved both at the agency level and at the vendor level, in the decision-making process and development of new systems and products charged with enhancing the practice of nursing. Older legacy systems are being replaced with newer systems that have more capacity to interface with other systems. Nurses and administrators have become more astute in the realm of NI, but there is still a long way to go. The Systems Development Life Cycle: Nursing Informatics and Organizational Deci- sion Making chapter introduces the system development life cycle, which is used to make important and appropriate organizational decisions for technology adoption.

Administrators need information systems that facilitate their administrative role, and they particularly need systems that provide financial, risk management, quality assurance, human resources, payroll, patient registration, acuity, communi- cation, and scheduling functions. The administrator must be open to learning about all of the tools available. One of the most important tasks that an administrator can oversee and engage in is data mining, or the extraction of data and informa- tion from big data, sizeable datasets that have been collected and warehoused. Data mining helps to identify patterns in aggregate data, gain insights, and ultimately discover and generate knowledge applicable to nursing science. To take advantage of these benefits, nursing administrators must become astute informaticists— knowledge workers who harness the information and knowledge at their fingertips to facilitate the practice of their clinicians, improve patient care, and advance the science of nursing.

170 SeCtIoN III Nursing Informatics Administrative Applications: Precare and Care Support

Clinical information systems (CIS) have traditionally been designed for use by one unit or department within an institution. However, because clinicians work- ing in other areas of the organization need access to this information, these data and information are generally used by more than one area. The new initiatives arising with the integration of the electronic health record place institutions in the position of striving to manage their CIS through the electronic health record. Currently, there are many CISs, including nursing, laboratory, pharmacy, moni- toring, and order entry, plus additional ancillary systems to meet the individual institutions’ needs. The Administrative Information Systems chapter provides an overview of administrative information systems and helps the reader to understand the powerful data aggregation and data mining tools afforded by these systems.

The Human–Technology Interface chapter discusses the need to improve qual- ity and safety outcomes significantly in the United States. Through the use of IT, the designs for human–technology interfaces can be radically improved so that the tech- nology better fits both human and task requirements. A number of useful tools are currently available for the analysis, design, and evaluation phases of development life cycles and should be used routinely by informatics professionals to ensure that tech- nology better fits both task and user requirements. In this chapter, the authors stress that the focus on interface improvement using these tools has dramatically improved patient safety in a specific area of health care: anesthesiology. With increased atten- tion from informatics professionals and engineers, the same kinds of improvements are being made in other areas. This human–technology interface is a crucial area if the theories, architectures, and tools provided by the building block sciences are to be implemented.

Each organization must determine who can access and use its information systems and provide robust tools for securing information in a networked environment. The Electronic Security chapter addresses the important safeguards for protecting information. As new technologies designed to improve inter- professional collaboration and enhance patient care are adopted, barriers to implementation and resistance by practitioners to change are frequently encoun- tered. The Workflow and Beyond Meaningful Use chapter provides insights into clinical workflow analysis and provides advice on improving efficiency and effec- tiveness while reviewing what we have learned as we tried to achieve meaningful use of caring technologies.

Pause to reflect on the Foundation of Knowledge model (Figure III-1) and its relationship to both personal and organizational knowledge management. Consider that organizational decision making must be driven by appropriate

SeCtIoN III Nursing Informatics Administrative Applications: Precare and Care Support 171

information and knowledge developed in the organization and applied with wisdom. Equally important to adopting technology within an organization is the consideration of the knowledge base and knowledge capabilities of the individu- als within that organization. Administrators must use the system development life cycle wisely and carefully consider organizational workflow as they adopt NI technology for meaningful use.

The reader of this section is challenged to ask the following questions: (1) How can I apply the knowledge gained from my practice setting to benefit my patients and enhance my practice?; (2) How can I help my colleagues and patients understand and use the current technology that is available?; and (3) How can I use my wisdom to create the theories, tools, and knowledge of the future?

172 SeCtIoN III Nursing Informatics Administrative Applications: Precare and Care Support

KA - Knowledge acquisition KD - Knowledge dissemination KG - Knowledge generation KP - Knowledge processing



Information Information



Bytes Bytes



Bits Data



Bytes Bytes Bits

Bits Data Information






Figure III-1 Foundation of Knowledge Model Designed by Alicia Mastrian

SeCtIoN III Nursing Informatics Administrative Applications: Precare and Care Support 173

Key terms » Chief information

officer » Computer-aided

software engineering

» Dynamic system development method

» End users

» Health management information system

» Hospital informa- tion system

» Information technology

» Integration » Interoperability » Iteration

» Milestones » MoSCoW » Object-oriented

systems development

» Open source software

» Prototype

» Rapid application development

» Rapid prototyping » Repository » Systems develop-

ment life cycle » TELOS strategy » Waterfall model

1. Describe the systems development life cycle (SDLC). 2. Explore selected approaches to SDLC. 3. Assess interoperability and its importance in

addressing and meeting the challenges of imple- menting the HITECH Act in health care.

4. Reflect on the past to move forward into the future to determine how new systems will be developed, integrated, and made interoperable in health care.


Introduction The following case scenario demonstrates the need to have all of the stake- holders involved from the beginning to the end of the systems development life cycle (SDLC). Creating the right team to manage the development is key. Various methodologies have been developed to guide this process. This chapter reviews the following approaches to SDLC: waterfall, rapid pro- totyping or rapid application development (RAD), object-oriented system development (OOSD), and dynamic system development method (DSDM). When reading about each approach, think about the case scenario and how important it is to understand the specific situational needs and the various methodologies for bringing a system to life. As in this case, it is generally necessary or beneficial to use a hybrid approach that blends two or more models for a robust development process.

As the case demonstrates, the process of developing systems or SDLC is an ongoing development with a life cycle. The first step in developing a system is to understand the problems or business needs. It is followed by understanding the solution or how to address those needs; developing a plan; implementing the plan; evaluating the implementation; and, finally, mainte- nance, review, and destruction. If the system needs major upgrading outside of the scope of the maintenance phase, if it needs to be replaced because of technological advances, or if the business needs change, a new project is launched, the old system is destroyed, and the life cycle begins anew.

SDLC is a way to deliver efficient and effective information systems (ISs) that fit with the strategic business plan of an organization. The busi- ness plan stems from the mission of the organization. In the world of health care, its development includes a needs assessment for the entire or- ganization, which should include outreach linkages (as seen in the case sce- nario) and partnerships and merged or shared functions. The organization’s participating physicians and other ancillary professionals and their offices are included in thorough needs assessments. When developing a strategic plan, the design must take into account the existence of the organization

Systems Development Life Cycle: Nursing Informatics and organizational Decision Making Dee McGonigle and Kathleen Mastrian



within the larger healthcare delivery system and assess the various factors outside of the organization itself, including technological, legislative, and environmental issues that impact the organization. The plan must identify the needs of the organization as a whole and propose solutions to meet those needs or a way to address the issues.

SDLC can occur within an organization, be outsourced, or be a blend of the two approaches. With outsourcing, the team hires an outside organization to carry out all or some of the development. Developing systems that truly meet business needs is not an easy task and is quite complex. Therefore, it is common to run over budget and miss milestones. When reading this chapter, reflect on the case scenario and in general the challenges teams face when developing systems.


Envision two large healthcare facilities that merge resources to better serve their community. This merger is called the Wellness Alliance, and its mission is to establish and manage community health programming that addresses the health needs of the rural, underserved populations in the area. The Well- ness Alliance would like to establish pilot clinical sites in five rural areas to promote access and provide health care to these underserved consumers. Each clinical site will have a full-time program manager and three part-time employees (a secretary, a nurse, and a doctor). Each program manager will report to the wellness program coordinator, a newly created position within the Wellness Alliance.

Because you are a community health nurse with extensive experience, you have been appointed as the wellness program coordinator. Your directive is to establish these clinical sites within 3 months and report back in 6 months as to the following: (1) community health programs offered, (2) level of community involvement in outreach health programs and clinical site–based programming, (3) consumer visits made to the clinical site, and (4) personnel performance.

You are excited and challenged, but soon reality sets in: You know that you have five different sites with five different program managers. You need some way to gather the vital information from each of them in a similar manner so that the data are meaningful and useful to you as you develop your reports and evaluate the strengths and weaknesses of the pilot project. You know that you need a system that will handle all of the pilot project’s information needs.

Your first stop is the chief information officer of the health system, a nurse informaticist. You

know her from the health management information system mini-seminar that she led. After explaining your needs, you share with her the constraint that this system must be in place in 3 months when the sites are up and running before you make your report. When she begins to ask questions, you realize that you do not know the answers. All you know is that you must be able to report on which community health programs were offered, track the level of community involvement in outreach health programs and clinical site–based programming, monitor consumer visits made to the clinical site, and monitor the performance of site personnel. You know that you want accessible, real-time tracking, but as far as programming and clinical site–related activities are concerned, you do not have a precise description of either the process or procedures that will be involved in implementing the pilot, or the means by which they will gather and enter data.

The chief information officer requires that you and each program manager remain involved in the development process. She assigns an information technology (IT) analyst to work with you and your team in the development of a system that will meet your current needs. After the first meeting, your head is spinning: The IT analyst has challenged your team not only to work out the process for your immedi- ate needs, but also to envision what your needs will be in the future. At the next meeting, you tell the analyst that your team does not feel comfortable trying to map everything out at this point. He states that there are several ways to go about building the system and software by using the SDLC. Noticing the blank look on everyone’s faces, he explains that the SDLC is a series of actions used to develop an

176 CHAPteR 9 Systems Development Life Cycle: Nursing Informatics and Organizational Decision Making

IS. The SDLC is similar to the nursing process, in which the nurse must assess, diagnose, plan, imple- ment, evaluate, and revise. If the plan developed in this way does not meet the patient’s need or if a new problem arises, the nurse either revises and updates the plan or starts anew. Likewise, you will plan, analyze, design, implement, operate, support, and secure the proposed community health system.

The SDLC is an iterative process—a conceptual model that is used in project management describ- ing the phases involved in building or developing an IS. It moves from assessing feasibility or project initiation, to design analysis, to system specifica- tion, to programming, to testing, to implementa- tion, to maintenance, and to destruction—literally from beginning to end. As the IT analyst describes this process, once again he sees puzzled looks. He quickly states that even the destruction of the system is planned—that is, how it will be retired, broken down, and replaced with a new system. Even during upgrades, destruction tactics can be invoked to secure the data and even decide if servers are to be disposed of or repurposed. The security people will tell you that this is their phase, where they make sure that any sensitive information is properly handled and decide whether the data are to be securely and safely archived or destroyed.

After reviewing all of the possible methods and helping you to conduct your feasibility and business study, the analyst chooses the DSDM. This SDLC model was chosen because it works well when the time span is short and the requirements are fluctuating and mainly unknown at the outset. The IT analyst explains that this model works well on tight schedules and is a highly iterative and incremental approach that stresses continuous user input and involvement. As part of this highly iterative process, the team will revisit and loop through the same development activities numerous times; this repetitive examina- tion provides ever-increasing levels of detail, thereby improving accuracy. The analyst explains that you will use a mockup of the hospital information system (HIS) and design for what is known; you will then create your own mini-system that will interface with the HIS. Because time is short, the analysis, design, and development phases will occur simultaneously while you are formulating and revising your specific requirements through the iterative process so that they can be integrated into the system.

The functional model iteration phase will be completed in 2 weeks based on the information that you have given to the analyst. At that time, the prototype will be reviewed by the team. The IT analyst tells you to expect at least two or more iterations of the prototype based on your input. You should end with software that provides some key capabilities. Design and testing will occur in the design and build iteration phase and continue until the system is ready for implementation, the final phase. This DSDM should work well because any previous phase can be revisited and reworked through its iterative process.

One month into the SDLC process, the IT analyst tells the team that he will be leaving his position at Wellness Alliance. He introduces his replacement. She is new to Wellness Alliance and is eager to work with the team. The initial IT analyst will be there 1 more week to help the new analyst with the transi- tion. When he explains that you are working through DSDM, she looks a bit panicky and states that she has never used this approach. She has used the waterfall, prototyping, iterative enhancement, spiral, and object- oriented methodologies—but never the DSDM. From what she heard, DSDM is new and often runs amok because of the lack of understanding as to how to implement it appropriately. After 1 week on the project, the new IT analyst believes that this approach was not the best choice. As the leader of this SDLC, she is growing concerned about having a product ready at the point when the clinical sites open. She might combine another method to create a hybrid approach with which she would be more comfortable; she is thinking out loud and has everyone very nervous.

The IT analyst reviews the equipment that has arrived for the sites and is excited to learn that the computers were ordered from Apple. They will be powerful and versatile enough for your needs.

Two months after the opening of the clinical sites, you, as the wellness program coordinator are still tweaking the system with the help of the IT analyst. It is hard to believe how quickly the team was able to get a robust system in place. As you think back on the process, it seems so long ago that you reviewed the HIS for deficiencies and screen shots. You reexamined your requirements and watched them come to life through five prototype iterations and constant security updates. You trained your personnel on its use, tested its performance, and

Introduction 177

made final adjustments before implementation. Your own stand-alone system that met your needs was installed and fully operational on the Friday before you opened the clinic doors on Monday,

1 day ahead of schedule. You are continuing to evaluate and modify the system, but that is how the SDLC works: It is never finished, but rather constantly evolving.







Figure 9-1 Waterfall Phases

Waterfall Model The waterfall model is one of the oldest methods and literally depicts a waterfall ef- fect—that is, the output from each previous phase flows into or becomes the initial input for the next phase. This model is a sequential development process in that there is one pass through each component activity from conception or feasibility through implementation in a linear order. The deliverables for each phase result from the in- puts and any additional information that is gathered. There is minimal or no iterative development where one takes advantage of what was learned during the development of earlier deliverables. Many projects are broken down into six phases (Figure 9-1), especially small- to medium-size projects.

Feasibility As the term implies, the feasibility study is used to determine whether the project should be initiated and supported. This study should generate a project plan and estimated budget for the SDLC phases. Often, the teLoS strategy—technological and systems, economic, legal, operational, and schedule feasibility—is followed. Tech- nological and systems feasibility addresses the issues of technological capabilities, including the expertise and infrastructure to complete the project. Economic feasibil- ity is the cost–benefit analysis, weighing the benefits versus the costs to determine whether the project is fiscally possible and worth undertaking. Formal assessments should include return on investment. Legal feasibility assesses the legal ramifications

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of the project, including current contractual obligations, legislation, regulatory bod- ies, and liabilities that could affect the project. Operational feasibility determines how effective the project will be in meeting the needs and expectations of the organization and actually achieving the goals of the project or addressing and solving the business problem. Schedule feasibility assesses the viability of the time frame, making sure it is a reasonable estimation of the time and resources necessary for the project to be de- veloped in time to attain the benefits and meet constraints. TELOS helps to provide a clear picture of the feasibility of the project.

Analysis During the analysis phase, the requirements for the system are teased out from a de- tailed study of the business needs of the organization. As part of this analysis, work flows and business practices are examined. It may be necessary to consider options for changing the business process.

Design The design phase focuses on high- and low-level design and interface and data de- sign. At the high-level phase, the team establishes which programs are needed and ascertains how they will interact. At the low-level phase, team members explore how the individual programs will actually work. The interface design determines what the look and feel will be or what the interfaces will look like. During data design, the team critically thinks about and verifies which data are required or essential.

The analysis and design phases are vital in the development cycle, and great care is taken during these phases to ensure that the software’s overall configuration is de- fined properly. Mockups or prototypes of screenshots, reports, and processes may be generated to clarify the requirements and get the team or stakeholders on the same page, limiting the occurrence of glitches that might result in costly software develop- ment revisions later in the project.

Implement During this phase, the designs are brought to life through programming code. The right programming language, such as C++, Pascal, Java, and so forth, is chosen based on the application requirements.

Test The testing is generally broken down into five layers: (1) the individual programming modules, (2) integration, (3) volume, (4) the system as a whole, and (5) beta testing. Typically, the programs are developed in a modular fashion, and these individual modules are then subjected to detailed testing. The separate modules are subsequently synthesized, and the interfaces between the modules are tested. The system is evalu- ated with respect to its platform and the expected amount or volume of data. It is then tested as a complete system by the team. Finally, to determine if the system per- forms appropriately for the user, it is beta tested. During beta testing, users put the new system through its paces to make sure that it does what they need it to do to perform their jobs.

Waterfall Model 179

Maintain Once the system has been finalized from the testing phase, it must be maintained. This could include user support through actual software changes necessitated through use or time.

According to Isaias and Issa (2015), “one common trait covers all the variations of this model: It is a sequential model. Each of its stages must be entirely concluded before the next can begin” (p. 23). The main lack of iterative development is seen as a major weakness, according to Purcell (2007). No projects are static, and typically changes occur during the SDLC. As requirements change, there is no way to address them formally using the waterfall method after project requirements are developed. The waterfall model should be used for simple projects when the requirements are well known and stable from the outset.

Rapid Prototyping or Rapid Application Development As technology advances and faster development is expected, rapid prototyping, also known as rapid application development (RAD), provides a fast way to add functional- ity through prototyping and user testing. It is easier for users to examine an actual prototype rather than documentation. A rapid requirements-gathering phase relies on workshops and focus groups to build a prototype application using real data. This prototype is then beta tested with users, and their feedback is used to perfect or add functionality and capabilities to the system (Figure 9-2).

According to Alexandrou (2016), “RAD (rapid application development) pro- poses that products can be developed faster and of higher quality” (para. 1). The RAD approach uses informal communication, repurposes components, and typically follows a fast-paced schedule. Object-oriented programming using such languages as C++ and Java promotes software repurposing and reuse.

The major advantage is the speed with which the system can be deployed; a working, usable system can be built within 3 months. The use of prototyping allows the developers to skip steps in the SDLC process in favor of getting a mockup in front of the user. At times, the system may be deemed acceptable if it meets a pre- defined minimum set of requirements rather than all of the identified requirements.

Testing Imple- mentation

Analysis and quick




Demon- strate

Figure 9-2 Rapid Application Development (RAD) or Rapid Prototyping

180 CHAPteR 9 Systems Development Life Cycle: Nursing Informatics and Organizational Decision Making

This rapid deployment also limits the project’s exposure to change elements. Un- fortunately, the fast pace can be its biggest disadvantage in some cases. Once one is locked into a tight development schedule, the process may be too fast for adequate testing to be put in place and completed. The most dangerous lack of testing is in the realm of security.

The RAD approach is chosen because it builds systems quickly through user-driven prototyping and adherence to quick, strict delivery milestones. This ap- proach continues to be refined and honed, and other contemporary manifestations of RAD continue to emerge in the agile software development realm.

object-oriented Systems Development The object-oriented systems development model blends SDLC logic with object-oriented modeling and programming power (Stair & Reynolds, 2016). Object-oriented model- ing makes an effort to represent real-world objects by modeling the real-world enti- ties or things (e.g., clinic, patient, account, nursing or healthcare professional) into abstract computer software objects. Once the system is object oriented, all of the interactions or exchanges take place between or among the objects. The objects are derived from classes, and each object is comprised of data and the actions that can be enacted on that data. Class hierarchy allows objects to inherit characteristics or at- tributes from parent classes, which fosters object reuse, resulting in less coding. The object-oriented programming languages, such as C++ and Java, promote software repurposing and reuse. Therefore, the class hierarchy must be clearly and appropri- ately designed to reap the benefits of this SDLC approach, which uses object-oriented programming to support the interactions of objects.

For example, in the case scenario, a system could be developed for the Wellness Alliance to manage the community health programming for the clinic system being set up for outreach. There could be a class of programs, and well-baby care could be an object in the class of programs; programs is a relationship between Wellness Alli- ance and well-baby care. The program class has attributes, such as clinic site, location address, or attendees or patients. The relationship itself may be considered an object having attributes, such as pediatric programs. The class hierarchy from which all of the system objects are created with resultant object interactions must be clearly defined.

The OOSD model is a highly iterative approach. The process begins by investigat- ing where object-oriented solutions can address business problems or needs, deter- mining user requirements, designing the system, programming or modifying object modeling (class hierarchy and objects), implementing, user testing, modifying, and reimplementing the system, and ends with the new system being reviewed regularly at established intervals and modifications being made as needed throughout its life.

Dynamic System Development Method The dynamic system development method is a highly iterative and incremental approach with a high level of user input and involvement. The iterative process requires repetitive examination that enhances detail and improves accuracy. The DSDM has three phases: (1) preproject, (2) project life cycle (feasibility and business

Dynamic System Development Method 181


Business studie s

Agree plan

Functional model iteration

Identity functional prototype

Review prototype

Create functional prototype

Identity design prototypes

Review design


Design & build iteration

Create design prototype

Agree plan


User approval &

User guidelines

Implementation Train users

Review business

Figure 9-3 Dynamic System Development Method (DSDM) Copyright 2014 Agile Business Consortium Limited. Reproduced by kind permission.

studies, functional model iteration, design and build iteration, and implementation), and (3) postproject.

In the preproject phase, buy-in or commitment is established and funding is se- cured. This helps to identify the stakeholders (administration and end users) and gain support for the project. In the second phase, the project’s life cycle begins. This phase includes five steps: (1) feasibility, (2) business studies, (3) functional model iteration, (4) design and build iteration, and (5) implementation (Figure 9-3).

In steps 1 and 2, the feasibility and business studies are completed. The team as- certains if this project meets the required business needs while identifying the poten- tial risks during the feasibility study. In step 1, the deliverables are a feasibility report, project plan, and a risk log. Once the project is deemed feasible, step 2, the business study, is begun. The business study extends the feasibility report by examining the processes, stakeholders, and their needs. It is important to align the stakeholders with the project and secure their buy-in because it is necessary to have user input and in- volvement throughout the entire DSDM process. Therefore, bringing them in at the beginning of the project is imperative.

Using the MoSCoW approach, the team works with the stakeholders to develop a prioritized requirements list and a development plan. MoSCoW stands for “Must have, Should have, Could have, and Would have.” The “must have” requirements are needed to meet the business needs and are critical to the success of the project. “Should have” requirements are those that would be great to have if possible, but the success of the project does not depend on them being addressed. The “could

182 CHAPteR 9 Systems Development Life Cycle: Nursing Informatics and Organizational Decision Making

have” requirements are those that would be nice to have met, and the “would have” requirements can be put off until later; these may be undertaken during future de- velopmental iterations. Timeboxing is generally used to develop the project plan. In timeboxing, the project is divided into sections, each having its own fixed budget and dates or milestones for deliverables. The MoSCoW approach is then used to prioritize the requirements within each section; the requirements are the only variables because the schedule and budget are set. If a project is running out of time or money, the team can easily omit the requirements that have been identified as the lowest priority to meet their schedule and budget obligations. This does not mean that the final deliver- able, the actual system, would be flawed or incomplete. Instead, because the team has already determined the “must have” or “should have” items, it still meets the business needs. According to Haughey (2010), the 80/20 rule, or Pareto principle, can be ap- plied to nearly everything. The Pareto principle states that 80% of the project comes from 20% of the system requirements; therefore, the 20% of requirements must be the crucial requirements or those with the highest priority. One also must consider the pancake principle: The first pancake is not as good as the rest, and one should know that the first development will not be perfect. This is why it is extremely important to clearly identify the “must have” and “should have” requirements.

In the third step of the project life cycle phase, known as functional model itera- tion, the deliverables are a functional model and prototype ready for user testing. Once the requirements are identified, the next step is to translate them into a func- tional model with a functioning prototype that can be evaluated by users. This could take several iterations to develop the desired functionality and incorporate the users’ input. At this stage, the team should examine the quality of the product and revise the list requirements and risk log. The requirements are adjusted, the ones that have been realized are deleted, and the remaining requirements are prioritized. The risk log is re- vised based on the risk analysis completed during and after prototype development.

The design and build iteration step focuses on integrating functional components and identifying the nonfunctional requirements that need to be in the tested system. Testing is crucial; the team will develop a system that the end users can safely use on a daily basis. The team will garner user feedback and generate user documentation. These efforts provide this step’s deliverable, a tested system with documentation for the next and final phase of the development process.

In the final step of the project life cycle phase, known as implementation, de- liverables are the system (ready to use), documentation, and trained users. The re- quirements list should be satisfied, along with the users’ needs. Training users and implementing the approved system is the first part of this phase, and the final part consists of a full review. It is important to review the impact of the system on the business processes and to determine if it addressed the goals or requirements estab- lished at the beginning of the project. This final review determines if the project is completed or if further development is necessary. If further development is needed, preceding phases are revisited. If the project is complete and satisfies the users, then it moves into maintenance and ongoing development.

The final phase is labeled “postproject.” In this phase, the team verifies that the system is functioning properly. Once verified, the maintenance schedule is begun. Be- cause the DSDM is iterative, this postproject phase is seen as ongoing development

Dynamic System Development Method 183

and any of the deliverables can be refined. This is what makes the DSDM such an iterative development process.

DSDM is one of an increasing number of agile methodologies being introduced, such as Scrum and Extreme Programming. These new approaches address the orga- nizational, managerial, and interpersonal communication issues that often bog down SDLC projects. Empowerment of teams and user involvement enhance the iterative and programming strengths provided in these SDLC models.

Computer-Aided Software engineering tools When reviewing SDLC, the computer-aided software engineering (CASE) tools that will be used must be described.

CASE tools promote adherence to the SDLC process since they automate several required tasks; this provides standardization and thoroughness to the total systems development method (Stair & Reynolds, 2016). These tools help to reduce cost and development time while enriching the quality of the product. CASE tools contain a repository with information about the system: models, data definitions, and references linking models together. They are valuable in their ability to make sure the models follow diagramming rules and are consistent and complete.

The various types of tools can be referred to as upper-CASE tools or lower-CASE tools. The upper-CASE tools support the analysis and design phases, whereas the lower-CASE tools support implementation. The tools can also be general or specific in nature, with the specific tools being designed for a particular methodology.

Two examples of CASE tools are Visible Analyst and Rational Rose. Accord- ing to Andoh-Baidoo, Kunene, and Walker (2009), Visible Analyst “supports struc- tured and object-oriented design (UML),” whereas Rational Rose “supports solely object-oriented design (UML)” (p. 372). Both tools can “build and reverse database schemas for SQL and Oracle” and “support code generation for pre-.NET versions of Visual Basic” (p. 372). Visible Analyst can also support shell code generation for pre-.NET versions of C and COBOL, whereas Rational Rose can support complete code for C++ and Java. In addition, Andoh-Baidoo et al. found that Rational Rose “[p]rovides good integration with Java, and incorporates common packages into class diagrams and decompositions through classes” (p. 372).

CASE tools have many advantages, including decreasing development time and producing more flexible systems. On the down side, they can be difficult to tailor or customize and use with existing systems.

open Source Software and Free/open Source Software Another area that must be discussed with SDLC is open source software (OSS). An ex- amination of job descriptions or advertisements for candidates shows that many ISs and IT professionals need a thorough understanding of SDLC and OSS development tools (e.g., PHP, MySQL, and HTML). With OSS, any programmer can implement, modify, apply, reconstruct, and restructure the rich libraries of source codes available from proven, well-tested products.

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As Karopka, Schmuhl, and Demski (2014) noted,

Free/Libre Open Source Software (FLOSS) has been successfully adopted across a wide range of different areas and has opened new ways of value creation. Today there are hundreds of examples of successful FLOSS proj- ects and products. . . . Especially in times of financial crisis and austerity the adoption of FLOSS principles opens interesting alternatives and options to tremendously lower total cost of ownership (TCO) and open the way for a continuous user-driven improvement process. (para. 6)

To transform health care, it is necessary for clinicians to use information sys- tems that can share patient data (Goulde & Brown, 2006; NORC, 2014). This all sounds terrific and many people wonder why it has not happened yet, but the challenges are many. How does one establish the networks necessary to share data between and among all healthcare facilities easily and securely? “Healthcare IT is beginning to adopt open source software to address these challenges” (Goulde & Brown, p. 4). Early attempts at OSS ventures in the healthcare realm failed because of a lack of support or buy-in for sustained effort, technologic lags, authority and credibility, and other such issues. “Spurred by a greater sense of urgency to adopt IT, health industry leaders are showing renewed interest in open source solutions” (Goulde & Brown, p. 5).

Karopka et al., (2014) concluded that

North America has the longest tradition in applying FLOSS-HC delivery. It is home of many mature, stable and widely disseminated FLOSS applications. Some of them are even used on a global scale. The deployment of FLOSS systems in healthcare delivery is comparatively low in Europe. (para. 48)

Health care is realizing the benefits of FLOSS. According to Goulde and Brown (2006), “other benefits of open source software—low cost, flexibility, opportunities to innovate—are important but independence from vendors is the most relevant for health care” (p. 10).

Interoperability Interoperability, the ability to share information across organizations, will remain paramount under the HITECH Act. The ability to share patient data is extremely important, both within an organization and across organizational boundaries (Figure 9-4).

According to the Health Information and Management Systems Society (HIMSS; Murphy, 2015), “an acceptable 2015 [interoperability standards] Advisory and more complete 2016 Advisory will not be achievable without the inclusion of health IT se- curity standards” (para. 4). Few healthcare systems take advantage of the full poten- tial of the current state of the art in computer science and health informatics (HIMSS, 2010). The consequences of this situation include a drain on financial resources from the economy, the inability to truly mitigate the occurrence of medical errors, and a lack of national preparedness to respond to natural and manmade epidemics and di- sasters. HIMSS has created the Integration and Interoperability Steering Committee

Interoperability 185

Data Interoperability


Health Information Exchanges



Registries Practices

State Agencies

Figure 9-4 Interoperability

to guide the industry on allocating resources to develop and implement standards and technology needed to achieve interoperability (para. 2).

As we enter into SDLCs, we must be aware of how this type of development will affect both our own healthcare organization and the healthcare delivery system as a whole. In an ideal world, we would all work together to create systems that are integrated within our own organization while having the interoperability to cross organizational boundaries and unite the healthcare delivery system to realize the common goal of improving the quality of care provided to consumers.

Summary At times during the SDLC, new information affects the outputs from earlier phases; the development effort may be reexamined or halted until these modifications can be reconciled with the current design and scope of the project. At other times, teams are overwhelmed with new ideas from the iterative SDLC process that result in new capabilities or features that exceed the initial scope of the project. Astute team leaders will preserve these ideas or initiatives so they can be considered at a later time. The team should develop a list of recommendations to improve the cur- rent software when the project is complete. This iterative and dynamic exchange makes the SDLC robust.

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1. Reflect on the SDLC in relation to the quality of the organizational decision making throughout the project. What are some of the major stumbling blocks faced by healthcare organizations?

2. Why is it important for all nurses and healthcare professionals to understand the basics of how information systems are selected and implemented?

References Alexandrou, M. (2016). Rapid application development (RAD) methodology. Infolific. Retrieved

from http://www.infolific.com/technology/methodologies/rapid-application-development Andoh-Baidoo, F., Kunene, K., & Walker, R. (2009). An evaluation of CASE tools as

pedagogical aids in software development courses. 2009 SWDSI Proceedings. Retrieved from http://www.swdsi.org/swdsi2009/Papers/9K10.pdf

Goulde, M., & Brown, E. (2006). Open source software: A primer for health care leaders. Protocode. Retrieved from http://www.protecode.com/an-open-source-world-a-primer-on -licenses-obligations-and-your-company

Haughey, D. (2010). Pareto analysis step by step. Project Smart. Retrieved from http://www .projectsmart.co.uk/pareto-analysis-step-by-step.html

Health Information and Management Systems Society (HIMSS). (2010). Interoperability & standards. Retrieved from http://www.himss.org/library/interoperability-standards ?navItemNumber=13323

Isaias, P. & Issa, T. (2015). High level models and methodologies for information systems. New York, NY: Springer.

Karopka, T., Schmuhl, H., & Demski, H. (2014). Free/Libre open source software in health care: A review. Healthcare Informatics Research, 20(1), 11–22. PMCID: PMC3950260

Murphy, K. (2007). HIMSS has ideas for 2015 interoperability standards advisory. HealthIT Interoperability. Retrieved from http://healthitinteroperability.com/news/himss-has-ideas -for-2015-interoperability-standards-advisory

NORC. (2014). Data sharing to enable clinical transformation at the community level: IT takes a village. Retrieved from http://www.healthit.gov/sites/default/files/beacondatasharingbrief 062014.pdf

Purcell, J. (2007). Comparison of software development lifecycle methodologies. SANS Institute. Retrieved from https://software-security.sans.org/resources/paper/cissp /comparison-software-development-lifecycle-methodologies

Stair, R., & Reynolds, G. (2016). Principles of information systems (12th ed.). Boston, MA: Cengage Learning.

As technology and research continue to advance, new SDLC models are being pio- neered and revised to enhance development techniques. The interpretation and imple- mentation of any model selected reflect the knowledge and skill of the team applying the model. The success of the project is often directly related to the quality of the or- ganizational decision making throughout the project—that is, how well the plan was followed and documented. United efforts to create systems that are integrated and interoperable will define the future of health care.

References 187

Key Terms » Acuity systems » Admission,

discharge, and transfer systems

» American National Standards Institute (ANSI)

» Attribute » Care plan » Case manage-

ment information systems

» Clinical documenta- tion systems

» Clinical information systems

» Collaboration » Columns » Communication

systems » Computerized

physician (provider) order entry systems

» Core business systems

» Data dictionary » Data file » Data mart » Data mining » Data warehouse » Database » Database manage-

ment system » Decision support » Drill-down » Electronic health

record » Entity » Entity–relationship

diagram » Fields » Financial systems » Information

systems » Information


» International Organization for Standardization (ISO)

» Interoperability » Key field » Knowledge

exchange » Laboratory informa-

tion systems » Managed care

information systems

» Order entry systems » Patient care infor-

mation system » Patient care sup-

port system » Patient centered » Pharmacy informa-

tion systems » Picture and


communication system

» Primary key » Query » Radiology informa-

tion system » Records » Relational database

management system (RDMS)

» Repository » Rows » Scheduling systems » Stakeholders » Standardized plan

of care » Structured Query

Language (SQL) » Table » Tiering » Triage » Tuples

1. Explore agency-based health information systems. 2. Evaluate how administrators use core business

systems in their practice.

3. Assess the function and information output from selected information systems used in healthcare organizations.


Introduction To compete in the ever-changing healthcare arena, organizations require quick and immediate access to a variety of types of information, data, and bodies of knowledge for daily clinical, operational, financial, and human re- source activities. Information is continuously shared between units and de- partments within healthcare organizations and is also required or requested from other healthcare organizations, regulatory and government agencies, educational and philanthropic institutions, and consumers. Organizations need interoperable systems that are accessible for data storage and retrieval.

The healthcare context is distinct from other organizations that use in- formation systems.

Fichman, Kohli, and Krishnan (2011) identify six important elements of health care that influence the development and implementation of information systems:

• The stakes are life and death. • Healthcare information is highly personal. • Health care is highly influenced by regulation and competition. • Health care is professionally driven and hierarchical. • Health care is multidisciplinary. • Healthcare information system implementation is complex, with im-

portant implications for learning and adaptation (pp. 420–423).

Healthcare organizations integrate a variety of clinical and administra- tive types of information systems (ISs). These systems collect, process, and distribute patient-centered data to aid in managing and providing care. To- gether, they create a comprehensive record of the patient’s medical history and support organizational processes. Each of these systems is unique in the way it functions and provides information to clinicians and administra- tors. An understanding of how each of these types of systems works within healthcare organizations is fundamental in the study of informatics. This chapter will focus on the administrative organizational systems.

Administrative Information Systems Marianela Zytkowski, Susan Paschke, Kathleen Mastrian, and Dee McGonigle



Types of Healthcare Organization Information Systems Case Management Information Systems Case management information systems identify resources, patterns, and variances in care to prevent costly complications related to chronic conditions and to enhance the overall outcomes for patients with chronic illness. These systems span past episodes of treatment and search for trends among the records. Once a trend is identified, case management systems provide decision support promoting preventive care. Care plans are a common tool found in case management systems. A care plan is a set of care guidelines that outline the course of treatment and the recommended interventions that should be implemented to achieve optimal results. By using a standardized plan of care, these systems present clinicians with treatment protocols to maximize patient out- comes and support best practices. Information technology in health care is positioned to support the development of interdisciplinary care plans. In the health informatics pathway, Standard 5 deals with documentation: “Health informatics professionals will understand the content and diverse uses of health information. They will accurately document and communicate appropriate information using legal and regulatory pro- cesses” (National Consortium for Health Science Education, 2012, para. 11).

Case management information systems are especially beneficial for patient popula- tions with a high cost of care and complex health needs, such as the elderly or patients with chronic disease conditions. Avoiding complications requires identifying the right resources for care and implementing preventive treatments across all medical visits. Ultimately, this preventive care decreases the costs of care for patients with chronic illnesses and supports a better quality of life. Such systems increase the value of indi- vidual care while controlling the costs and risks associated with long-term health care.

Case management systems are increasingly being integrated with electronic health records (EHRs). Information collected by these systems is processed in a way that helps to reduce risks, ensure quality, and decrease costs. A presentation of results of the 2012 Health Information Technology Survey, conducted by the Case Management Society of America (CMSA, 2014), revealed several key trends in information technology, including the increased use of social media and wireless communications, the use of IT to support care transitions and prevent readmissions, expanded use of patient engagement technologies (text messaging, email, portals, smartphone apps), and work toward the integration of case management software into the EHR.

Communication Systems Communication systems promote interaction among healthcare providers and between providers and patients. Such systems have historically been kept separate from other types of health information systems and from one another. Healthcare professionals overwhelmingly recognize the value of these systems, however, so they are now more commonly integrated into the design of other types of systems as a newly develop- ing standard within the industry. Examples of communication systems include call light systems, wireless telephones, pagers, email, and instant messaging, which have

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traditionally been forms of communication targeted at clinicians. Other communica- tion systems target patients and their families. Some patients are now able to access their electronic chart from home via an Internet connection. They can update their own medical record to inform their physician of changes to their health or personal practices that impact their physical condition. Inpatients in hospital settings also receive communication directly to their room. Patients and their families may, for example, review individualized messages with scheduled tests and procedures for the day and confirm menu choices for their meals. These types of systems may also com- municate educational messages, such as smoking cessation advice.

As health care begins to introduce more of this technology into practice, the value of having communication tools integrated with other types of systems is being widely recognized. Integrating communication systems with clinical applications provides a real-time approach that facilitates interactions among the entire healthcare team, patients, and their families to enhance care. These systems enhance the flow of com- munication within an organization and promote an exchange of information to care better for patients. The next generation of communication systems will be integrated with other types of healthcare systems and guaranteed to work together smoothly. The Research Brief discusses the economic impact of communication inefficiencies in U.S. hospitals. As hospitals and physician practices strive to become more patient centered, communication technologies will be an integral part of this goal. Many of us have experienced the anxiety of waiting for news about a loved one during a surgi- cal procedure. Newer communication techniques, such as surgical tracking boards that communicate about the process, help to ease these anxieties. Gordon and col- leagues (2015) report high patient and family satisfaction with a HIPAA-compliant surgical instant messaging system to communicate real-time surgical progress with patient-designated recipients. They stated that

[w]hile this study focused on the discipline of surgery, we can easily imagine the benefits of this type of communications application outside of the surgi- cal model that we have studied. The results of any laboratory, pathology, or radiography studies can be instantaneously shared with concerned family members all over the globe. In the critical care setting, doctors can commu- nicate with a patient’s extended support group more efficiently and in a less stress-inducing environment than the typical crowded consultation room outside of the intensive care unit. News of the arrival of a newborn baby boy or girl can be sent to eager aunts, uncles, and grandparents back home. The opportunities for enhancing communication pertaining to medical issues are seemingly limitless. (p. 6)

What are some other ways that new communication technologies could be used to increase patient and family satisfaction with health care in your practice?

Core Business Systems Core business systems enhance administrative tasks within healthcare organizations. Unlike clinical information systems (CISs), whose aim is to provide direct patient care, these systems support the management of health care within an organization. Core

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business systems provide the framework for reimbursement, support of best practices, quality control, and resource allocation. There are four common core business sys- tems: (1) admission, discharge, and transfer (ADT) systems; (2) financial systems; (3) acuity systems; and (4) scheduling systems.

Admission, discharge, and transfer systems provide the backbone structure for the other types of clinical and business systems (Hassett & Thede, 2003). These systems were among the first to be automated in health care. Admitting, billing, and bed management departments most commonly use ADT systems. These systems hold key information on which all other systems rely. For example, ADT systems maintain the patient’s name; medical record number; visit or account number; and demographic information, such as age, gender, home address, and contact information. Such sys- tems are considered the central source for collecting this type of patient information and communicating it to other types of healthcare information systems.

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Researchers attempted to quantify the costs of poor communication, termed “communication inefficiencies,” in hospitals. This qualitative study was con- ducted in seven acute care hospitals of varying sizes via structured interviews with key informants at each facility. The interview questions focused on four broad categories: (1) communication bottlenecks, (2) negative outcomes as a result of those bottlenecks, (3) subjective perceptions of the potential effective- ness of communication improvements on the negative outcomes, and (4) ideas for specific communication improvements. The researchers independently coded the interview data and then compared results to extract themes.

All of the interviewees indicated that communication was an issue. Inefficien- cies revolved around time spent tracking people down to communicate with them, with various estimates provided: 3 hours per nursing shift wasted tracking people down, 20% of productive time wasted on communication bottlenecks, and a reported average of five to six telephone calls to locate a physician. Several respondents pointed to costly medical errors that were the direct result of com- munication issues. Communication lapses also resulted in inefficient use of clinician resources and increased length of stay for patients.

The researchers developed a conceptual model of communication quality with four primary dimensions: (1) efficiency of resource use, (2) effectiveness of resource use, (3) quality of work life, and (4) service quality. They concluded that the total cost of communication inefficiencies in U.S. hospitals is more than $12 billion annually and estimated that a 500-bed hospital could lose as much as $4 million annually because of such problems. They urge the adoption of information technologies to redesign workflow processes and promote better communication.

The full article appears in Agarwal, R., Sands, D., Schneider, J., & Smaltz, D. (2010). Quantifying the economic impact of communication inefficiencies in U.S. hospitals. Journal of Healthcare Manage- ment, 55(4), 265–281.

Financial systems manage the expenses and revenue for providing health care. The finance, auditing, and accounting departments within an organization most com- monly use financial systems. These systems determine the direction for maintenance and growth for a given facility. They often interface to share information with materials management, staffing, and billing systems to balance the financial impact of these resources within an organization. Financial systems report fiscal outcomes, which can then be tracked and related to the organizational goals of an institution. These systems are key components in the decision-making process as healthcare institutions prepare their fiscal budgets. They often play a pivotal role in determining the strategic direction for an organization.

Acuity systems monitor the range of patient types within a healthcare organiza- tion using specific indicators. They track these indicators based on the current patient population within a facility. By monitoring the patient acuity, these systems provide feedback about how intensive the care requirement is for an individual patient or group of patients. Identifying and classifying a patient’s acuity can promote better organizational management of the expenses and resources necessary to provide care. Acuity systems help predict the ability and capacity of an organization to care for its current population. They also forecast future trends to allow an organization to suc- cessfully strategize on how to meet upcoming market demands.

Scheduling systems coordinate staff, services, equipment, and allocation of patient beds. They are frequently integrated with the other types of core business systems. By closely monitoring staff and physical resources, these systems provide data to the finan- cial systems. For example, resource-scheduling systems may provide information about operating room use or availability of intensive care unit beds and regular nursing unit beds. These systems also provide great assistance to financial systems when they are used to track medical equipment within a facility. Procedures and care are planned when the tools and resources are available. Scheduling systems help to track resources within a facility while managing the frequency and distribution of those resources.

Order Entry Systems Order entry systems are one of the most important systems in use today. They automate the way that orders have traditionally been initiated for patients—that is, clinicians place orders using these systems instead of creating traditional handwritten transcrip- tions onto paper. Order entry systems provide major safeguards by ensuring that physician orders are legible and complete, thereby providing a level of patient safety that was historically missing with paper-based orders. Computerized physician (provider) order entry systems provide decision support and automated alert functionality that was unavailable with paper-based orders.

The seminal report by the Institute of Medicine estimated that medical errors cost the United States approximately $37.6 billion each year; nearly $17 billion of those costs are associated with preventable errors (Kohn, Corrigan, Donaldson, & Institute of Medicine, 2000). Consequently, the federal Agency for Healthcare Research and Quality Patient Safety Network (2015) continued to recommend eliminating reliance on handwriting for ordering medications. Because of the global concern for patient safety as a result of incorrect and misinterpreted orders, healthcare organizations are

Order Entry Systems 193

incorporating order entry systems into their operations as a standard tool for prac- tice. Such systems allow for clear and legible orders, thereby both promoting patient safety and streamlining care. Although much of the health information technology literature suggests that physicians are resistant to adopting health information tech- nology, a recent study by Elder, Wiltshire, Rooks, BeLue, and Gary (2010) found that physicians who use information technology were more satisfied overall with their careers. The Informatics Tools to Promote Patient Safety and Quality Outcomes chapter provides more information about the use of computerized physician order entry systems in clinical care.

Patient Care Support Systems Most specialty disciplines within health care have an associated patient care informa- tion system. These patient-centered systems focus on collecting data and disseminating information related to direct care. Several of these systems have become mainstream types of systems used in health care. The four systems most commonly encountered in health care include (1) clinical documentation systems, (2) pharmacy information sys- tems, (3) laboratory information systems, and (4) radiology information systems.

Clinical documentation systems, also known as “clinical information systems,” are the most commonly used type of patient care support system within healthcare organi- zations. CISs are designed to collect patient data in real time. They enhance care by putting data at the clinician’s fingertips and enabling decision making where it needs to occur—that is, at the bedside. For that reason, these systems often are easily acces- sible at the point of care for caregivers interacting with the patient. CISs are patient centered, meaning they contain the observations, interventions, and outcomes noted by the care team. Team members enter information, such as the plan of care, hemody- namic data, laboratory results, clinical notes, allergies, and medications. All members of the treatment team use clinical documentation systems; for example, pharmacists, allied health workers, nurses, physicians, support staff, and many others access the clinical record for the patient using these systems. Frequently these types of systems are also referred to as the electronic patient record or electronic health record. The Electronic Health Record and Clinical Informatics chapter provides a comprehensive overview of CISs and the electronic health record.

Pharmacy information systems also have become mainstream patient care support systems. They typically allow pharmacists to order, manage, and dispense medications for a facility. They also commonly incorporate information regarding allergies and height and weight to ensure effective medication management. Pharmacy information systems streamline the order entry, dispensing, verification, and authorization process for medication administration. They often interface with clinical documentation and order entry systems so that clinicians can order and document the administration of medications and prescriptions to patients while having the benefits of decision sup- port alerting and interaction checking.

Laboratory information systems were perhaps some of the first clinical information systems ever used in health care. Because of their long history of use within medicine, laboratory systems have been models for the design and implementation of other types of patient care support systems. Laboratory information systems report on

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blood, body fluid, and tissue samples, along with biological specimens collected at the bedside and received in a central laboratory. They provide clinicians with reference ranges for tests indicating high, low, or normal values to make care decisions. Often, the laboratory system provides result information in the EHR and directs clinicians toward the next course of action within a treatment regimen.

The final type of patient care support system commonly found within health care is the radiology information system (RIS) found in radiology departments. These systems schedule, result, and store information related to diagnostic radiology procedures. One feature found in most radiology systems is a picture archiving and communication system (PACS). The PACS may be a stand-alone system, kept separate from the main radiology system, or it can be integrated with the RIS and CIS. These systems collect, store, and distribute medical images, such as computed tomography scans, magnetic resonance images, and X-rays. PACS replace traditional hard-copy films with digital media that are easy to store, retrieve, and present to clinicians. The benefit of RIS and PACS is their ability to assist in diagnosing and storing vital patient care support data. Imaging studies can be available in minutes as opposed to 2–6 hours for images in a film-based system. The digital workstations provide enhanced imaging capabilities and on-screen measurement tools to improve diagnostic accuracy. Finally, the archive system stores images in a database that is readily accessible, so that images can be easily retrieved and compared to subsequent testing or shared instantly with consultants.

The mobility of patients both geographically and within a single healthcare delivery system challenges information systems because data must be captured wherever and whenever the patient receives care. In the past, managed care information systems were implemented to address these issues. Consequently, data can be obtained at any and all of the areas where a patient interacts with the healthcare system. Patient-tracking mechanisms continue to be honed, but the financial impact of health care also has changed these systems to some extent. The information systems currently in use enable nurses and physicians to make clinical decisions while being mindful of their financial ramifications. In the future, vast improvements in information systems and systems that support health information exchange are likely to continue to emerge.

One such trend is the incentive to develop accountable care organizations encouraged by the Patient Protection and Affordable Care Act of 2010. According to the Centers for Medicare and Medicaid Services (2015), “Accountable Care Organizations (ACOs) are groups of doctors, hospitals, and other health care pro- viders, who come together voluntarily to give coordinated high quality care to their Medicare patients. The goal of coordinated care is to ensure that patients, especially the chronically ill, get the right care at the right time, while avoiding unnecessary duplication of services and preventing medical errors” (para. 1–2). Members of an ACO share data and information to better coordinate care and they also share in any health care cost savings generated when the coordination of care reduces unnec- essary and duplicated costs.

Interoperability A key component to coordinated care is the interoperability of healthcare informa- tion systems. In 2015, the Office of the National Coordinator for Health IT (ONC)

Interoperability 195

released an interoperability roadmap to promote ease of access and use of electronic healthcare data. Interoperability is defined as “the ability of a system to exchange electronic health information with and use electronic health information from other systems without special effort on the part of the user” (Healthcare Information and Management Systems Society [HIMSS], 2015, para. 2). The final goal of the national roadmap emphasis on interoperability is driven by the need to “achieve nationwide interoperability to enable a learning health system, with the person at the center of a system that can continuously improve care, public health, and science through real-time data access” (ONC, 2015, p. vii). As we develop more sophisticated elec- tronic systems, we are realizing the huge potential benefits of exchanging secure and precise healthcare data. However, in the current landscape, several things need to happen to realize this goal. Chief among them is a worldwide commitment to interoperability. HIMSS (2013) identified three types of health information technol- ogy interoperability—foundational, structural, and semantic—each with increasing complexity. Foundational interoperability is basic data reception from one system to another without interpretation. Structural interoperability is more complex and depends on consistency of clinical terminology and meaning of the data. Semantic interoperability depends on data that is consistent and codified allowing for infor- mation system interpretation and analysis of the data. Semantic interoperability is considered the highest and most complex form of interoperability. Semantic interop- erability is necessary for seamless health information exchange.

Suppose you have a joint replacement patient who is being discharged from the acute care facility to a rehabilitation center. You create a discharge summary for the patient in a PDF format and send it via a secure electronic exchange to the new facility. The staff at the rehabilitation center is able to read and understand the report and a staff assistant can scan a copy of the discharge summary into the electronic re- cord for the rehabilitation facility. This is an example of functional interoperability. If each facility uses Health Level Seven standards for data exchange and collects certain minimum data, then it might be possible for certain data fields from one facility to populate automatically into an appropriate data field in the new facility. This is an example of structural interoperability. To achieve true semantic interoperability, sys- tems must use the same standardized terminologies or disparate terminologies must be mapped, and the two systems must be able to “talk” to each other to exchange data seamlessly and to populate the data into to the appropriate fields in the new sys- tem. True semantic interoperability enables machine-to-machine data exchange.

Consistently representing electronic health information across different stakeholders and systems is the bedrock of successful interoperability. In a learning health system, while user interfaces can and should be different depending on the user, the format in which electronic health information is shared between health IT systems must be consistent and machine readable, so that the meaning and integrity of information is retained as a variety of users interact with it. (ONC, 2015, p. 28)

For more detailed information on interoperability, download and read the ONC’s Interoperability Roadmap: https://www.healthit.gov/sites/default/files/hie-interoperability /nationwide-interoperability-roadmap-final-version-1.0.pdf

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Aggregating Patient and Organizational Data Many healthcare organizations now aggregate data in a data warehouse (DW) for the purpose of mining the data to discover new relationships and to build organizational knowledge. Rojas (2015) stated that

Hospitals and medical centers have more to gain from big data analytics than perhaps any other industry. But as data sets continue to grow, healthcare facilities are discovering that success in data analytics has more to do with storage methods than with analysis software or techniques. Traditional data silos are hindering the progress of big data in the healthcare industry, and as terabytes turn into petabytes, the most successful hospitals are the ones that are coming up with new solutions for storage and access challenges. (para. 1)

When disparate information systems within an organization are unable to inter- face with any other information systems (either within or outside of the organiza- tion), the result is poor communication, billing errors, and issues with continuity of care. By developing a single comprehensive database, healthcare systems are able to facilitate interprofessional communications, yet maintain compliance with privacy regulations. Figure 10-1 depicts moving from siloed to integrated data.

Based on the size of the organization, data triage and tiering might be necessary. These decision-making processes related to data storage are based on predictions related to how quickly data might need to be accessed.

Consider the case of Intermountain, a chain of 22 hospitals in Salt Lake City. With 4.7 petabytes of data under its management, cloud storage becomes cost prohibitive. The network estimates the size of the hospital chain’s data will grow by 25–30% each year until it reaches 15 petabytes in 5 years. With such massive data needs, In- termountain found ways to cut costs and streamline efficiency. One way was through data tiering, which is the creation of data storage tiers that can be accessed at the appropriate speeds. Tiering is currently done manually through triaging, but several different organizations are exploring autotiering, which automatically stores data ac- cording to availability needs (Rojas, 2015, para. 9–10).

Aggregating Patient and Organizational Data 197

Figure 10-1 Moving from Data Silos to Integrated Data Data from Smart Data Collective. (2015). 2 critical obstacles facing retailers for data driven marketing. Retrieved from http://www.smartdatacollective.com/lbedgood/349875/two-critical-obstacles -facing-retailers-data-driven-marketing

Laboratory/ X-ray/Pharmacy


Clinical EHR data

Demographic information

Siloed Data

3 60° Holistic view of the pa tien


Integrated Data

The most basic element of a database system is the data. Data refers to raw facts that can consist of unorganized text, graphics, sound, or video. Information is data that have been processed—it has meaning; information is organized in a way that people find meaningful and useful. Even useful information can be lost if one is mired in unor- ganized information. Computers can come to the rescue by helping to create order out of chaos. Computer science and information science are designed to help cut down the amount of information to a more manageable size and organize it so that users can cope with it more efficiently through the use of databases and database programs technology. Learning about basic databases and database management programs is paramount so that users can apply data and information management principles in health care.

A database is a structured or organized collection of data that is typically the main component of an information system. Databases and database management software allow the user to input, sort, arrange, structure, organize, and store data and turn those data into useful information. An individual can set up a personal database to organize recipes, music, names and addresses, notes, bills, and other data. In health care, databases and information systems make key information available to healthcare providers and ancillary personnel to promote the provision of quality patient care. Box 10-1 provides a detailed description of a database.

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Databases consist of fields (columns) and records (rows). Within each record, one of the fields is identified as the primary key or key field. This primary key contains a code, name, number, or other information that acts as a unique identifier for that record. In the healthcare system, for example, a patient is assigned a patient number or ID that is unique for that patient. As you compile related records, you create data files or tables. A data file is a collection of related records. Therefore, databases consist of one or more related data files or tables.

An entity represents a table, and each field within the table becomes an at- tribute of that entity. The database developer must critically think about the at- tributes for each specific entity. For example, the entity “disease” might have the attributes of “chronic disease,” “acute disease,” or “communicable disease.” The name of the entity, “disease,” implies that the entity is about diseases. The fields or attributes are “chronic,” “acute,” or “communicable.”

The entity–relationship diagram specifies the relationship among the entities in the database. Sometimes the implied relationships are readily apparent based on the entities’ definitions; however, all relationships should be specified as to how they relate to one another. Typically, three relationships are possible: (1) one to one, (2) one to many, and (3) many to many. A one-to-one relationship exists between the entities of the table about a patient and the table about the patient’s birth. A one-to-many relationship could exist when one entity is repeat- edly used by another entity. Such a one-to-many relationship could then be a table query for age that could be used numerous times for one patient entity. The many-to-many relationship reflects entities that are all used repeatedly by other

Aggregating Patient and Organizational Data 199

entities. This is easily explained by the entities of patient and nurse. The patient could have several nurses caring for him or her, and the nurse could have many patients assigned to him or her (see Figure 10-2).

The relational model is a database model that describes data in which all data elements are placed in relation in two-dimensional tables; the relations or tables are analogous to files. A relational database management system (RDMS) is a system that manages data using this kind of relational model. A relational database could link a patient’s table to a treatment table (e.g., by a common field, such as the patient ID number). To keep track of the tables that constitute a database, the database management system uses software called a data dictionary. The data dictionary contains a listing of the tables and their details, including field names, validation settings, and data types. The data type refers to the type of informa- tion, such as a name, a date, or a time.

The database management system is an important program because before it was available, many health systems and businesses had dozens of database files with incompatible formats. Because patient data come from a variety of sources, these separated, isolated data files required duplicate entry of the same informa- tion, thereby increasing the risk of data entry error. The design of the relational databases eliminates data duplication. Some examples of popular database man- agement system software include Microsoft’s Access or Visual FoxPro, Corel’s Paradox, Oracle’s Oracle Database 10g, and IBM’s DB2.

ID number

ID number



ID number Name Advanced

practice nurse


PatientAssigned to


Figure 10-2 Example of an Entity Relationship Diagram (ERD)

On a large scale, a data warehouse is an extremely large database or repository that stores all of an organization’s or institution’s data and makes these data avail- able for data mining. The DW can combine an institution’s many different databases to provide management personnel with flexible access to the data. On the smaller scale, a data mart represents a large database where the data used by one of the units or a division of a healthcare system are stored and maintained. For example, a university hospital system might store clinical information from its many affiliate hospitals in a DW, and each separate hospital might have a data mart housing its data.

There are many ways to access and retrieve information in databases. Searching information in databases can be done through the use of a query, as is used in Microsoft’s Access database. A query asks questions of the database to retrieve spe- cific data and information. Box 10-2 provides a detailed description of the Structured Query Language (SQL).

Data mining software sorts thorough data to discover patterns and ascertain or establish relationships. This software discovers or uncovers previously unidentified relationships among the data in a database by conducting an exploratory analysis looking for hidden patterns in data. Using such software, the user searches for previously undiscovered or undiagnosed patterns by analyzing the data stored in a DW. Drill-down is a term that means the user can view DW information by drilling down to lower levels of the database to focus on information that is pertinent to his or her needs at the moment.

As users move through databases within the healthcare system, they can access anything from enterprise-wide DWs to data marts. For example, an infection-control nurse might notice a pattern of methicillin-resistant Staphylococ- cus aureus infections in the local data mart (a single hospital within a larger sys- tem). The nurse might want to find out if the outbreak is local (data mart) or more widespread in the system (DW). The nurse might also query the database to deter- mine if certain patient attributes (e.g., age or medical diagnosis) are associated with the incidence of infection.

These kinds of data mining capabilities are also quite useful for healthcare practi- tioners who wish to conduct clinical research studies. For example, one might query a database to tease out attributes (patient characteristics) associated with asthma- related hospitalizations. For a more detailed description and review of data mining, refer to the Data Mining as a Research Tool chapter.

According to Mishra, Sharma, and Pandey (2013), there is a new set of challenges and opportunities for managing data, data mining, and establishing algorithms in the cloud. Data mining in the cloud is emerging and evolving. This frontier is becom- ing a potent way to take advantage of the power of cloud computing and combine it with SQL. The world as we know it is changing: “Clouds” are leading us to develop revolutionary data mining technologies. There are five typical clinical applications for databases: (1) hospitals, (2) clinical research, (3) clinical trials, (4) ambulatory care, and (5) public health. Some healthcare systems are connecting their hospitals together by choosing a single CIS to capture data on a system-wide basis. In such healthcare orga- nizations, multiple application programs share a pool of related data. Think about how potent such databases might potentially be in managing organizations and providing insights into new relationships that may ultimately transform the way work is done.

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Aggregating Patient and Organizational Data 201

BOX 10-2 SQL

SQL was originally called SEQUEL, or Structured English Query Language. SQL, still pronounced “sequel,” now stands for Structured Query Language; it is a database querying language, rather than a programming language. It is a standard language for accessing and manipulating databases. SQL is “used with relational databases; it allows users to define the structure and organization of stored data, verify and maintain data integrity, control access to the data, and define relation- ships among the stored data items” (University of California at San Diego, 2010, para. 8). In this way, it simplifies the process of retrieving information from a database in a functional or usable form while facilitating the reorganization of data within the databases.

The relational database management system is the foundation or basis for SQL. An RDMS stores data in “database objects called tables” (W3Schools. com, 2010, para. 6). A table is a collection of related data that consists of columns and rows; as noted earlier, columns are also referred to as fields, and rows are also referred to as records or tuples. Databases can have many tables, and each table is identified by a name (see the Database Example: School of Nursing Faculty).

SQL statements handle most of the actions users need to perform on a database. SQL is an International Organization for Standardization (ISO) standard and American National Standards Institute (ANSI) standard, but many different versions of the SQL language exist (Indiana University, 2010). To remain compliant with the ISO and ANSI standards, SQL must handle or support the major commands of SELECT, UPDATE, DELETE, INSERT, and WHERE in a similar manner (W3Schools.com, 2010). The SELECT command allows you to extract data from a database. UPDATE updates the data, DELETE deletes the data, and INSERT inserts new data. WHERE is used to specify selection criteria, thereby restricting the results of the SQL query. Thus SQL allows you to create databases and manipulate them by storing, retrieving, updating, and deleting data.

Database Example: School of Nursing Faculty

Table Named “Faculty”

Last First Department Office Phone Office

P_ID Name Name Affiliation Number Location UserID

1 Eggleers Renee Informatics 444-111-1104 104A Eggleersr100

2 Feistyz Judi Gerontology 444-111-2202 202b Feistyzj562

3 Martinez Bethann Neurology 444-111-3336 336C Martinezb789

4 Smythe Ralph Informatica 444-111-1110 110A Smyther355

The database example provided here reflects the faculty listing for a school of nursing. The table that contains the data is identified by the name “Faculty.” The faculty members are each categorized by the following fields (columns): Last Name, First Name, Department Affiliation, Office Phone Number, Office Location, and UserID. Each individual faculty member’s information is a record (tuple or row).

Using the SQL command SELECT, all of the records in the “Faculty” table can be selected:

SELECT*FROM Faculty This command would SELECT all (*) of the records FROM the table known

as FACULTY. The asterisk (*) is used to select all of the columns.

Department Collaboration and Exchange of Knowledge and Information The implementation of systems within health care is the responsibility of many peo- ple and departments. All systems require a partnership of collaboration and knowl- edge sharing to implement and maintain successful standards of care. Collaboration is the sharing of ideas and experiences for the purposes of mutual understanding and learning. Knowledge exchange is the product of collaboration when sharing an un- derstanding of information promotes learning from past experiences to make better future decisions.

Depending on the type of project, collaboration may occur at many different levels within an organization. At an administrative level, collaboration among key stakeholders is critical to the success of any project. Stakeholders have the most responsibility for completing the project. They have the greatest influence in the overall design of the system, and ultimately they are the people who are most impacted by a system implementation. Together with the organizational ex- ecutive team, stakeholders collaborate on the overall budget and time frame for a system implementation.

Collaboration may also occur among the various departments impacted by the system. These groups frequently include representatives from information technology, clinical specialty areas, support services, and software vendors. Once a team is assembled, it defines the objectives and goals of the system. The team members work strategically to align their goals with the goals of the organization where the system is to be used. The focus for these groups is on planning, resource management, tran- sitioning, and ongoing support of the system. Their collaboration determines the way in which the project is managed, the deliverables for the project, the individuals held accountable for the project, the time frame for the project, opportunities for process improvement using the system, and the means by which resources are allocated to support the system.

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From collaboration comes the exchange of information and ideas through knowledge sharing. Specialists exchange knowledge within their respective areas of expertise to ensure that the system works for an entire organization. From one another, they learn requirements that make the system successful. This exchange of ideas is what makes healthcare information systems so valuable. A multidisciplinary approach ensures that systems work in the com- plex environment of healthcare organizations that have diverse and complex patient populations.

Summary The integration of technology within healthcare organizations offers limitless possibilities. As new types of systems emerge, clinicians will become smarter and more adept at incorporating these tools into their daily practice. Success will be achieved when health care incorporates technology systems in a way that they are not viewed as separate tools to support healthcare practices, but rather as neces- sary instruments to provide health care. Patients, too, will become savvier at us- ing healthcare information systems as a means of communication and managing their personal and preventive care. In the future, these two mindsets will become expectations for health care and not simply a high-tech benefit, as they are often viewed today.

Ultimately, it is not the type of systems adopted that is important, but rather the method in which they are put into practice. In an ideal world, robust and transparent information technologies will support clinical and administrative functions and pro- mote safe, quality, and cost-effective care.

Summary 203


1. Which type of technology exists today that could be converted into new types of information systems to be used in health care?

2. How could collaboration and knowledge sharing at a single organization be used to help individuals preparing for information technology at a different facility?

3. Explore the administrative information systems and their applications in your healthcare organization. a. What are the main systems used? b. How is data shared among systems? c. What examples of functional, structural, and semantic interoperability can

you identify?

References Agency for Healthcare Research and Quality (AHRQ) Patient Safety Network. (2015). Patient

safety primers: Medication errors. Retrieved from http://psnet.ahrq.gov/primer .aspx?primerID=23

Case Management Society of America (CMSA). (2014, September 12). Health IT survey. Retrieved from http://www.cmsa.org/Individual/NewsEvents/2010HealthITSurvey /tabid/539/Default.aspx

Centers for Medicare and Medicaid Services (CMS). (2015). Accountable care organizations. Retrieved from https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/ACO /index.html

Elder, K., Wiltshire, J., Rooks, R., BeLue, R., & Gary, L. (2010, Summer). Health information technology and physician career satisfaction. Perspectives in Health Information Management, 1–18. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 2118694921).

Fichman, R. G., Kohli, R., & Krishnan, R. (2011). Editorial overview: The role of information systems in healthcare; current research and future trends. Information Systems Research, 22(3), 419–428. doi:10.1287/isre.1110.0382

Gordon, C. R., Rezzadeh, K. S., Li, A., Vardanian, A., Zelken, J., Shores, J. T., . . . Jarrahy, R. (2015). Digital mobile technology facilitates HIPAA-sensitive perioperative messaging, improves physician-patient communication, and streamlines patient care. Patient Safety In Surgery, 9(1), 1–7. doi:10.1186/s13037-015-0070-9

Hassett, M., & Thede, L. (2003). Information in practice: Clinical information systems. In B. Cunningham (Ed.), Informatics and nursing opportunities and challenges (2nd ed., rev., pp. 222–239). Philadelphia, PA: Lippincott Williams & Wilkins.

Healthcare Information and Management Systems Society (HIMSS). (2013). Definition of interoperability. Retrieved from http://www.himss.org/library/interoperability-standards /what-is-interoperability

Healthcare Information and Management Systems Society (HIMSS). (2015). ONC releases final interoperability roadmap. Retrieved from http://www.himss.org/news /onc-releases-final-interoperability-roadmap?ItemNumber=44779

Indiana University. (2010). University information technology services knowledge base: What is SQL? Retrieved from http://kb.iu.edu/data/ahux.html

Kohn, L. T., Corrigan, J., Donaldson, M. S., & Institute of Medicine (U.S.). Committee on Quality of Health Care in America. (2000). To err is human: Building a safer health system. Washington, DC: National Academy Press.

Mishra, N., Sharma, S. & Pandey, A. (2013). High performance cloud data mining algorithm and data mining in clouds. IOSR Journal of Computer Engineering, 8(4), 54–61. Retrieved from http://www.iosrjournals.org/iosr-jce/papers/Vol8-Issue4/I0845461 .pdf

National Consortium for Health Science Education. (2012). Health informatics pathway standards and accountability criteria. Retrieved from http://www.healthscienceconsortium .org/wp-content/uploads/2015/07/Health-Informatics-Standards.pdf

Office of the National Coordinator for Health Information Technology (ONC). (2015). Connecting health and care for the nation: A shared nationwide interoperability roadmap. Final version 1.0. Retrieved from https://www.healthit.gov/sites/default/files/hie -interoperability/nationwide-interoperability-roadmap-final-version-1.0.pdf

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Rojas, N. (2015). Healthcare industry finds new solutions to big data storage challenges. Data Science Central. Retrieved from http://www.datasciencecentral.com/profiles/blogs/healthcare -industry-finds-new-solutions-to-big-data-storage

University of California at San Diego. (2010). Data warehouse terms. Retrieved from http:// blink.ucsd.edu/technology/help-desk/queries/warehouse/terms.html#s

W3Schools.com. (2010). Introduction to SQL. Retrieved from http://www.w3schools.com/SQL /sql_intro.asp

References 205

Key Terms » Cognitive task

analysis » Cognitive

walkthrough » Cognitive work

analysis » Earcons

» Ergonomics » Field study » Gulf of evaluation » Gulf of execution » Heuristic evaluation » Human–computer


» Human factors » Human–technology

interaction » Human–technology

interface » Mapping

» Situational awareness

» Task analysis » Usability » Workarounds

1. Describe the human–technology interface. 2. Explore human–technology interface problems.

3. Reflect on the future of the human–technology interface.


Introduction One of this chapter’s authors stayed in a new hotel on the outskirts of London. When she entered her room, she encountered three wall-mounted light switches in a row, but with no indication of which lights they operated. In fact, the mapping of switches to lights was so peculiar that she was more often than not surprised by the light that came on when she pressed a particular switch. One might conclude that the author had a serious problem, but she prefers to attribute her difficulty to poor design.

When these kinds of technology design issues surface in health care, they are more than just an annoyance. Poorly designed technology can lead to errors, lower productivity, or even the removal of the system (Alexander & Staggers, 2009). Unfortunately, as more and more kinds of increasingly complex health information technology applications are integrated, the problem becomes even worse (Johnson, 2006). However, nurses are very creative and, if at all possible, will design workarounds that allow them to circumvent troublesome technology. However, workarounds are only a Band-Aid; they are not a long-term solution.

In his classic book The Psychology of Everyday Things, Norman (1988) argued that life would be a lot simpler if people who built the things that others encounter (such as light switches) paid more attention to how they would be used. At least one everyday thing meets Norman’s criteria for good design: the scythe. Even people who have never encountered one will pick up a scythe in the manner needed to use it because the design makes only one way feasible. The scythe’s design fits perfectly with its intended use and a human user. Would it not be great if all technology were so well fit to human use? In fact, this is not such a far-fetched idea. Scientists and engineers are making excellent strides in understanding human–technology interface problems and proposing solutions to them.

As you read through this chapter, reflect on the everyday items you use. What makes them easy or difficult to use? Is it evident that the developer

The Human–Technology Interface Dee McGonigle, Kathleen Mastrian, and Judith A. Effken



thought about how they would be used to facilitate their design and function? Next, turn your attention to the technologies you use. Is it evident that the developer thought about how the technology would be used to facilitate its design and function? Think about your smartphone. How easy is it to hold your smartphone? Is it intuitive and easy to access and use? What improvements would you make? Does the electronic health record (EHR) system you use support your workflow and patient needs? Do you use workarounds to avoid items that you feel should not be there or are not needed at the time of entry? Do you think that the developer understood you, as the user, or did not realize how their technology tool would be used? By the end of this chapter, you should be able to critically examine the human–technology interfaces currently avail- able in health care and describe models, strategies, and exemplars for improving inter- faces during the analysis, design, and evaluation phases of the development life cycle.

The Human–Technology Interface What is the human–technology interface? Broadly speaking, anytime a human uses technology, some type of hardware or software enables and supports the interaction. It is this hardware and software that defines the interface. The array of light switches described previously was actually an interface (although not a great one) between the lighting technology in the room and the human user.

In today’s healthcare settings, one encounters a wide variety of human–technology interfaces. Those who work in hospitals may use bar-coded identification cards to log their arrival time into a human resources management system. Using the same cards, they might log into their patients’ EHR, access their patient’s drugs from a drug administration system, and even administer the drugs using bar-coding technol- ogy. Other examples of human–technology interfaces one might encounter include a defibrillator, a patient-controlled analgesia (PCA) pump, any number of physiologic monitoring systems, electronic thermometers, and telephones and pagers. According to Rice and Tahir (2014),

[R]ecent studies have found that rapid implementation of new medical tech- nology—electronic health records, patient monitoring devices, surgical robots and other tools—can lead to adverse patient events when it is not thoughtfully integrated into workflow. The right processes require under- standing the devices and the users. Testing in controlled environments often does not adequately consider the “human factor,” or how people interact with technology in high-pressure, real-life situations. (p. 12)

The human interfaces for each of these technologies are different and can even differ among different brands or versions of the same device. For example, to enter data into an EHR, one might use a keyboard, a light pen, a touch screen, or voice. Healthcare technologies may present information via computer screen, printer, or smartphone. Patient data might be displayed in the form of text, images (e.g., the results of a brain scan), or even sound (an echocardiogram); in addition, the infor- mation may be arrayed or presented differently, based on roles and preferences. Some human–technology interfaces mimic face-to-face human encounters. For example, faculty members are increasingly using videoconferencing technology to

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communicate with their students. Similarly, telehealth allows nurses to use telecom- munication and videoconferencing software to communicate more effectively and more frequently with patients at home by using the technology to monitor patients’ vital signs, supervise their wound care, or demonstrate a procedure. According to Gephart and Effken (2013), “The National eHealth Collaborative Technical Expert Panel recommends fully integrating patient-generated data (e.g., home monitoring of daily weights, blood glucose, or blood pressure readings) into the clinical workflow of healthcare providers” (para. 3). Telehealth technology has fostered other virtual interfaces, such as system-wide intensive care units in which intensivists and specially trained nurses monitor critically ill patients in intensive care units, some of whom may be in rural locations. Sometimes telehealth interfaces allow patients to interact with a virtual clinician (actually a computer program) that asks questions, provides social support, and tailors education to identify patient needs based on the answers to screening questions. These human–technology interfaces have been remarkably successful; sometimes patients even prefer them to live clinicians.

Human–technology interfaces may present information using text, numbers, images, icons, or sound. Auditory, visual, or even tactile alarms may alert users to important information. Users may interact with (or control) the technology via key- boards, digital pens, voice activation, or even touch.

A small, but growing, number of clinical and educational interfaces rely heavily on tactile input. For example, many students learn to access an intravenous site using virtual technology. Other, more sophisticated virtual reality applications help physi- cians learn to do endoscopies or practice complex surgical procedures in a safe envi- ronment. Still others allow drug researchers to design new medications by combining virtual molecules (here, the tactile response is quite different for molecules that can be joined from those that cannot). In each of these training environments, accurately de- picting tactile sensations is critical. For example, feeling the kind and amount of pres- sure required to penetrate the desired tissues, but not others, is essential to a realistic and effective learning experience.

The Human–Technology Interface 209

© Thomas Andreas/Shutterstock

The growing use of large databases for research has led to the design of novel hu- man–technology interfaces that help researchers visualize and understand patterns in the data that generate new knowledge or lead to new questions. Many of these inter- faces now incorporate multidimensional visualizations, in addition to scatter plots, histograms, or cluster representations (Vincent, Hastings-Tolsma, & Effken, 2010). Some designers, such as Quinn (the founder of the Design Rhythmics Sonification Re- search Laboratory at the University of New Hampshire) and Meeker (2000), use vari- ations in sound to help researchers hear the patterns in large datasets. In Quinn and Meeker’s (2000) “climate symphony,” different musical instruments, tones, pitches, and phrases are mapped onto variables, such as the amounts and relative concentra- tions of minerals, to help researchers detect patterns in ice core data covering more

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© Innocenti/Cultura/Getty

© Carlos Amarillo/Shutterstock

than 110,000 years. Climate patterns take centuries to emerge and can be difficult to detect. The music allows the entire 110,000 years to be condensed into just a few minutes, making detection of patterns and changes much easier.

The human–technology interface is ubiquitous in health care and takes many forms. A look at the quality of these interfaces follows. Be warned: It is not always a pretty picture.

The Human–Technology Interface Problem In The Human Factor, Vicente (2004) cited the many safety problems in health care identified by the Institute of Medicine’s (1999) report and noted how the technology (defined broadly) used often does not fit well with human characteristics. As a case in point, Vicente described his own studies of nurses’ PCA pump errors. Nurses made the errors, in large part, because of the complexity of the user interface, which required as many as 27 steps to program the device. Vicente and his colleagues devel- oped a PCA in which programming required no more than 12 steps. Nurses who used it in laboratory experiments made fewer errors, programmed drug delivery faster, and reported lower cognitive workloads compared to the commercial device. Further evi- dence that human–technology interfaces do not work as well as they might is evident in the following events.

Doyle (2005) reported that when a bar-coding medication system interfered with their workflow, nurses devised workarounds, such as removing the armband from the patient and attaching it to the bed, because the bar-code reader failed to interpret bar codes when the bracelet curved tightly around a small arm. Koppel et al. (2005) reported that a widely used computer-based provider order entry (CPOE) system meant to decrease medication errors actually facilitated 22 types of errors because the information needed to order medications was fragmented across as many as 20 screens, available medication dosages differed from those the physicians expected, and allergy alerts were triggered only after an order was written.

Han et al. (2005) reported increased mortality among children admitted to Children’s Hospital in Pittsburgh after CPOE implementation. Three reasons were cited for this unexpected outcome. First, CPOE changed the workflow in the emer- gency room. Before CPOE, orders were written for critical time-sensitive treatment based on radio communication with the incoming transport team before the child arrived. After CPOE implementation, orders could not be written until the patient arrived and was registered in the system (a policy that was later changed). Second, entering an order required as many as 10 clicks and took as long as 2 minutes; moreover, computer screens sometimes froze or response time was slow. Third, when the team changed its workflow to accommodate CPOE, face-to-face contact among team members diminished. Despite the problems with study methods identified by some of the informatics community, there certainly were serious human–technology interface problems.

In 2005, a Washington Post article reported that Cedars-Sinai Medical Center in Los Angeles had shut down a $34 million system after 3 months because of the medical staff’s rebellion. Reasons for the rebellion included the additional time it took to complete the structured information forms, failure of the system to recognize

The Human–Technology Interface Problem 211

misspellings (as nurses had previously done), and intrusive and interruptive auto- mated alerts (Connolly, 2005). Even though physicians actually responded appro- priately to the alerts, modifying or canceling 35% of the orders that triggered them, designers had not found the right balance of helpful-to-interruptive alerts. The system simply did not fit the clinicians’ workflow.

Such unintended consequences (Ash, Berg, & Coiera, 2004) or unpredictable outcomes (Aarts, Doorewaard, & Berg, 2004) of healthcare information systems may be attributed, in part, to a flawed implementation process, but there were clearly also human–technology interaction issues. That is, the technology was not well matched to the users and the context of care. In the pediatric case, a system developed for medi- cal–surgical units was implemented in a critical care unit.

Human–technology interface problems are the major cause of as many as 87% of all patient monitoring incidents (Walsh & Beatty, 2002). It is not always that the tech- nology itself is faulty. In fact, the technology may perform flawlessly, but the interface design may lead the human user to make errors (Vicente, 2004).

Rice and Tahir (2014) reported on two errors that remind us we still have a long way to go to ensure patient safety: In 2011, a pop-up box on a digital blood glucose reader was misread and the patient was given too much insulin, sending her into a diabetic coma; in 2013, a patient did not receive his psychiatric medicine for almost 3 weeks because the pharmacy’s computer system was set to automatically discon- tinue orders for certain drugs, and there was no alert built in to notify the team providing care to this patient that the drug was suspended. The real issue is that the healthcare personnel–technology interfaces continue to cause these adverse events and near-misses. It is important to remember that it is not only a technology or human interface issue. Many of these problems occur when new technology is introduced or existing technology is modified. In addition, we must examine how the technology tools are tested, how the human users are prepared for their use, and how the tools are integrated into the care delivery process (Rice & Tahir, 2014).

Improving the Human–Technology Interface Much can be learned from the related fields of cognitive engineering, human factors, and ergonomics (Figures 11-1 and 11-2) about how to make interfaces more compat- ible with their human users and the context of care. Each of these areas of study is multidisciplinary and integrates knowledge from multiple disciplines (e.g., computer science, engineering, cognitive engineering, psychology, and sociology).

These areas are also concerned with health issues arising from computer and other technology use. Longo and Reese (2014) reminded us that

Nearly 20 years ago, the American Optometric Association termed computer vision syndrome (CVS) as the complex of eye and vision problems related to near work experienced while using a computer. CVS symptoms reflect the current broad diagnosis of asthenopia (ICD-9, 368.13) [2017 ICD-10-CM H53.149] also referred to as eyestrain. Symptoms include: fatigue, blurred distal or proximal vision, headache, dry or irritated eyes, neck and/or back- aches, blurred near vision and diplopia (double vision). (p. 8)

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Improving the Human–Technology Interface 213

Figure 11-2 Human Factors and Ergonomics, Continued

Soft incoming light

Thighs parallel to floor

Adjustable seat

Display at eye level or slightly below

18 to 28 inches

Keyboard at elbow height

Feet flat on floor or foot rest

Figure 11-1 Human Factors and Ergonomics

Job Performance


External Distractions


Personal Issues

Longo and Reese described how to prevent computer vision syndrome. One of the best ways to help your eyes is to remember to look 20 feet away from your screen every 20 minutes for a minimum of 20 seconds. With the increased smartphone use, we are seeing neck issues caused by the tilt of the head (with the chin on the chest) while looking down at the smartphone or other handheld device. You should hold your phone up so that you are keeping your neck and eyes aligned properly with the device’s screen for more comfortable viewing and interactions. We must all be aware of our posture and how our work areas are set up when using our computers, smart- phones, tablets, and any other devices that consume a great deal of our time during our work or personal hours.

Effken (2016) proposed the ecological approach to interface design to help us realize a more meaningful EHR. This approach borrowed from a small field of psy- chology, ecological psychology, which “emerged after the 3-Mile Island nuclear fiasco to allow complex processes (like nuclear power plants) to be more easily and safely controlled by operators. Ecological displays subsequently have enhanced the control of airplanes, bottling plants—and even nuclear power plants. In the 1990s, the approach began to be extended to the complexities of healthcare” (Effken, para. 2). Ecological displays help the user identify deviations from normal physical or physi- ological processes. According to Effken,

Given the current pressure to achieve meaningful use of the EHR and the availability of new, more flexible technology, this seems like an ideal time for informaticists (and nurse informaticists, in particular) to consider seri- ously how the ecological approach might be applied to make the meaning of the EHR’s data more transparent to clinician and patient users, as well as to make clear the value proposition of various treatments. (para. 8)

It is evident that users and clinicians need the technology and interfaces neces- sary to quickly comprehend the multiple discrete data that are contained in distinct parts of the EHR. “Because these are exactly the kind of complex problems that they were developed to solve, the analysis and design approaches derived from ecological psychology are worth examining further as we attempt to derive a more meaningful EHR” (Effken, 2016, para. 8).

Over the years, three axioms have evolved for developing effective human– computer interactions (Staggers, 2003): (1) Users must be an early and continuous focus during interface design; (2) the design process should be iterative, allowing for evaluation and correction of identified problems; and (3) formal evaluation should take place using rigorous experimental or qualitative methods. These axioms still apply today and, even after all of these years, are often not followed.

Axiom 1: Users Must Be an Early and Continuous Focus During Interface Design Rubin (1994) used the term user-centered design to describe the process of designing products (e.g., human–technology interfaces) so that users can carry out the tasks needed to achieve their goals with “minimal effort and maximal efficiency” (p. 10).

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Thus, in user-centered design, the end user is emphasized. This is still a focus of human–technology interface design today.

Vicente (2004) argued that technology should fit human requirements at five levels of analysis (physical, psychological, team, organizational, and political). Physi- cal characteristics of the technology (e.g., size, shape, or location) should conform to the user’s size, grasp, and available space. Information should be presented in ways that are consistent with known human psychological capabilities (e.g., the number of items that can be remembered is seven plus or minus two). In addition, systems should conform to the communication, workflow, and authority structures of work teams; to organizational factors, such as culture and staffing levels; and even to politi- cal factors, such as budget constraints, laws, or regulations.

A number of analysis tools and techniques have been developed to help designers better understand the task and user environment for which they are designing. Discussed next are task analysis, cognitive task analysis, and cognitive work analysis (CWA).

Task analysis examines how a task must be accomplished. Generally, analysts describe the task in terms of inputs needed for the task, outputs (what is achieved by the task), and any constraints on actors’ choices on carrying out the task. Analysts then lay out the sequence of temporally ordered actions that must be carried out to complete the task in flowcharts (Vicente, 1999). A worker’s tasks must be analyzed. Task analysis is very useful in defining what users must do and which functions might be distributed between the user and technology (U.S. Department of Health and Hu- man Services, 2013). Cognitive task analysis usually starts by identifying, through inter- views or questionnaires, the particular task and its typicality and frequency. Analysts then may review the written materials that describe the job or are used for training and determine, through structured interviews or by observing experts perform the task, which knowledge is involved and how that knowledge might be represented. Cognitive task analysis can be used to develop training programs. Zupanc and col- leagues (2015) reported on the use of cognitive task analysis techniques to develop a framework from which a colonoscopy training program could be designed. “Task analysis methods (observation, a think-aloud protocol and cued-recall) and subse- quent expert review were employed to identify the competency components exhibited by practicing endoscopists with the aim of providing a basis for future instructional design” (Zupanc et al., p. 10). The resulting colonoscopy competency framework consisted of “twenty-seven competency components grouped into six categories: clin- ical knowledge; colonoscope handling; situation awareness; heuristics and strategies; clinical reasoning; and intra and inter-personal” (Zupanc et al., p. 10).

Cognitive work analysis was developed specifically for the analysis of complex, high- technology work domains, such as nuclear power plants, intensive care units, and emergency departments, where workers need considerable flexibility in responding to external demands (Burns & Hajdukiewicz, 2004; Vicente, 1999). A complete CWA includes five types of analysis: (1) work domain, (2) control tasks, (3) strategies, (4) social–organizational, and (5) worker competencies. The work domain analysis describes the functions of the system and identifies the information that users need to accomplish their task goals. The control task analysis investigates the control struc- tures through which the user interacts with or controls the system. It also identifies

Improving the Human–Technology Interface 215

which variables and relations among variables discovered in the work domain analysis are relevant for particular situations so that context-sensitive interfaces can present the right information (e.g., prompts or alerts) at the right time. The strategies analysis looks at how work is actually done by users to facilitate the design of appropriate human–computer dialogues. The social–organizational analysis identifies the respon- sibilities of various users (e.g., doctors, nurses, clerks, or therapists) so that the system can support collaboration, communication, and a viable organizational structure. Finally, the worker competencies analysis identifies design constraints related to the us- ers themselves (Effken, 2002).

Specialized tools are available for the first three types of CWA (Vicente, 1999). Analysts typically borrow tools (e.g., ethnography) from the social sciences for the two remaining types. Hajdukiewicz, Vicente, Doyle, Milgram, and Burns (2001) used CWA to model an operating room environment. Effken (2002) and Effken et al. (2001) used CWA to analyze the information needs for an oxygenation manage- ment display for an ICU. Other examples of the application of CWA in health care are described by Burns and Hajdukiewicz (2004) in their chapter on medical systems (pp. 201–238). Ashoon et al. (2014) used team CWA to reveal the interactions of the healthcare team in the context of work models in a birthing unit. They felt that team CWA enhances CWA in complex environments, such as health care, that require ef- fective teamwork because it reveals additional constraints relevant to the workings of the team. The information gleaned about the teamwork could be used for systems design applications.

Axiom 2: The Design Process Should Be Iterative, Allowing for Evaluation and Correction of Identified Problems Today, both principles and techniques for developing human–technology interfaces that people can use with minimal stress and maximal efficiency are available. An excellent place to start is with Norman’s (1988, pp. 188–189) principles:

1. Use both knowledge in the world and knowledge in the head. In other words, pay attention not only to the environment or to the user, but to both, and to how they relate. By using both, the problem actually may be simplified.

2. Simplify the structure of tasks. For example, reduce the number of steps or even computer screens needed to accomplish the goal.

3. Make things visible: Bridge the gulf of execution and the gulf of evaluation. Users need to be able to see how to use the technology to accomplish a goal (e.g., which buttons does one press and in which order to program this PCA?); if they do, then designers have bridged the gulf of execution. They also need to be able to see the effects of their actions on the technology (e.g., if a nurse practitioner prescribes a drug to treat a certain condition, the actual patient response may not be perfectly clear). This bridges the gulf of evaluation.

4. Get the mappings right. Here, the term mapping is used to describe how environmental facts (e.g., the order of light switches or variables in a physi- ologic monitoring display) are accurately depicted by the information presentation.

216 CHAPTER 11 The Human–Technology Interface

5. Exploit the power of constraints, both natural and artificial. Because of where the eyes are located in the head, humans have to turn their heads to see what is happening behind them; however, that is not true of all animals. As the loca- tion of one’s eyes constrains what one can see, so also do physical elements, social factors, and even organizational policy constrain the way tasks are ac- complished. By taking these constraints into account when designing technol- ogy, it can be made easier for humans use.

6. Design for error. Mistakes happen. Technology should eliminate predictable errors and be sufficiently flexible to allow humans to identify and recover from unpredictable errors.

7. When all else fails, standardize. To get a feel for this principle, think how dif- ficult it is to change from a Macintosh to a Windows environment or from the iPhone operating system to Android.

Kirlik and Maruyama (2004) described a real-world human–technology interface that follows Norman’s principles. In their classic analogy, the authors observed how a busy expert short-order cook strategically managed to grill many hamburgers at the same time, but each to the customer’s desired level of doneness. The cook put those burgers that were to be well-done on the back and far right portion of the grill, those to be medium well-done in the center of the grill, and those to be rare at the front of the grill, but farther to the left. The cook moved all burgers to the left as grilling pro- ceeded and turned them over during their travel across the grill. Everything the cook needed to know was available in this simple interface. As a human–technology inter- face, the grill layout was elegant. The interface used knowledge housed both in the environment and in the expert cook’s head; also, things were clearly visible, both in the position of the burgers and in the way they were moved. The process was clearly and effectively standardized, with built-in constraints. What might it take to create such an intuitive human–technology interface in health care?

Several useful books have been written about effective interface design (e.g., Burns & Hajdukiewicz, 2004; Cooper, 1995; Mandel, 1997; McKay, 2013; Wigdor & Wixon, 2011). In addition, a growing body of research is exploring new ways to present clini- cal data that might facilitate clinicians’ problem identification and accurate treatment (Agency for Healthcare Research and Quality, 2010). Just as in other industries, health care is learning that big data can provide big insights if it can be visualized, accessed, and meaningful (Intel IT Center, 2013). Often, designers use graphical objects to show how variables relate. The first to do so were likely Cole and Stewart (1993), who used changes in the lengths of the sides and area of a four-sided object to show the relation- ship of respiratory rate to tidal volume. Other researchers have demonstrated that histograms and polygon displays are better than numeric displays for detecting changes in patients’ physiologic variables (Gurushanthaiah, Weinger, & Englund, 1995). When Horn, Popow, and Unterasinger (2001) presented physiologic data via a single circu- lar object with 12 sectors (where each sector represented a different variable), nurses reported that it was easy to recognize abnormal conditions, but difficult to compre- hend the patient’s overall status. This kind of graphical object approach has been most widely used in anesthesiology, where a number of researchers have shown improved

Improving the Human–Technology Interface 217

clinician situational awareness or problem detection time by mapping physiologic vari- ables onto display objects that have meaningful shapes, such as using a bellows-like ob- ject to represent ventilation (Agutter et al., 2003; Blike, Surgenor, Whallen, & Jensen, 2000; Michels, Gravenstein, & Westenskow, 1997; Zhang et al., 2002).

Effken (2006) compared a prototype display that represented physiologic data in a structured pictorial format with two bar graph displays. The first bar graph display and the prototype both presented data in the order that experts were observed to use them. The second bar graph display presented the data in the way that nurses col- lected them. In an experiment in which resident physicians and novice nurses used simulated drugs to treat observed oxygenation management problems using each dis- play, residents’ performance was improved with the displays ordered as experts used them, but nurses’ performance was not improved. Instead, nurses performed better when the variables were ordered as they were used to collecting them, demonstrating the importance of understanding user roles and the tasks they need to accomplish.

Data also need to be represented in ways other than visually. Gaver (1993) pro- posed that because ordinary sounds map onto familiar events, they could be used as icons to facilitate easier technology navigation and use and to provide continuous background information about how a system is functioning. In health care, auditory displays have been used to provide clinicians with information about patients’ vital signs (e.g., in pulse oximetry), such as by altering volume or tone when a significant change occurs (Sanderson, 2006).

Admittedly, auditory displays are probably more useful for quieter areas of the hospital, such as the operating room. Perhaps that is why researchers have most fre- quently applied the approach in anesthesiology. For example, Loeb and Fitch (2002) reported that anesthesiologists detected critical events more quickly when auditory information about heart rate, blood pressure, and respiratory parameters was added to a visual display. Auditory tones also have been combined as earcons to represent relationships among data elements, such as the relationship of systolic to diastolic blood pressure (Watson & Gill, 2004).

Axiom 3: Formal Evaluation Should Take Place Using Rigorous Experimental or Qualitative Methods Perhaps one of the highest accolades that any interface can achieve is to say that it is transparent. An interface becomes transparent when it is so easy to use that users no longer think about it, but only about the task at hand. For example, a transparent clini- cal interface would enable clinicians to focus on patient decisions rather than on how to access or combine patient data from multiple sources. In Figure 11-3, instead of the nurse interacting with the computer, the nurse and the patient interact through the technology interface. The more transparent the interface, the easier the interaction should be.

Usability is a term that denotes the ease with which people can use an interface to achieve a particular goal. Usability of a new human–technology interface needs to be evaluated early and often throughout its development. Typical usability indicators in- clude ease of use, ease of learning, satisfaction with using, efficiency of use, error tol- erance, and fit of the system to the task (Staggers, 2003). Some of the more commonly used approaches to usability evaluation are discussed next.

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Surveys of Potential or Actual Users Chernecky, Macklin, and Waller (2006) assessed cancer patients’ preferences for website design. Participants were asked their preferences for a number of design characteristics, such as display color, menu buttons, text, photo size, icon metaphor, and layout, by se- lecting on a computer screen their preferences for each item from two or three options.

Focus Groups Typically used at the very start of the design process, focus groups can help the de- signer better understand users’ responses to potential interface designs and to content that might be included in the interface.

Cognitive Walkthrough In a cognitive walkthrough, evaluators assess a paper mockup, working prototype, or completed interface by observing the steps users are likely to take to use the interface to accomplish typical tasks. This analysis helps designers determine how understand- able and easy to learn the interface is likely to be for these users and the typical tasks (Wharton, Rieman, Lewis, & Polson, 1994).

Heuristic Evaluation A heuristic evaluation has become the most popular of what are called “discount usability evaluation” methods. The objective of a heuristic evaluation is to detect problems early in the design process, when they can be most easily and economically corrected. The methods are termed “discount” because they typically are easy to do, involve fewer than 10 experts (often experts in relevant fields such as human– computer technology or cognitive engineering), and are much less expensive than

Improving the Human–Technology Interface 219

Figure 11-3 Nurse–Patient Interaction Framework in Which the Technology Supports the Interaction Modified from Staggers, N., & Parks, P. L. (1993). Description and initial applications of the Staggers & Parks nurse–computer interaction framework. Computers in Nursing, 11, 282–290. Reprinted by permission of AMIA.

Information Exchange

Computer interface

Computer characteristics


Patient data

Patient status

Clinician characteristics

Clinician processes




other methods. They are called “heuristic” because evaluators assess the degree to which the design complies with recognized usability rules of thumb or principles (the heuristics), such as those proposed by Nielsen (1994) and available on his website (www.useit.com/papers/heuristic/heuristic_list.html).

For example, McDaniel and colleagues (2002) conducted a usability test of an interactive computer-based program to encourage smoking cessation by low-income women. As part of the initial evaluation, healthcare professionals familiar with the intended users reviewed the design and layout of the program. The usability test re- vealed several problems with the decision rules used to tailor content to users that were corrected before implementation.

Formal Usability Test Formal usability tests typically use either experimental or observational studies of actual users using the interface to accomplish real-world tasks. A number of research- ers use these methods. For example, Staggers, Kobus, and Brown (2007) conducted a usability study of a prototype electronic medication administration record. Partici- pants were asked to add, modify, or discontinue medications using the system. The time they needed to complete the task, their accuracy in the task, and their satisfac- tion with the prototype were assessed (the last criterion through a questionnaire). Al- though satisfaction was high, the evaluation also revealed design flaws that could be corrected before implementation.

Field Study In a field study, end users evaluate a prototype in the actual work setting just before its general release. For example, Thompson, Lozano, and Christakis (2007) evaluated the use of touch-screen computer kiosks containing child health–promoting informa- tion in several low-income, urban community settings through an online question- naire that could be completed after the kiosk was used. Most users found the kiosk easy to use and the information it provided easy to understand. Researchers also gained a better understanding of the characteristics of the likely users (e.g., 26% had never used the Internet and 48% had less than a high school education) and the in- formation most often accessed (television and media use, and smoke exposure).

Dykes and her colleagues (2006) used a field test to investigate the feasibility of using digital pen and paper technology to record vital signs as a way to bridge an organization from a paper to an electronic health record. In general, satisfaction with the tool increased with use, and the devices conformed well to nurses’ workflow. However, 8% of the vital sign entries were recorded inaccurately because of inac- curate handwriting recognition, entries outside the recording box, or inaccurate data entry (the data entered were not valid values). The number of modifications needed in the tool and the time that would be required to make those changes ruled out using the digital pen and paper as a bridging technology.

Ideally, every healthcare setting would have a usability laboratory of its own to test new software and technology in its own setting before actual implementation. However, this can be expensive, especially for small organizations. Kushniruk and Borycki (2006) developed a low-cost rapid usability engineering method for creating a portable usability laboratory consisting of video cameras and other technology that

220 CHAPTER 11 The Human–Technology Interface

one can take out of the laboratory into hospitals and other locations to test the tech- nology on site using as close to a real world environment as possible. This is a much more cost-effective and efficient solution and makes it possible to test all technologies before their implementation.

A Framework for Evaluation Ammenwerth, Iller, and Mahler (2006) proposed a fit between individuals, tasks, and technology (FITT) model that suggests that each of these factors be considered in designing and evaluating human–technology interfaces. It is not enough to con- sider only the user and technology characteristics; the tasks that the technology sup- ports must be considered as well. The FITT model builds on DeLone and McLean’s (1992) information success model, Davis’s (1993) technology acceptance model, and Goodhue and Thompson’s (1995) task technology fit model. A notable strength of the FITT model is that it encourages the evaluator to examine the fit between the various pairs of components: user and technology, task and technology, and user and task.

Johnson and Turley (2006) compared how doctors and nurses describe patient information and found that doctors emphasized diagnosis, treatment, and manage- ment, whereas the nurses emphasized functional issues. Although both physicians and nurses share some patient information, how they thought about patients differed. For that reason, an EHR needs to present information (even the same information) to the two groups in different ways.

Hyun, Johnson, Stetson, and Bakken (2009) used a combination of two models (technology acceptance model and task–technology fit model) to design and evaluate an electronic documentation system for nurses. To facilitate the design, they employed multiple methods, including brainstorming of experts, to identify design requirements. To evaluate how well the prototype design fit both task and user, nurses were asked to carry out specific tasks using the prototype in a laboratory setting, and then com- plete a questionnaire on ease of use, usefulness, and fit of the technology with their documentation tasks. Because the researchers engaged nurses at each step of the de- sign process, the result was a more useful and usable system.

Future of the Human–Technology Interface Increased attention to improving the human–technology interface through human factors approaches has already led to significant improvements in one area of health care: anesthesiology. Anesthesia machines that once had hoses that would fit into any delivery port now have hoses that can only be plugged into the proper port. Anesthesiologists have also been actively working with engineers to improve the computer interface through which they monitor their patients’ status and are among the leaders in investigating the use of audio techniques as an alternative way to help anesthesiologists maintain their situational awareness. As a result of these efforts, anesthesia-related deaths dropped from 2 in 20,000 to 1 in 200,000 in less than 10 years (Vicente, 2004). It is hoped that continued emphasis on human factors (Vi- cente, 2004) and user-centered design (Rubin, 1994) by informatics professionals and

Future of the Human–Technology Interface 221

human–computer interactions experts will have equally successful effects on other parts of the healthcare system. The increased amount of informatics research in this area is encouraging, but there is a long way to go.

A systematic review of clinical technology design evaluation studies (Alexander & Staggers, 2009) found 50 nursing studies. Of those, nearly half (24) evaluated effective- ness, fewer (16) evaluated satisfaction, and still fewer (10) evaluated efficiency. The evaluations were not systematic—that is, there was no attempt to evaluate the same system in different environments or with different users. Most evaluations were done in a laboratory, rather than in the setting where the system would be used. The authors argued for a broader range of studies that use an expanded set of outcome measures. For example, instead of looking at user satisfaction, evaluators should dig deeper into the design factors that led to the satisfaction or dissatisfaction. In addition, performance measures, such as diagnostic accuracy, errors, and correct treatment, should be used.

Rackspace, Brauer, and Barth (2013) reported on a social study of the human cloud formed in part by data collected from wearable technologies; they focused on assessing attitudes and “exploring how cloud computing is enabling this new genera- tion of smart devices” (p. 2). Today, smartphones, glasses, clothing, watches, cameras, and monitors for health or patient tracking, to name but a few devices, are available to this purpose.

222 CHAPTER 11 The Human–Technology Interface

Figure 11-4 Human Technology Interface and Task Completion




Human Technology Interface and Task Compltion

Task DemandsUser Ability

User Ability and/or Human Technology

Interface > Task Demands

Task Demands > User Ability and/or Human Technology


The additional technologies that are entering our lives on a daily basis can enhance or challenge our ability to complete both our activities of daily living and our professional tasks. As our home monitoring and patient technologies increase, the user’s (patient’s or nurse’s) ability to use the technology is paramount. No matter who is using the technology, the human–technology interface addresses the user’s ability and the technology’s functionality to complete the task demands (see Figure 11-4).

As our technologies continue to evolve, we are creating more design issues. The proliferation of smart devices and wearable technology brings new concerns related to human–technology interfaces. According to Madden (2013), wearable technologies are adding another wrinkle into the design process—namely, human behavior. How will someone use this technology? How will individuals behave with it on their per- son? How will they wear it? How and when will they enable and use it? Will others be able to detect the technologies (that is, will someone be able to wear Google Glass and take pictures or videos of other people’s actions), and will users be able to seam- lessly move among all of the capabilities of his or her wearable technologies? The human–technology interface must address these issues. There is a long way to go.

Summary There are at least three messages the reader should take away from the discussion in this chapter. First, if there is to be significant improvement in quality and safety outcomes in the United States through the use of information technology, the designs for human–technology interfaces must be radically improved so that the technol- ogy better fits human and task requirements. However, that improvement will be possible only if clinicians identify and report problems, rather than simply creating workarounds. That means that each clinician has a responsibility to participate in the design process and to report designs that do not work.

Second, a number of useful tools are currently available for the analysis, design, and evaluation phases of development life cycles. They should be used routinely by informatics professionals to ensure that technology better fits both task and user requirements.

Third, focusing on interface improvement using these tools has had a huge impact on patient safety in the area of anesthesiology and medication administration. With increased attention from informatics professionals and engineers, the same kind of

Summary 223


1. You are a member of a team that has been asked to evaluate a prototype smartphone-based application for calculating drug dosages. Based on what you know about usability testing, which kind of test (or tests) might you do and why?

2. Is there a human–technology interface that you have encountered that you think needs improvement? If you were to design a replacement, which analysis tech- niques would you choose? Why?

3. Which type of functionality and interoperability would you want from your smart- phone, watch, clothing, glasses, camera, and monitor? Provide a detailed response.

improvement should be possible in other areas regardless of the technologies actually employed there. In the ideal world, one can envision that every human–technology interface will be designed to enhance users’ workflow, will be as easy to use as bank- ing ATMs, and will be fully tested before its implementation in a setting that mirrors the setting where it will be used.

References Aarts, J., Doorewaard, H., & Berg, M. (2004). Understanding implementation: The case of

a computerized physician order entry system in a large Dutch university medical center. Journal of the American Medical Association, 11, 207–216.

Agency for Healthcare Research and Quality (AHRQ). (2010). Improving data collection across the health care system. Retrieved from http://www.ahrq.gov/research/findings/final -reports/iomracereport/reldata5.html

Agutter, J., Drews, F., Syroid, N., Westneskow, D., Albert, R., Strayer, D., . . . Weinger, M. (2003). Evaluation of graphic cardiovascular display in a high-fidelity simulator. Anesthesia and Analgesia, 97, 1403–1413.

Alexander, G., & Staggers, N. (2009). A systematic review of the designs of clinical technology findings and recommendations for future research. Advances in Nursing Science, 32(3), 252–279.

Ammenwerth, E., Iller, C., & Mahler, C. (2006). IT-adoption and the interaction of task, technology and individuals: A fit framework and a case study. BMC Medical Informatics and Decision Making, 6, 3.

Ash, J. S., Berg, M., & Coiera, E. (2004). Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. Journal of the American Medical Informatics Association, 11, 104–112.

Ashoon, M., Burns, C., d’Entremont, B., & Momtahan, K. (2014). Using team cognitive work analysis to reveal healthcare team interactions in a birthing unit. Ergonomics, 57(7), 973–986.

Blike, G. T., Surgenor, S. D., Whallen, K., & Jensen, J. (2000). Specific elements of a new hemodynamics display improves the performance of anesthesiologists. Journal of Clinical Monitoring & Computing, 16, 485–491.

Burns, C. M., & Hajdukiewicz, J. R. (2004). Ecological interface design. Boca Raton, FL: CRC Press.

Chernecky, C., Macklin, D., & Waller, J. (2006). Internet design preferences of patients with cancer. Oncology Nursing Forum, 33, 787–792.

Cole, W. G., & Stewart, J. G. (1993). Metaphor graphics to support integrated decision making with respiratory data. International Journal of Clinical Monitoring and Computing, 10, 91–100.

Connolly, C. (2005, March 21). Cedars-Sinai doctors cling to pen and paper. Washington Post, p. A01.

Cooper, A. (1995). About face: Essentials of window interface design. New York, NY: Hungry Minds, Inc.

Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man–Machine Studies, 38, 475–487.

DeLone, W. H., & McLean, E. (1992). Information systems success: The question for the dependent variable. Information Systems Research, 3(1), 60–95.

Doyle, M. (2005). Impact of the Bar Code Medication Administration (BCMA) system on medication administration errors. Unpublished doctoral dissertation, University of Arizona, Tucson, AZ.

224 CHAPTER 11 The Human–Technology Interface

Dykes, P. C., Benoit, A., Chang, F., Gallagher, J., Li, Q., Spurr, C., . . . Prater, M. (2006). The feasibility of digital pen and paper technology for vital sign data capture in acute care settings. In AMIA 2006 Symposium Proceedings (pp. 229–233). Washington, DC: American Medical Informatics Association.

Effken, J., Loeb, R., Johnson, K., Johnson, S., & Reyna, V. (2001). Using cognitive work analysis to design clinical displays. In V. L. Patel, R. Rogers, & R. Haux (Eds.), Proceedings of MedInfo-2001 (pp. 27–31). London, UK: IOS Press.

Effken, J. (2016, February). Proposed: An ecological interface design approach to a more meaningful EHR. Online Journal of Nursing Informatics (OJNI), 19(3). Retrieved from http://www.himss.org/proposed-ecological-interface-design-approach-more- meaningful-ehr

Effken, J. A. (2002). Different lenses, improved outcomes: A new approach to the analysis and design of healthcare information systems. International Journal of Medical Informatics, 65, 59–74.

Effken, J. A. (2006). Improving clinical decision making through ecological interfaces. Ecological Psychology, 18(4), 283–318.

Gaver, W. W. (1993). What in the world do we hear? An ecological approach to auditory event perception. Ecological Psychology, 5, 1–30.

Gephart, S., & Effken, J. (2013). Using health information technology to engage patients in their care. Online Journal of Nursing Informatics (OJNI), 17(3). Retrieved from http://ojni .org/issues/?p=2848

Goodhue, D. L., & Thompson, R. L. (1995). Task–technology fit and individual performance. MIS Quarterly, 19(2), 213–236.

Gurushanthaiah, K. I., Weinger, M. B., & Englund, C. E. (1995). Visual display format affects the ability of anesthesiologists to detect acute physiologic changes: A laboratory study employing a clinical display simulator. Anesthesiology, 83, 1184–1193.

Hajdukiewicz, J. R., Vicente, K. J., Doyle, D. J., Milgram, P., & Burns, C. M. (2001). Modeling a medical environment: An ontology for integrated medical informatics design. International Journal of Medical Informatics, 62, 79–99.

Han, Y. Y., Carcillo, J. A., Venkataraman, S. T., Clark, R. S. B., Watson, R. S., Nguyen, T., . . . Orr, R. (2005). Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics, 116, 1506–1512.

Horn, W., Popow, C., & Unterasinger, L. (2001). Support for fast comprehension of ICU data: Visualization using metaphor graphics. Methods in Informatics Medicine, 40, 421–424.

Hyun, S., Johnson, S. B., Stetson, P. D., & Bakken, S. (2009). Development and evaluation of nursing user interface screens using multiple methods. Journal of Biomedical Informatics, 42(6), 1004–1012.

Institute of Medicine. (1999). To err is human: Building a safer health system. Washington, DC: Author.

Intel IT Center. (2013). Big data visualization: Turning big data into big insights. Retrieved from http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/big -data-visualization-turning-big-data-into-big-insights.pdf

Johnson, C. M., & Turley, J. P. (2006). The significance of cognitive modeling in building healthcare interfaces. International Journal of Medical Informatics, 75(2), 163–172.

Johnson, C. W. (2006). Why did that happen? Exploring the proliferation of barely usable software in healthcare systems. Quality & Safety in Healthcare, 15(suppl 1), 176–181.

Kirlik, A., & Maruyama, S. (2004). Human–technology interaction and music perception and performance: Toward the robust design of sociotechnical systems. Proceedings of the IEEE, 92(4), 616–631.

References 225

Koppel, R., Metlay, J. P., Cohen, A., Abaluck, B., Localio, A. R., Kimmel, S. E., & Stron, B. (2005). Role of computerized physician order entry systems in facilitating medication errors. Journal of the American Medical Association, 293(10), 1197–1203.

Kushniruk, A. W., & Borycki, E. M. (2006). Low-cost rapid usability engineering: Designing and customizing usable healthcare information systems. Healthcare Quarterly (Toronto, Ont.), 9(40), 98–100, 102.

Loeb, R. G., & Fitch, W. T. (2002). A laboratory evaluation of an auditory display designed to enhance intraoperative monitoring. Anesthesia and Analgesia, 94, 362–368.

Longo, B., & Reese, C. (2014). Human–technology interface: Computers & vision health. Nevada RNformation, 23(1), 8–19.

Madden, S. (2013). With wearable tech like Google Glass, human behavior is now a design problem. Gigaom. Retrieved from http://gigaom.com/2013/06/15/with-wearable-tech-like -google-glass-human-behavior-is-now-a-design-problem

Mandel, T. (1997). The elements of user interface design. New York, NY: John Wiley & Sons. McDaniel, A., Hutchinson, S., Casper, G. R., Ford, R. T., Stratton, R., & Rembush, M. (2002).

Usability testing and outcomes of an interactive computer program to promote smoking cessation in low income women. In Proceedings AMIA 2002 (pp. 509–513). Washington, DC: American Medical Informatics Association.

McKay, E. (2013). UI is communication: How to design intuitive, user-centered interfaces by focusing on effective communication. New York, NY: Elsevier.

Michels, P., Gravenstein, D., & Westenskow, D. R. (1997). An integrated graphic data display improves detection and identification of critical events during anesthesia. Journal of Clinical Monitoring & Computing, 13, 249–259.

Nielsen, J. (1994). Heuristic evaluation. In J. Nielsen & R. L. Mack (Eds.), Usability inspection methods (pp. 25–62). New York, NY: John Wiley & Sons.

Norman, D. A. (1988). The psychology of everyday things. New York, NY: Basic Books. Quinn, M., & Meeker, L. (2000). Research set to music: The climate symphony and other

sonifications of ice core, radar, DNA, seismic and solar wind data. Retrieved from http:// www.drsrl.com/climate_paper.html

Rackspace, Brauer, C. & Barth, J. (2013). The human cloud: Wearable technology from novelty to productivity. A social study into the impact of wearable technology. Retrieved from http:// www.rackspace.co.uk/sites/default/files/whitepapers/The_Human_Cloud_-_June_2013.pdf

Rice, S. & Tahir, D. (2014). The human factor. Modern Healthcare, 44(33), 12–15. Rubin, J. (1994). Handbook of usability testing: How to plan, design, and conduct effective

tests. New York, NY: Wiley & Sons. Sanderson, P. (2006). The multimodal world of medical monitoring displays. Applied

Ergonomics, 37, 501–512. Staggers, N. (2003). Human factors: Imperative concepts for information systems in critical

care. AACN Clinical Issues, 14(3), 310–319. Staggers, N., Kobus, D., & Brown, C. (2007). Nurses’ evaluations of a novel design for an

electronic medication administration record. CIN: Computers, Informatics, Nursing, 25(2), 67–75.

Thompson, D. A., Lozano, P., & Christakis, D. A. (2007). Parent use of touchscreen computer kiosks for child health promotion in community settings. Pediatrics, 119(3), 427–434.

U.S. Department of Health and Human Services. (2013). Usability.gov: Task analysis. Retrieved from http://www.usability.gov/how-to-and-tools/methods/task-analysis.html

Vicente, K. (2004). The human factor. New York, NY: Routledge. Vicente, K. J. (1999). Cognitive work analysis: Toward safe, productive, and healthy

computer-based work. Mahwah, NJ: Lawrence Erlbaum Associates.

226 CHAPTER 11 The Human–Technology Interface

Vincent, D., Hastings-Tolsma, M., & Effken, J. (2010). Data visualization and large nursing datasets. Online Journal of Nursing Informatics, 14(2). Retrieved from http://ojni.org/14_2 /Vincent.pdf

Walsh, T., & Beatty, P. C. W. (2002). Human factor error and patient monitoring. Physiological Measurement, 23, R111–R132.

Watson, G., & Gill, T. (2004). Earcon for intermittent information in monitoring environments. In Proceedings of the 2004 Conference of the Computer–Human Interaction Special Interest Group of the Human Factors and Ergonomics Society of Australia (OzCHI2004), Wollonggong, New South Wales, November 22–24, 1994.

Wharton, C., Rieman, J., Lewis, C., & Polson, P. (1994). The cognitive walkthrough: A practitioner’s guide. In J. Nielsen & R. L. Mack (Eds.), Usability inspection methods (pp. 105–139). New York, NY: John Wiley & Sons.

Wigdor, D., & Wixon, D. (2011). Brave NUI world: Designing natural user interfaces for touch and gesture. New York, NY: Elsevier.

Zhang, Y., Drews, F. A., Westenskow, D. R., Foresti, S., Agutter, J., Burmedez, J. C., . . . Loeb, R. (2002). Effects of integrated graphical displays on situation awareness in anaesthesiology. Cognition, Technology & Work, 4, 82–90.

Zupanc, C., Burgess-Limerick, R., Hill, A., Riek, S., Wallis, G., Plooy, A., . . . Hewett, D. (2015). A competency framework for colonoscopy training derived from cognitive task analysis techniques and expert review. BMC Medical Education, 15, 1–11.

References 227

Key Terms » Antivirus software » Authentication » Baiting » Biometrics » Brute force attack » Confidentiality » Electronic protected

health information (EPHI)

» Firewall » Flash drives » Hackers

» Integrity » Intrusion detection

devices » Intrusion detection

system » Jump drives » Malicious code » Malicious insiders » Malware » Mask » Negligent insider » Network

» Network accessibility

» Network availability » Network security » Password » Phishing » Proxy server » Radio frequency

identification (RFID) » Ransomware » Scareware » Secure information

» Security breaches

» Shoulder surfing » Social

engineering » Spear phishing » Spyware » Thumb drives » Trojan horses » Viruses » Worms » Zero day attack

1. Assess processes for securing electronic informa- tion in a computer network.

2. Identify various methods of user authentication and relate authentication to security of a network.

3. Explain methods to anticipate and prevent typical threats to network security.


Introduction In addition to complying with federal HIPAA and HITECH guidelines regarding the privacy of patient information, healthcare systems need to be vigilant in the way that they secure information and manage network security. Mowry and Oakes (n.d.) discuss the vulnerability of electronic health records to data breaches. They suggest that as many as 77 per- sons could view a patient’s record during a hospital stay. It is critical for information technology (IT) policies and procedures to ensure appropri- ate access by clinicians and to protect private information from inappro- priate access. However, authentication procedures can be cumbersome and time consuming, thus reducing clinician performance efficiency.

Physicians spend on average 7 minutes per patient encounter, with nearly 2 minutes of that time being devoted to managing logins and ap- plication navigation. Likewise, an average major healthcare provider must deal with more than 150 applications—most requiring different user names and passwords—making it difficult for caregivers to navigate and receive contextual information. Healthcare organizations must strike the right balance, in terms of simplifying access to core clinical datasets while maxi- mizing the time providers can interact with patients without jeopardizing data integrity and security (Mowry & Oakes, n.d., para. 7).

This chapter explores use of information and processes for securing in- formation in a health system computer network.

Securing Network Information Typically, a healthcare organization has computers linked together to facilitate communication and operations within and outside the facility. This is commonly referred to as a network. The linking of computers together and to the outside world creates the possibility of a breach of network security and exposes the information to unauthorized use. With the advent of smart

Electronic Security Lisa Reeves Bertin, Kathleen Mastrian, and Dee McGonigle



devices, managing all of these risks has become a nightmare for some institutions’ security processes. In the past, stationary devices or computers resided within health- care facilities. Today, smart devices travel in and out of healthcare organizations with patients, family members, and other visitors, as well as employees—both staff and healthcare providers alike. According to Sullivan (2012), “Even as they promise bet- ter health and easier care delivery, wireless medical devices (MDs) carry significant security risks. And the situation is only getting trickier as more and more MDs come with commercial operating systems that are both Internet-connected and susceptible to attack” (para. 1).

The three main areas of secure network information are (1) confidentiality, (2) availability, and (3) integrity. An organization must follow a well-defined policy to ensure that private health information remains appropriately confidential. The confi- dentiality policy should clearly define which data are confidential and how those data should be handled. Employees also need to understand the procedures for releasing confidential information outside the organization or to others within the organization and know which procedures to follow if confidential information is accidentally or intentionally released without authorization. In addition, the organization’s confiden- tiality policy should contain consideration for elements as basic as the placement of monitors so that information cannot be read by passersby. Shoulder surfing, or watch- ing over someone’s back as that person is working, is still a major way that confiden- tiality is compromised.

Availability refers to network information being accessible when needed. This area of the policy tends to be much more technical in nature. An accessibility pol- icy covers issues associated with protecting the key hardware elements of the com- puter network and the procedures to follow in case of a major electric outage or Internet outage. Food and drinks spilled onto keyboards of computer units, drop- ping or jarring hardware, and electrical surges or static charges are all examples of ways that the hardware elements of a computer network may be damaged. In the case of an electrical outage or a weather-related disaster, the network administra- tor must have clear plans for data backup, storage, and retrieval. There must also be clear procedures and alternative methods of ensuring that care delivery remains largely uninterrupted.

Another way organizations protect the availability of their networks is to institute an acceptable use policy. Elements covered in such policy could include which types of activities are acceptable on the corporate network. For example, are employees permitted to download music at work? Restricting downloads is a very common way for organizations to prevent viruses and other malicious code from entering their net- works. The policy should also clearly define which activities are not acceptable and identify the consequences for violations.

The last area of information security is integrity. Employees need to have con- fidence that the information they are reading is true. To accomplish this, organi- zations need clear policies to clarify how data are actually inputted, determine who has the authorization to change such data, and track how and when data are changed. All three of these areas use authorization and authentication to enforce the corporate policies. Access to networks can easily be grouped into areas of

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authorization (e.g., users can be grouped by job title). For example, anyone with the job title of “floor supervisor” might be authorized to change the hours worked by an employee, whereas an employee with the title of “patient care assistant” cannot make such changes.

Authentication of Users Authentication of employees is also used by organizations in their security policies. The most common ways to authenticate rely on something the user knows, something the user has, or something the user is (Figure 12-1).

Something a user knows is a password. Most organizations today enforce a strong password policy, because free software available on the Internet can break a pass- word from the dictionary very quickly. Strong password policies include using combi- nations of letters, numbers, and special characters, such as plus signs and ampersands. Some organizations are suggesting the use of passphrases to increase the strength of a password. See Box 12-1 for an overview of best practices to create strong passwords. Policies typically include the enforcement of changing passwords every 30 or 60 days.

Authentication of Users 231

Figure 12-1 Ways to Authenticate Users A. An ID badge, B. Examples of weak and strong passwords, C. A finger on a biometric scanner.

Weak password: BobandSue

Strong password: M2f4#eegh/

A © Photos.com

C © Gary James Calder/Shutterstock


Passwords should never be written down in an obvious place, such as a sticky note attached to the monitor or under the keyboard.

The second area of authentication is something the user has, such as an identifica- tion (ID) card. ID cards can be magnetic, similar to a credit card, or have a radio frequency identification (RFID) chip embedded into the card.

The last area of authentication is biometrics. Devices that recognize thumb prints, retina patterns, or facial patterns are available. Depending on the level of security needed, organizations commonly use a combination of these types of authentication.

Threats to Security The largest benefit of a computer network is the ability to share information. However, organizations need to protect that information and ensure that only

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• Review the specific system guidelines for users—most will have information on password parameters and allowable characters.

• Use a combination of letters, numbers, special characters (!, $, %, &, *) and upper- and lowercase.

• Longer passwords are harder to crack. Consider at least 8 characters if the system allows.

• Choose a password that is based on a phrase: Use portions or abbreviations of the words in the phrase, or use substitutions (e.g., $ for S, 4 for “for”) to create the password. Example phrase: “Lucy in the Sky with Diamonds” was released in 1967; example password: LUit$wdia67.

• Think carefully about the password and create something that is easy for you to remember.

• Change your password frequently, and do so immediately if you believe your system or email has been hacked.

• Consider using a password manager program to help you create strong pass- words and store them securely.


• Share your password with anyone. • Post your passwords in plain sight. • Use dictionary words or any personal characteristics (your name, phone num-

ber, or birthday). • Use a string of numbers. • Use the same password for multiple sites.

Data from Pennsylvania State Information Technology Services. (2015). Password best practices. Retrieved from http://its.psu.edu/legacy/be-safe/password-best-practices.html

authorized individuals have access to the network and the data appropriate to their role. Threats to data security in healthcare organizations are becoming in- creasingly prevalent. A nationwide survey by the Computing Technology Industry Association (CompTIA) found that human error was responsible for more than half of security breaches. Human error was categorized as failure to follow policies and procedures, general carelessness, lack of experience with websites and appli- cations, and being unaware of new threats (Greenberg, 2015). According to De- gaspari (2010), “Given the volume of electronic patient data involved, it’s perhaps not surprising that breaches are occurring. According to the Department of Health and Human Services’ Office of Civil Rights (OCR), 146 data breaches affecting 500 or more individuals occurred between December 22, 2009, and July 28, 2010. The types of breaches encompass theft, loss, hacking, and improper disposal; and include both electronic data and paper records” (para. 4). The Fifth Annual Bench- mark Study on Privacy & Security of Healthcare Data (Ponemon Institute, 2015) reported that “[m]ore than 90 percent of healthcare organizations represented in this study had a data breach, and 40 percent had more than five data breaches over the past two years” (para. 3). Interestingly, the most common type of data breach was related to a criminal attack on the healthcare organization (up 125% in the last 5 years). Key terms related to criminal attacks are brute force attack (software used to guess network passwords) and zero day attack (searching for and exploiting software vulnerabilities). Of the intentional data breaches (as opposed to unintentional), “45 percent of healthcare organizations say the root cause of the data breach was a criminal attack and 12 percent say it was due to a malicious insider” (Ponemon Institute, para. 4). That leaves nearly 43% of data breaches in the unintentional category. The Healthcare Information and Management Systems Society (HIMSS) 2015 survey reported the negligent insider as the most common source of a security breach. Examples of unintentional/negligent breaches include lost or stolen devices, or walking away from a workstation without logging off. If you use a device in your work and it is lost or stolen, or you violate policy by walking away from a workstation without logging off, this may be considered negligence and you may be subject to discipline or even lose your job. An interest- ing example of an unintentional data breach was reported on the OCR website: A company leased photocopier equipment and returned it without erasing the healthcare data stored on the copier hard drive, resulting in a settlement of over $1.2 million (U.S. Department of Health and Human Services, n.d.). Healthcare organizations need to be proactive in anticipating the potential for and preventing security breaches.

The first line of defense is strictly physical. A locked office door, an operating system that locks down after 5 minutes of inactivity, and regular security training programs are extremely effective in this regard. Proper workspace security discipline is a critical aspect of maintaining security. Employees need to be properly trained to be aware of computer monitor visibility, shoulder surfing, and policy regarding the removal of computer hardware. A major issue facing organizations is removable stor- age devices (Figure 12-2). CD/DVD burners, jump drives, flash drives, and thumb drives (which use USB port access) are all potential security risks. These devices can be slipped into a pocket and, therefore, are easily removed from the organization. One

Threats to Security 233

way to address this physical security risk is to limit the authorization to write files to a device. Organizations are also turning off the CD/DVD burners and USB ports on company desktops.

The most common security threats a corporate network faces are hackers, malicious code (spyware, adware, ransomware, viruses, worms, Trojan horses), and mali- cious insiders. Acceptable use policies help to address these problems. For example, employees may be restricted from downloading files from the Internet. Downloaded files, including email attachments, are the most common way viruses and other malicious codes enter a computer network. Network security policies typically pro- hibit employees from using personal CDs/DVDs and USB drives, thereby preventing the transfer of malicious code from a personal computer to the network.

Let’s look more closely at some of these common network security threats. We typically think of hackers as outsiders who attempt to break into a network by exploiting software and network vulnerabilities, and indeed these black hat (mali- cious) hackers (crackers) do exist. However, more organizations are looking to employ ethical hackers (white hat hackers), those who are skilled at looking for and closing network security vulnerabilities (Caldwell, 2011).

Spyware and adware are normally controlled in a corporate network by limiting the functions of the browsers used to surf the Internet. For example, the browser privacy options can control how cookies are used. A cookie is a very small file writ- ten to the hard drive of a computer whose user is surfing the Internet. This file con- tains information about the user. For example, many shopping sites write cookies to the user’s hard drive containing the user’s name and preferences. When that user returns to the site, the site will greet her by name and list products in which she is possibly interested. Weather websites send cookies to users’ hard drives with their

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Figure 12-2 A Removable Storage Device © Alex Kotlov/Shutterstock

ZIP code so that when each user returns to that site, the local weather forecast is immediately displayed. On the negative side, cookies can follow the user’s travels on the Internet. Marketing companies use spying cookies to track popular websites that could provide a return on advertising expenditures. Spying cookies related to marketing typically do not track keystrokes in an attempt to steal user IDs and passwords; instead, they simply track which websites are popular, and these data are used to develop advertising and marketing strategies. Nurse informaticists exploring new healthcare technologies on the Internet may find that ads for these technologies begin to pop up the next few times they are on the Internet. Spyware that does steal user IDs and passwords contains malicious code that is normally hidden in a seem- ingly innocent file download. This threat to security explains why healthcare orga- nizations typically do not allow employees to download files. The rule of thumb to protect the network and one’s own computer system is to only download files from a reputable site that provides complete contact information. Be aware that mali- cious code is sometimes hidden in an email link or in a file sent by a trusted contact whose email has been hacked. If you are not expecting a file from an email contact, or if you receive an email with only a link in it—resist the urge to download or click!

A relatively new threat to healthcare organizations is ransomware—malicious code that blocks the organization from using their computer systems until a ransom is paid to the hacker. Consider this recent case of ransomware intrusion:

In February 2016 a hospital in Los Angeles made headlines for giving in to the ransom demand of hackers who used encryption to cripple its internal computer network, including electronic patient records, for three weeks, causing it to lose patients and money. After the hackers initially demanded $3.4 million, the hospital paid them $17,000. In explaining his decision, Allen Stefanek, president of Hollywood Presbyterian Medical Center, said, “The quickest and most efficient way to restore our systems and administra- tive functions was to pay the ransom.” The money was transferred through Bitcoin, a cryptocurrency that permits anonymity. (Goldsborough, 2016, para. 2–3)

In addition to strict policies related to network security, organizations may also use such devices as firewalls (covered in the next section) and intrusion detection devices to protect from hackers. Protect yourself at home by ensuring that you have an updated version of antivirus software, be wary of unusual emails, and develop strong passwords and change them frequently. If your email is hacked, report it to the proper authorities as soon as possible, warn your contacts that you have been hacked, change your password, and check to see that your antivirus software is up to date.

Another huge threat to corporate security is social engineering, or the manipula- tion of a relationship based on one’s position (or pretend position) in an organiza- tion. For example, someone attempting to access a network might pretend to be an employee from the corporate IT office, who simply asks for an employee’s user ID and password. The outsider can then gain access to the corporate network. Once this access has been obtained, all corporate information is at risk. A second

Threats to Security 235

example of social engineering is a hacker impersonating a federal government agent. After talking an employee into revealing network information, the hacker has an open door to enter the corporate network. A related type of social engineer- ing is phishing. Phishing is an attempt to steal information by manipulating the recipient of an email or phone call to provide passwords or other private infor- mation. Box 12-2 contains an example of a phishing email and tips for identifying phishing scams.

Additional types of social engineering schemes include spear phishing, which is a more specifically targeted scheme where the attacker takes advantage of contact information provided in an organization’s directory and tailors the scam email to a specific person; baiting, where a malware-infected USB flash drive is left in a pub- lic area, thus tricking the finder into loading it to identify its owner; and scareware,

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Check suspicious emails for grammar and spelling errors, generic greetings (User, Dear, Dearest, etc.), requests for immediate action, or requests for personal information (passwords, bank account numbers). Some phishing emails may appear to come from your bank or other trusted organization. Think carefully about why a seemingly legitimate organization might be asking for information they should already have, or ask yourself why they might need to know what they are asking for. Be aware of your organization’s procedures for reporting phishing scams, and do so immediately.

Data from Pennsylvania State University Office of Information Security. (2016). Stop phishing scams. Retrieved from http://phishing.psu.edu/what-is-phishing

User, This is to inform you that due to company Personal Account updates completed today We will need you to update you Personal Information and E-mail account as soon as possible. Please follow the link bellow to verfy your Personal Information to avoid the cancelation of your E-mail account. CLICK HERE PERSONAL ACCOUNT We apologize for the inconvienence. Thank you in advance! Regards, Tech Support Team – Security Administrator

Dear From:


Tech Support

[email protected] March 5, 2017 1:48 PM

Example of a Phishing Scam Email

where the scam email reports that the user has been hacked and tricks them into giving the hacker remote access to the computer to “fix” it (TechTarget, n.d.).

Another example of an important security threat to a corporate network is the malicious insider. This person can be a disgruntled or recently fired employee whose rights of access to the corporate network have not yet been removed. In the case of a recently fired employee, his or her network access should be suspended immediately upon notice of termination. To avoid the potentially hazardous issues created by ma- licious insiders, healthcare organizations need some type of policy and specific pro- cedures to monitor employee activity to ensure that employees carry out only those duties that are part of their normal job. Separation of privileges is a common secu- rity tool; no one employee should be able to complete a task that could cause a criti- cal event without the knowledge of another employee. For example, the employee who processes the checks and prints them should not be the same person who signs those checks. Similarly, the employee who alters pay rates and hours worked should be required to submit a weekly report to a supervisor before the changes take effect. Software that can track and monitor employee activity is also available. This soft- ware can log which files an employee accesses, whether changes were made to files, and whether the files were copied. Depending on the number of employees, organi- zations may also employ a full-time electronic auditor who does nothing but moni- tor activity logs. More than half of healthcare organizations have hired full-time employees to provide network security (HIMSS, 2015). Additional strategies for securing networks suggested in this most recent HIMSS survey were mock cyberde- fense exercises, sharing information between and among healthcare organizations, monitoring vendor intelligence feeds, and subscribing to security alerts and tips from US_CERT (United States Computer Emergency Readiness Team).

Security Tools A wide range of tools are available to an organization to protect the organizational network and information. These tools can be either a software solution, such as antivirus software, or a hardware tool, such as a proxy server. Such tools are effective only if they are used along with employee awareness training. The 2015 HIMSS Cybersecurity Survey results indicate that an average of 11 different software tools were used by respondents to provide network security, with antivirus technology, firewalls, and data encryption as the most common tools.

For example, email scanning is a commonly used software tool. All incoming email messages are scanned to ensure they do not contain a virus or some other malicious code. This software can find only viruses that are currently known, so it is important that the virus software be set to search for and download updates automatically. Organizations can further protect themselves by training employees to never open an email attachment unless they are expecting the attachment and know the sender. Even IT managers have fallen victim to email viruses that sent infected emails to everyone in their address book. Employees should be taught to protect their organization from new viruses that may not yet be included in their scanning software by never opening an email attachment unless the sender is known and the

Security Tools 237

attachment is expected. Email scanning software and antivirus software should never be turned off, and updates should be installed at least weekly—or, ideally, daily. Software is also available to scan instant messages and to delete automatically any spam email.

Many antivirus and adware software packages are available for fees ranging from free to more than $25 per month (for personal use) to several thousands of dollars per month (to secure an organization’s network). The main factors to consider when purchasing antivirus software are its effectiveness (i.e., the number of viruses it has missed), the ease of installation and use, the effectiveness of the updates, and the help and user support available. Numerous websites compare and contrast the most recent antivirus software packages. Be aware, however, that some of these sites also sell anti- virus software, so they may present biased information.

Firewalls are another tool used by organizations to protect their corporate net- works when they are attached to the Internet. A firewall can be either hardware, soft- ware, or a combination of both that examines all incoming messages or traffic to the network. The firewall can be set up to allow only messages from known senders into the corporate network. It can also be set up to look at outgoing information from the corporate network. If the message contains some type of corporate secret, the firewall may prevent the message from leaving. In essence, firewalls serve as electronic security guards at the gate of the corporate network.

Proxy servers also protect the organizational network. Proxy servers prevent users from directly accessing the Internet. Instead, users must first request passage from the proxy server. The server looks at the request and makes sure the request is from a legitimate user and that the destination of the request is permissible. For example, organizations can block requests to view a website with the word “sex” in the title or the actual uniform resource locator of a known pornography site. The proxy server can also lend the requesting user a mask to use while he or she is surf- ing the Web. In this way, the corporation protects the identity of its employees. The proxy server keeps track of which employees are using which masks and directs the traffic appropriately.

With hacking becoming more common, healthcare organizations must have some type of protection to avoid this invasion. An intrusion detection system (both hardware and software) allows an organization to monitor who is using the network and which files that user has accessed. Detection systems can be set up to monitor a single com- puter or an entire network. Corporations must diligently monitor for unauthorized access of their networks. Anytime someone uses a secured network, a digital footprint of all of the user’s travels is left, and this path can be easily tracked by electronic auditing software.

Offsite Use of Portable Devices Offsite uses of portable devices, such as laptops, tablets, home computing systems, smartphones, smart devices, and portable data storage devices, can help to stream- line the delivery of health care. For example, home health nurses may need to ac- cess electronic protected health information (EPHI) via a wireless laptop connection during a home visit, or a physician might use a smartphone to get specific patient

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information related to a prescription refill in response to a patient request. These mobile devices are invaluable to healthcare efficiency and responsiveness to patient need in such cases. At the very least, however, agencies should require data encryp- tion when EPHI is being transmitted over unsecured networks or transported on a mobile device as a way of protecting sensitive information. Hotspots provided by companies, such as coffee shops or restaurants, and by airports are not secured networks. Virtual private networks (VPNs) must be used to ensure that all data transmitted on unsecured networks are encrypted. The user must log into the VPN to reach the organization’s network.

Only data essential for the job should be maintained on the mobile device; other nonclinical information, such as Social Security numbers, should never be car- ried outside the secure network. Some institutions make use of thin clients, which are basic interface portals that do not keep secure information stored on them. Es- sentially, users must log in to the network to get the data they need. Use of thin clients may be problematic in patient care situations where the user cannot access the network easily. For example, some rural areas of the United States do not have wireless or cellular data coverage. In these instances, private health information may need to be stored in a clinician’s laptop or tablet. This is comparable to home health nurses carrying paper charts in their cars to make home visits, and it entails the same responsibilities accompanying such use of private information outside the institution’s walls.

What happens if one of these devices is lost or stolen? The agency is ultimately responsible for the integrity of the data contained on these devices and is required by HIPAA regulations (U.S. Department of Health and Human Services, 2006) to have policies in place covering such items as appropriate remote use, removal of de- vices from their usual physical location, and protection of these devices from loss or theft. Simple rules, such as covering laptops left in a car and locking car doors during transport of mobile devices containing EPHI, can help to deter theft. If a device is lost or stolen, the agency must have clear procedures in place to help ensure that sensi- tive data are not released or used inappropriately. Software packages that provide for physical tracking of the static and mobile computer inventory including laptops, smartphones, and tablets are being used more widely and can assist in the recovery of lost or stolen devices. In addition, some software that allows for remote data deletion (data wipe) in the event of theft or loss of a mobile device can be invaluable to the agency in preventing the release of EPHI.

If a member of an agency is caught accessing EPHI inappropriately or steals a mobile device, the sanctions should be swift and public. Sanctions may range from a warning or suspension with retraining to termination or prosecution, depending on the severity of the security breach. The sanctions must send a clear message to all that protecting EPHI is serious business.

The U.S. Department of Health and Human Services (n.d.) suggests the following strategies for managing remote access:

• Restricting remote access to computers owned or configured by your organization

• Disallowing administrator privileges on remote access computers

Offsite Use of Portable Devices 239

• Placing restrictions in the VPN and remote access policies • Configuring the VPN to operate in a “sandbox” or virtual environment that iso-

lates the session from other software running on the remote machine • Educating users about safe computing practices in remote locations (para. 8)

To protect our patients and their data, nurses must consider the impact of wireless mobile devices (see Box 12-3). Data can be stolen by an employee very easily through the use of email or file transfers.

Malware, or malicious code that infiltrates a network, can collect easily acces- sible data. One of the evolving issues is lost or stolen devices that can provide a gateway into a healthcare organization’s network and records. When the device is owned by the employee, other issues arise as to how the device is used and secured.

The increase in cloud computing has also challenged our personal and profes- sional security and privacy. Cloud computing refers to storing and accessing data and computer programs on the Internet, rather than the local hard drive of a computer. Common examples of cloud computing for personal use include Google Drive, Apple iCloud, and Amazon. Cloud computing allows for easy syncing of separate devices to

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Informatics nurse specialists must be aware of the uses of portable devices. In 2016, one hospital in the Pittsburgh area was a site of a popular game, and the administration was upset because it creates a privacy issue for people using their hospital as a search site. This hospital actually contacted the game developer to be removed from their game.

Hospitals must always be concerned about privacy and safety issues within their control, but also be on the alert for those outside their control, such as the Pokemon Go game. Pittsburgh’s Action News 4, Marcie Cipriani, reported that Pokemon Go used West Penn Hospital, part of Allegheny Health Network in Pittsburgh, as a real-world location in the game. The game utilizes enhanced reality, which allows players to combine images from the real world with those of the game. The Allegheny Health Network officials stated that the exciting, interactive game created concerns when it brought players inside their hospital. They say hunting Pokemon at the hospital created a patient privacy issue and a safety concern. Administrators warned those who are playing to stay out of their hospitals and contacted the game’s developer, who agreed to remove their hospitals from the app. They have asked their employees to be on the lookout for anyone playing the app while they are walking around the hospital and to contact security if they see Pokemon Go players.

Data from Cipriani, M. (2016, July 30). Pokemon Go targets Allegheny Health System hospitals in Pittsburgh. Pittsburgh’s Action News 4. Retrieved from http://www.wtae.com /news/pokemon-go-players-not-welcome-at-allegheny-health-network-hospitals/40946828

promote sharing and collaboration (Griffith, 2016). According to Jansen and Grance (2011), cloud computing “promises to have far reaching effects on the systems and networks of federal agencies and other organizations. Many of the features that make cloud computing attractive, however, can also be at odds with traditional security models and controls” (p. vi). Healthcare organizations are moving to the cloud because cloud computing tends to be cheaper and faster, offers more flexibility for work location, provides nearly immediate disaster recovery, supports collaboration, provides security, and offers frequent software updates (Salesforce UK, 2015). However, there are important security concerns related to cloud computing in health care. Guccione (2015) offers these important considerations for maintaining security in a cloud environment:

First, a cloud service should be have client-side encryption of data, which both protects files on the local hard drive as well as in the cloud. Second, a secure cloud service should offer multi-factor authentication to add an extra layer of access control for all users. Finally, a secure cloud provider should either provide data loss prevention tools to protect the stored data or allow an organization to extend its DLP protocols to the cloud. In both cases, the organization is alerted immediately the moment a user attempts to send sen- sitive files to an outside source. (para. 5)

It is clear that healthcare organizations need to be extra vigilant about their data security when using cloud computing. However, as we emphasized several times in this chapter, employee training on security measures may be the most important de- fense, because “the latest techniques for cyber theft are much less about breaching networks from the outside, such as through the cloud service, than they are exploiting holes inside an organization, particularly from careless employees” (Guccione, 2015, para. 9).

Summary Technology changes so quickly that even the most diligent user will likely encounter a situation that could constitute a threat to his or her network. Orga- nizations must provide their users with the proper training to help them avoid known threats and—more importantly—be able to discern a possible new threat. Consider that 10 years ago wireless networks were the exception to the rule, where today access to wireless networks is almost taken for granted. How will computer networks be accessed 10 years from now? The most important concept to remember from this chapter is that the only completely safe network is one that is turned off. Network accessibility and network availability are necessary evils that pose security risks. The information must be available to be accessed, yet remain secured from hackers, unauthorized users, and any other potential security breaches. As the cloud expands, so do the concerns over security and privacy. In an ideal world, everyone would understand the potential threats to network security and would diligently monitor and implement tools to prevent unauthor- ized access of their networks, data, and information.

Summary 241

References Caldwell, T. (2011). Ethical hackers: Putting on the white hat. Network Security, 2011(7),

10–13. doi:10.1016/S1353-4858(11)70075-7 Degaspari, J. (2010). Staying ahead of the curve on data security. Healthcare Informatics,

27(10), 32–36. Goldsborough, R. (2016). Protecting yourself from ransomware. Teacher Librarian, 43(4),

70–71. Griffith, E. (2016). What is cloud computing? PCMag. Retrieved from http://www.pcmag.com

/article2/0,2817,2372163,00.asp Greenberg, A. (2015). Human error cited as leading contributor to breaches, study shows.

SC Magazine. Retrieved from http://www.scmagazine.com/study-find-carelessness-among -top-human-errors-affecting-security/article/406876

Guccione, D. (2015). Is the cloud safe for healthcare? Healthcare Informatics. Retrieved from http://www.healthcare-informatics.com/article/cloud-safe-healthcare

Health Information and Management Systems Society (HIMSS). (2015) 2015 HIMSS Cybersecurity Survey. Retrieved from http://www.himss.org/2015-cybersecurity-survey /executive-summary

Jansen, W., & Grance, T. (2011). National Institute of Standards and Technology (NIST): Guidelines on security and privacy in public cloud computing. Retrieved from https:// cloudsecurityalliance.org/wp-content/uploads/2011/07/NIST-Draft-SP-800-144_cloud -computing.pdf

Mowry, M., & Oakes, R. (n.d.). Not too tight, not too loose. Healthcare Informatics, Healthcare IT Leadership, Vision & Strategy. Retrieved from http://www.healthcare -informatics.com/article/not-too-tight-not-too-loose

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1. Sue is a chronic obstructive pulmonary disorder clinic nurse enrolled in a master’s education program. She is interested in writing a paper on the fac- tors that are associated with poor compliance with medical regimens and associated repeat hospitalization of chronic obstructive pulmonary disorder patients. She downloads patient information from the clinic database to a thumb drive that she later accesses on her home computer. Sue understands rules about privacy of information and believes that because she is a nurse and needs this information for a graduate school assignment, she is entitled to the information. Is Sue correct in her thinking? Describe why she is or is not correct.

2. The nursing education department of a large hospital system has been cen- tralized; as a consequence, the nurse educators are no longer assigned to one hospital but must now travel among all of the hospitals. They use their smartphones to interact and share data and information. What are the first steps you would take to secure these transactions? Describe why each step is necessary.

3. Research cloud computing in relation to health care. What are the major security and privacy challenges? Please choose three and describe them in detail.

Ponemon Institute. (2015, May). Fifth annual benchmark study on privacy & security of healthcare data. Retrieved from http://media.scmagazine.com/documents/121/healthcare _privacy_security_be_30019.pdf

Salesforce UK. (2015). Why move to the cloud? Ten benefits of cloud computing. Retrieved from https://www.salesforce.com/uk/blog/2015/11/why-move-to-the-cloud-10-benefits-of -cloud-computing.html

Sullivan, T. (2012). Government health IT: DHS lists top 5 mobile medical device security risks. Retrieved from http://www.govhealthit.com/news/dhs-lists-top-5-mobile-device-security-risks

TechTarget (n.d.). Social engineering. Retrieved from http://searchsecurity.techtarget.com /definition/social-engineering

U.S. Department of Health and Human Services. (2006). HIPAA security guidance. Retrieved from https://www.hhs.gov/sites/default/files/ocr/privacy/hipaa/administrative/securityrule /remoteuse.pdf

U.S. Department of Health and Human Services. (n.d.). Implement privacy and security protection measures. Retrieved from http://www.hrsa.gov/healthit/toolbox/healthitimplementation /implementationtopics/ensureprivacysecurity/ensureprivacysecurity_9.html

References 243

Key Terms » Alternative Payment

Models (APMs) » American Recovery

and Reinvestment Act (ARRA)

» Bar-code medication administration (BCMA)

» Clinical transformation

» Computerized provider order entry (CPOE)

» Electronic health records (EHRs)

» Events » Health information

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» Medicare Access and CHIP Reautho- rization Act of 2015 (MACRA)

» Merit-Based Incentive Payment System (MIPS)

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program (QPP) » Six Sigma » Tasks » Work process » Workflow » Workflow analysis

1. Provide an overview of the purpose of conduc­ ting workflow analysis and design.

2. Deliver specific instructions on workflow analysis and redesign techniques.

3. Cite measures of efficiency and effectiveness that can be applied to redesign efforts.

4. Explore meaningful use and beyond with the Medi care Access and Summary CHIP Reautho­ rization Act.


Introduction The healthcare environment has grown more complex and continues to evolve every day. Unfortunately, the complexities that help clinicians to deliver better care and improve patient outcomes also take a toll on the clinicians themselves. This toll is exemplified through hours spent learning new technology, loss in productivity as the user adjusts and adapts to new technology, and unintended workflow consequences from the use of technology.

Despite the perceived negative downstream effects to end users and pa- tients as a result of technology, this very same technology can improve effi- ciency and yield a leaner healthcare environment. The intent of this chapter is to outline the driving forces that create the need to redesign workflow as well as to elucidate what the nurse needs to know about how to conduct workflow redesign, measure the impact of workflow changes, and assess the impact of meaningful use.

Workflow Analysis Purpose According to the American Association for Justice (2016),

Research has confirmed that 440,000 people die every year be- cause of preventable medical errors. That is equivalent to almost the entire population of Atlanta, Georgia dying from a medical error each year. Preventable medical errors are the third leading cause of death in the United States and cost our country tens of billions of dollars a year. (para. 1)

Not only is there an impact on patients and their families from these errors, but there is also a significant financial impact on healthcare orga- nizations. Clearly, we must minimize these errors, and one of the most

Workflow and Beyond Meaningful Use Dee McGonigle, Kathleen Mastrian, and Denise Hammel­Jones



important tools for this purpose is the use of electronic health records and information systems to provide point-of-care decision support and automation. The key point is that most of these errors are preventable and we must find ways to prevent them.

Technology can provide a mechanism to improve care delivery and create a safer patient environment, provided it is implemented appropriately and considers the surrounding workflow. In an important article by Campbell, Guappone, Sittig, Dykstra, and Ash (2009), the authors suggested that technology implemented without consideration of workflow can provide greater patient safety concerns than no technology at all. Computerized provider order entry (CPOE) causes us to focus more specifically on workflow considerations. These workflow implications are referred to as the unintended consequences of CPOE implementation; they are just some of the effects of poorly implemented technology. The Healthcare Information Management Systems Society (HIMSS, 2010) ME-PI Toolkit addressed workflow redesign and considered why it is so critical to successful technology implementa- tions. Thompson, Kell, Shetty, and Banerjee (2016) stated “By partnering clinicians with informaticists we strove to leverage the power of the electronic medical record (EMR) to reduce heart failure readmissions and improve patient transitions back to the community” (p. 380). They concluded that “Partnering with clinical informatics enabled the multidisciplinary team to leverage the power of the EMR in supporting and tracking new clinical workflows that impact patient outcomes” (p. 380). This multidisciplinary team believed that their success could reshape how healthcare providers facilitate patient discharge and the transition home. Leveraging the multidisciplinary team and EMR could provide a model for patient-centered and cost-effective care that could extend beyond their patients with heart failure.

Technology is recognized to have a potentially positive effect on patient outcomes. Nevertheless, even with the promise of improving how care is delivered, adoption of technology has been slow. The cost of technology solutions such as CPOE, barcode medication administration (BCMA), and electronic health records (EHRs) remain staggeringly high. The cost of technology, coupled with the lengthy timelines required to develop and implement such technology, has put this endeavor out of reach for many health- care organizations. In addition, upgrades or enhancements to the technology are of- ten necessary either mid-implementation or shortly after a launch, leaving little time to focus efforts on the optimization of the technology within the current workflow. Furthermore, the existence of technology does not in itself guarantee that it will be used in a manner that promotes better outcomes for patients.

Given the sluggish adoption of technology, in 2009 the U.S. government took an unprecedented step when it formally recognized the importance of health information technology (HIT) for patient care outcomes. As a result of the provisions of American Recovery and Reinvestment Act (ARRA), healthcare organizations can qualify for financial incentives based on the level of meaningful use achieved. Meaningful use (MU) refers to the rules and regulations established by the ARRA. The three stages of MU were part of an EHR incentive program. During stage 1, the focus was on data capturing and sharing. Stage 2 focused on advanced clinical processes, and stage 3 sought to improve outcomes. Stage 1 was initiated during 2011–2012, stage 2 began in 2014, and stage 3 was to be launched in 2016/2017 and was intended

246 CHAPTER 13 Workflow and Beyond Meaningful Use

to last through 2019 and beyond (Centers for Medicare & Medicaid Services [CMS], 2016a). However, with the new goal of paying for value and better care, the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) reformed Medicare payments by making changes that created a quality payment program (QPP) to replace the hodgepodge system of Medicare reporting programs (CMS, 2016b; see Figure 13-1). The MACRA QPP has two paths—Merit-Based Incentive Payment System (MIPS) or Alternative Payment Models (APMs)—that will be in effect through 2021 and beyond (CMS, 2016b). The MACRA requirements for the measure development plan consist of the following:

• Multipayer applicability • Coordination and sharing across measure developers • Clinical practice guidelines • Evidence base for non-endorsed measures • Gap analysis • Quality domains and priorities • Applicability of measures across healthcare settings • Clinical practice improvement activities • Considerations for electronic specifications and Qualified Clinical Data Registries

(QCDRs) (CMS, 2016c, p. 16).

According to Hagland (2016), MACRA, MIPS, and APMs will:

• Allow physicians and other clinicians to choose to select the measures that reflect how technology best suits their day-to-day practice

• Simplify the process for achievement and provide multiple paths for success • Align with the Office for the National Coordinator for Health Information

Technology’s 2015 edition Health IT Certification Criteria • Emphasize interoperability, information exchange, and security measures and

give patients access to their information through APIs (application program interfaces)

• Reduce the number of measures to an all-time low of 11 measures, down from 18 measures, and no longer require reporting on the clinical decision support and CPOE measures

• Exempt certain physicians from reporting when EHR technology is less appli- cable to their practice and allow physicians to report as a group (para. 4).

Workflow Analysis Purpose 247

Figure 13-1 MACRA



MACRA 2016

Allow Simplify Align Emphasize Reduce Exempt

For an organization that seeks to meet these measures, the data to support these measures must be gathered and reported on electronically—necessitating the use of technology in all patient care areas. The successful implementation of the measure- ment development plan “depends on a successful partnership with patients, frontline clinicians, and professional organizations and collaboration with other diverse stake- holders to develop measures that are meaningful to patients and clinicians and can be used across payers and health care settings” (CMS, 2016c, p. 64). Many of the quality reporting measures rely on nursing and medical documentation. Most healthcare per- sonnel already use EHRs, but MACRA measures will push healthcare organizations to reexamine the use of clinical technologies within their organization and approach implementations in a new way.

Not only is there a potential for patient safety and quality issues to arise from technology implementations that do not address workflow, but a financial impact to the organization is possible as well. All organizations, regardless of their industry, must operate efficiently to maintain profits and continue to provide services to their customers. For hospitals, which normally have significantly smaller profit margins than other organizations, the need to maintain efficient and effective care is essential for survival. Given that hospital profit margins are diminishing, never has there been a more crucial time to examine the costs of errors and poorly designed workflows and the financial burden they present to an organization than now. Moreover, what are the costs to an organization that fails to address the integration of technology? This is an area where few supporting data exist to substantiate the claim that technol- ogy without workflow considerations can, in fact, impact the bottom line.

Today, many healthcare organizations are experiencing the effects of poorly imple- mented clinical technology solutions. These effects may be manifested in the form of redundant documentation, non-value-added steps, and additional time spent at the computer rather than in direct care delivery. For example, Gugerty et al. (2007) stud- ied the challenges and opportunities in nursing documentation and determined that it was possible to decrease the time a nurse spends documenting per shift by 25%. Tech- nology ought not to be implemented for the sake of automation unless it promises to deliver gains in patient outcomes and proper workflow. In fact, the cost to organiza- tions for duplicate/redundant documentation by nursing can range from $6,500 to $13,000 per nurse, per year (Clancy, Delaney, Morrison, & Gunn, 2006). Stokowski (2013) found other issues, such as systems that are slow, freeze, lose data, and “don’t dump data from monitors and screening devices into the EHR in real time” slowing the documentation process and increasing the amount of time the nurse must spend on the computer and not in direct patient care (p. 9).

Examining the workflow surrounding the use of technology enables better use of the technology and more efficient work. It also promotes safer patient care delivery. The need to focus on workflow and technology is attracting increasing recognition, although there remains a dearth of literature that addresses the importance of this area. As more organizations work to achieve a level of technology adoption that will enable them to meet MACRA measures and receive financial payments, we will likely see more attention paid to the area of workflow design and, therefore, a greater body of research and evidence (AHRQ, n.d.; Qualis Health, 2011; Yuan, Finley, Long, Mills, & Johnson, 2013).

248 CHAPTER 13 Workflow and Beyond Meaningful Use

Workflow and Technology Workflow is a term used to describe the action or execution of a series of tasks in a prescribed sequence. Another definition of workflow is a progression of steps (tasks, events, interactions) that constitute a work process, involve two or more persons, and create or add value to the organization’s activities. In a sequential workflow, each step depends on the occurrence of the previous step; in a parallel workflow, two or more steps can occur concurrently. The term workflow is sometimes used interchangeably with process or process flows, particularly in the context of implementations. Obser- vation and documentation of workflow to better understand what is happening in the current environment and how it can be altered is referred to as process or workflow analysis. A typical output of workflow analysis is a visual depiction of the process, called a process map. The process map ranges from simplistic to fairly complex and provides an excellent tool to identify specific steps. It also can provide a vehicle for communication and a tool upon which to build educational materials as well as poli- cies and procedures.

One school of thought suggests that technology should be designed to meet the needs of clinical workflow (Yuan et al., 2013). This model implies that system ana- lysts have a high degree of control over screen layout and data capture. It also implies that technology is malleable enough to allow for the flexibility to adapt to a variety of workflow scenarios. Lessons learned from more than three decades of clinical technology implementations suggest that clinical technologies still have a long way to go on the road to maturity to allow this to be possible. The second and probably most prevalent thought process is that workflow should be adapted to the use of technology. Today, this is by far the most commonly used model given the progress of clinical technology. Bucur et al. (2016) developed clinical models to support clinical decision making that were inserted into the workflow models. This system integrates a workflow suite and functionality for the storage, management, and execution of clinical workflows and for the storage of traces of execution. The knowledge models are integrated and run from the workflow to support decisions at the right point in the clinical process (Bucur et al., p. 152). The ability to track and assess decision mak- ing throughout a clinical course of care for a patient will enhance our knowledge and improve patient care.

A concept that has gained popularity in recent years relative to workflow redesign is clinical transformation. Clinical transformation is the complete alteration of the clinical environment and, therefore, this term should be used cautiously to describe redesign efforts. Earl, Sampler, and Sghort (1995) define transformation as “a radical change ap- proach that produces a more responsive organization that is more capable of perform- ing in unstable and changing environments that organizations continue to be faced with” (p. 31). Many workflow redesign efforts are focused on relatively small changes and not the widespread change that accompanies transformational activities. More- over, clinical transformation would imply that the manner in which work is carried out and the outcomes achieved are completely different from the prior state—which is not always true when the change simply involves implementing technology. Technol- ogy can be used to launch or in conjunction with a clinical transformation initiative, although the implementation of technology alone is not perceived as transformational.

Workflow and Technology 249

Before undertaking transformative initiatives, the following guidelines should be understood:

• Leadership must take the lead and create a case for transformation. • Establish a vision for the end point. • Allow those persons with specific expertise to provide the details. • Think about the most optimal experience for both the patient and the clinician. • Do not replicate the current state. • Focus on those initiatives that offer the greatest value to the organization. • Recognize that small gains have no real impact on transformation.

Optimization Most of what has been and will be discussed in this chapter is related to workflow analysis in conjunction with technology implementations. Nevertheless, not all work- flow analysis and redesign occurs prior to the implementation of technology. Some analysis and redesign efforts may occur weeks, months, or even years following the implementation. When workflow analysis occurs postimplementation, it is often referred to as optimization. Optimization is the process of moving conditions past their current state and into more efficient and effective method of performing tasks. Merriam-Webster Online Dictionary (2016) considered optimization to be the act, process, or methodology of making something (as a design, system, or decision) as fully perfect, functional, or effective as possible. Some organizations will routinely engage in optimization efforts following an implementation, whereas other organiza- tions may undertake this activity in response to clinician concerns or marked change in operational performance.

Furthermore, workflow analysis can be conducted either as a stand-alone effort or as part of an operational improvement event. When the process is addressed alone, the effort is termed process improvement. Nursing informatics professionals should always be included in these activities to represent the needs of clinicians and to serve as a liaison for technological solutions to process problems. Additionally, informati- cists will likely become increasingly operationally focused and will need to transform their role accordingly to address workflow in an overall capacity as well as respec- tive to technology. As mentioned earlier, hospitals tend to operate with smaller profit margins than other industries and these profits will likely continue to diminish, forc- ing organizations to work smarter, not harder—and to use technology to accomplish this goal.

If optimization efforts are undertaken, the need to revisit workflow design should not be considered a flaw in the implementation approach. Even a well- designed future-state workflow during a technology implementation must be reexamined postimplementation to ensure that what was projected about the future state remains valid and to incorporate any additional workflow elements into the process redesign.

Exploring the topic of workflow analysis with regard to clinical technology implementation will yield considerably fewer literature results than searching for other topical areas of implementation. More research is needed in the area of the financial implications of workflow inefficiencies and their impact on patient care.

250 CHAPTER 13 Workflow and Beyond Meaningful Use

Time studies require an investment of resources and may be subject to patient privacy issues as well as the challenges of capturing time measurements on processes that are not exactly replicable. Another confounding factor affecting the quality and quantity of workflow research is the lack of standardized termi- nology for this area. A comprehensive literature search was conducted and pub- lished through the Agency for Healthcare Quality and Research (AHRQ) in 2008 as an evidence-based handbook for nurses; this literature search yielded findings indicating that a lack of standardized terminology in the area of workflow and publications on this topic have made it a difficult topic to support through research findings.

What all organizations ultimately strive for is efficient and effective delivery of patient care. The terms efficient and effective are widely known in quality areas or Six Sigma and Lean departments, but are not necessarily known or used in informatics. Ef- fective delivery of care or workflow suggests that the process or end product is in the most desirable state. An efficient delivery of care or workflow would mean that little waste—that is, unnecessary motion, transportation, over-processing, or defects—was incurred. Health systems such as Virginia Mason University Medical Center, among others, have experienced significant quality and cost gains from the widespread imple- mentation of Lean development throughout their organization.

Workflow Analysis and Informatics Practice The American Nurses Association (ANA), in Nursing Informatics: Scope and Stan- dards of Practice (2015), defined functional areas of practice for the informatics nurse specialist (INS). The functional area of analysis identified the specific functional quali- ties related to workflow analysis. Particularly, the ANA indicated that the INS should develop techniques necessary to assess and improve human–computer interaction. Workflow analysis, however, is not relevant solely to analysis, but rather is part of every functional area the INS engages in. The functional areas covered by consultants, researchers, and other areas need to understand workflow and appreciate how lack of efficient workflow affects patient care.

A critical aspect of the informatics role is workflow design. Nursing informatics is uniquely positioned to engage in the analysis and redesign of processes and tasks surrounding the use of technology. The ANA (2015) cites workflow redesign as one

Workflow Analysis and Informatics Practice 251


In my experience consulting, I have seen several examples of organizations that engage in the printing of paper reports that replicate informa- tion that has been entered and is available with the electronic health record. These reports are often reviewed, signed, and acted on, instead of using the electronic information. Despite the knowledge that the information contained in these reports was outdated the moment the report was printed and

that the very nature of using the report for workflow is an inefficient practice, this method of clinical workflow remains prevalent in many hospitals across the United States.

There is an underlying fear that drives the deci- sions to mold a paper-based workflow around clinical technology. There is also a lack of the appropriate amount of integration that would otherwise allow this information to be available in an electronic form.

of the fundamental skills sets that make up the discipline of this specialty. Moreover, workflow analysis should be part of every technology implementation, and the role of the informaticist within this team is to direct others in the execution of this task or to perform the task directly.

Unfortunately, many nurses find themselves in an informatics capacity without sufficient preparation for a process analysis role. One area of practice that is particu- larly susceptible to inadequate preparation is the ability to facilitate process analysis. Workflow analysis requires careful attention to detail and the ability to moderate group discussions, organize concepts, and generate solutions. These skills can be ac- quired through a formal academic informatics program or through courses that teach the discipline of Six Sigma or Lean, by example. Regardless of where these skills are acquired, it is important to understand that they are now and will continue to remain a vital aspect of the informatics role.

Some organizations have felt strongly enough about the need for workflow analysis that departments have been created to address this very need. Whether the department carries the name of clinical excellence, organizational effectiveness, or Six Sigma/Lean, it is critical to recognize the value this group can offer technology imple- mentations and clinicians.

As we examine how workflow analysis is conducted, note that while the nursing informaticist is an essential member of the team to participate in or enable workflow analysis, a team dedicated to this effort is necessary for its success.

Building the Design Team The workflow redesign team is an interdisciplinary team consisting of “process owners.” Process owners are those persons who directly engage in the workflow to be analyzed and redesigned. These individuals can speak about the intricacy of process, including process variations from the norm. When constructing the team, it is impor- tant to include individuals who are able to contribute information about the exact current-state workflow and offer suggestions for future-state improvement. Members of the workflow redesign team should also have the authority to make decisions about how the process should be redesigned. This authority is sometimes issued by managers, or it could come from participation of the managers directly. Such a care- ful blend of decision makers and “process owners” can be difficult to assemble but is critical for forming the team and enabling them for success. Often, individuals at the manager level will want to participate exclusively in the redesign process. While having management participate provides the advantage of having decision makers and management-level buy-in, these individuals may also make erroneous assump- tions about how the process should be versus how the process is truly occurring. Conversely, including only process owners who do not possess the authority to make decisions can slow down the work of the team while decisions are made outside the group sessions.

Team focus needs to be addressed at the outset of the team’s assembly. Early on, the team should decide which workflow will be examined to avoid confusion or spending time unnecessarily on workflow that does not ultimately matter to the outcome. In the early stages of workflow redesign, the team should define the begin- ning and end of a process and a few high-level steps of the process. Avoid focusing on

252 CHAPTER 13 Workflow and Beyond Meaningful Use

process steps in great detail in the beginning, as the conversation can get sidetracked or team members may get bogged down by focusing on details and not move along at a good pace. Six Sigma expert George Eckes uses the phrase “Stay as high as you can as long as you can”—a good catch phrase to remember to keep the team focused and at a high level. The pace at which any implementation team progresses ultimately af- fects the overall timeline of a project; therefore, focus and speed are skills that the in- formatics expert should develop and use throughout every initiative, but particularly when addressing workflow redesign.

The workflow redesign team will develop a detailed process map after agreement is reached on the current-state process’s beginning and end points, and a high-level map depicting the major process steps is finalized. Because workflow crosses many different care providers, it may be useful to construct the process map using a swim- lane technique (Figure 13-2). A swim-lane technique uses categories such as functional workgroups and roles to visually depict groups of work and to indicate who performs the work. The resulting map shows how workflow and data transition to clinicians and can demonstrate areas of potential process and information breakdowns.

It may take several sessions of analysis to complete a process map, as details are uncovered and workarounds discussed. There is a tendency for individuals who participate in process redesign sessions to describe workflow as they believe it to be occurring, rather than not how it really is. The informatics expert and/or the process team facilitator should determine what is really happening, however, and capture that information accurately. Regardless of whether a swim-lane or simplistic process map design is used, the goal is to capture enough details to accurately portray the process as it is happening today.

Other techniques (aside from process mapping) may be used to help the team understand the workflow as it exists in the current state. The future-state workflow planning will be only as good as the reliability of the current state; thus it is crucial to undertake whatever other actions are needed to better understand what is happen- ing in the current state. Observation, interviews, and process or waste walks are also helpful in understanding the current state.

Value Added Versus Non–Value Added Beyond analysis of tasks, current-state mapping provides the opportunity for the pro- cess redesign team to distinguish between value-added and non-value-added activities. A value-added activity or step is one that ultimately brings the process closer to com- pletion or changes the product or service for the better. An example of a value-added step would be placing a name tag on a specimen sample. The name tag is necessary for the laboratory personnel to identify the specimen and, therefore, its placement is an essential or value-added step in the process. Some steps in a process do not necessarily add value but are necessary for regulatory or compliance reasons. These steps are still considered necessary and need to be included in the future process. A non-value-added step, in contrast, does not alter the outcome of a process or prod- uct. Activities such as handling, moving, and holding are not considered value-added steps and should be evaluated during workflow analysis. Manipulating papers, mov- ing through computer screens, and walking or transporting items are all considered non-value-added activities.

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254 CHAPTER 13 Workflow and Beyond Meaningful Use

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The five whys represent one technique to drive the team toward identifying value-added versus non-value-added steps. The process redesign facilitator will query the group about why a specific task is done or done in a particular way through a series of questions asking “why?” The goal is to uncover tasks that came about due to workarounds or for other unsubstantiated reasons. Tasks that are considered non–value added and are not necessary for the purpose of compliance or regulatory reasons should be eliminated from the future-state process. The team’s purpose in re- designing workflow is to eliminate steps in a process that do not add value to the end state or that create waste by their very nature.

Waste A key underpinning of the Lean philosophy is the removal of waste activities from workflow. Waste is classified as unnecessary activities or an excess of products to per- form tasks. The seven categories listed here are the most widely recognized forms of waste:

• Overproduction: pace is faster than necessary to support the process • Waiting • Transport • Inappropriate processing: over-processing • Unnecessary inventory: excess stock • Unnecessary motion: bending, lifting, moving, and so on • Defects: reproduction

Variation The nature of the work situation for the nurse is one of frequent interruptions caus- ing the workflow to be disrupted and increasing the chance of error (Yuan et al., 2013). Variation in workflow is considered the enemy of all good processes and, therefore, should be eliminated when possible. Variation occurs when workers per- form the same function in different ways. It usually arises because of flaws in the way a process was originally designed, lack of knowledge about the process, or in- ability to execute a process as originally designed due to disruption or disturbances in the workflow. Examining the process as it exists today will help with identifying variation. A brief statement about variation that cannot be eliminated: Processes that involve highly customized products or services are generally not conducive to stan- dardization and the elimination of variation inherent to the process.

Some argue that delivery of care is subject to variation owing to its very nature and the individual needs of patients. There is little doubt that each patient’s care should be tailored to meet his or her specific needs. Nevertheless, delivery of care in- volves some common processes that can be standardized and improved upon without jeopardizing care.

Transitioning to the Future State Following redesign efforts, regardless of whether they occurred during or after an im- plementation or as a stand-alone process improvement event, steps must be taken to

Workflow Analysis and Informatics Practice 255

ensure that change takes hold and the new workflow continues after the support team has disbanded. Management support and involvement during the transition phase is essential, as management will be necessary to enforce new workflow procedures and further define/refine roles and responsibilities. Documentation of the future-state workflow should have occurred during the redesign effort but is not completely finished until after the redesign is complete and the workflow has become operational. Policies and procedures are addressed and rewritten to encompass the changes to workflows and role assignments. Help desk, system analyst, nursing education, and other support personnel need to be educated about the workflow specifics as part of the postimprovement effort. It is considered good practice to involve the operational staff in the future process discussions and planning so as to incorporate specifics of these areas and ensure the buy-in of the staff.

When workflow changes begin to fail and workarounds develop, they signal that something is flawed about the way in which the new process was constructed and needs to be evaluated further. The workflow redesign team is then brought together to review and, if necessary, redesign the process.

The future state is constructed with the best possible knowledge of how the pro- cess will ideally work. To move from the current state to the future state, gap analysis is necessary. Gap analysis zeros in on the major areas most affected by the change— namely, technology. What often happens in redesign efforts is an exact or near-exact replication of the current state using automation. The gap analysis discussion should generate ideas from the group about how best to utilize the technology to transform practice. A prudent step is to consider having legal and risk representatives at the table when initiating future-state discussions to identify the parameters within which the group should work; nevertheless, the group should not assume the existing pa- rameters are its only boundaries.

Future-state process maps become the basis of educational materials for end users, communication tools for the project team, and the foundation of new policies and procedures. Simplified process maps provide an excellent schematic for communicat- ing change to others.

Informatics as a Change Agent Technology implementations represent a significant change for clinicians, as does the workflow redesign that accompanies adoption of technology. Often the degree of change and its impact are underappreciated and unaccounted for by leadership and staff alike. A typical response to change is anger, frustration, and a refusal to accept the proposed change. All of these responses should be expected and need to be ac- counted for; thus a plan to address the emotional side of change is developed early on. Every workflow redesign effort should begin with a change management plan (Figure 13-3). Engagement of the end user is a critical aspect of change management and, therefore, technology adoption. Without end-user involvement, change is resisted and efforts are subject to failure. Users may be engaged and brought into the prospec- tive change through question-and-answer forums, technology demonstrations, and frequent communications regarding change, and as department-specific representa- tives in working meetings.

256 CHAPTER 13 Workflow and Beyond Meaningful Use

Many change theories have been developed. No matter which change theory is ad- opted by the informatics specialist, however, communication, planning, and support are key factors in any change management strategy. Informaticists should become knowledgeable about at least one change theory and use this knowledge as the basis for change management planning as part of every effort. John Kotter (1996), one of the most widely recognized change theorists, suggested the following conditions must be addressed to deal with change in an organization:

• Education and communication • Participation and involvement • Facilitation and support • Negotiation and agreement • Manipulation and co-optation • Explicit and implicit coercion

In the HIMSS (2015) Nursing Informatics Impact survey, nursing informaticists were identified as the most significant resource in a project team that influences adop- tion and change management. Nurses bring to such teams their ability to interact with various clinicians, their knowledge of clinical practice, and their ability to em- pathize with the clinicians as they experience the impact of workflow change. These innate skills differentiate the nursing informaticist from other members of the imple- mentation team and are highly desirable in the informatics community.

Nevertheless, no matter which change management techniques are employed by the informatics specialist and the project team, adoption of technology and workflow may be slow to evolve. Change is often a slow process that requires continual posi- tive reinforcement and involvement of supporting resources. Failure to achieve strong adoption results early on is not necessarily a failure of the methods utilized, but rather may be due to other factors not entirely within the control of the informaticist.

Informatics as a Change Agent 257

Figure 13-3 Change Management © Digital Storm/Shutterstock

Perhaps a complete alteration in behavior is not possible, but modifications to be- haviors needed to support a desired outcome can be realized. This situation is analo- gous to the individual who stops smoking; the desire for the cigarette remains, but the behavior has been modified to no longer sustain smoking. To manage change in an organization, nurses must modify behavior to produce the intended outcome.

Change takes hold when strong leadership support exists. This support manifests itself as a visible presence to staff, clear and concise communications, an unwavering position, and an open door policy to field concerns about change. Too often, leader- ship gives verbal endorsement of change and then fails to follow through with these actions or withdraws support when the going gets tough. Inevitability, if leadership wavers, so too will staff.

Measuring the Results Metrics provide understanding about the performance of a process or function. Typi- cally within clinical technology projects, we identify and collect specific metrics about the performance of the technology or metrics that capture the level of participa- tion or adoption. Equally important is the need for process performance metrics. Process metrics are collected at the initial stage of project or problem identification. Current-state metrics are then benchmarked against internal indicators. When there are no internal indicators to benchmark against, a suitable course of action is to benchmark against an external source such as a similar business practice within a dif- ferent industry. Consider examining the hotel room change-over strategy or the cus- tomer service approach of Walt Disney Company or Ritz Carlton hotels, for example, to determine suitable metrics for a particular project or focus area.

The right workflow complement will provide the organization with the data it needs to understand operational and clinical performance. This area is highlighted through the need for healthcare organizations to capture MACRA measures. Good metrics should tell the story of accomplishment. The presence of technology alone does not guarantee an organization’s ability to capture and report on these measures without also addressing the surrounding workflow. Metrics should focus on the vari- ables of time, quality, and costs. Table 13-1 provides examples of relevant metrics.

MACRA highlights the need for healthcare organizations to collect informa- tion that represents the impact of technology on patient outcomes. Furthermore, data are necessary to demonstrate how a process is performing in its current state. In spite of the MACRA mandates, the need to collect data to demonstrate improve- ment in workflow— though it remains strong—is all too often absent in implemen- tation or redesign efforts. A team cannot demonstrate improvements to an existing

258 CHAPTER 13 Workflow and Beyond Meaningful Use

Table 13-1 Examples of Metrics

Turnaround times Cycle times Throughput

Change-over time Set-up time System availability

Patient satisfaction Employee satisfaction

process without collecting information about how the process is performing today. Current-state measures also help the process team validate that the correct area for improvement was identified. Once a process improvement effort is over and the new solution has been implemented, postimprovement measures should be gathered to demonstrate progress.

In some organizations, the informatics professional reports to the director of operations, the chief information officer, or the chief operations officer. In this rela- tionship, the need to demonstrate operational measures is even stronger. Operational measures such as turnaround times, throughput, and equipment or technology avail- ability are some of the measures captured.

Future Directions Workflow analysis is not an optional part of clinical implementations, but rather a necessity for safe patient care supported by technology. The ultimate goal of work- flow analysis is not to “pave the cow path,” but rather to create a future-state solu- tion that maximizes the use of technology and eliminates non-value-added activities. Although many tools to accomplish workflow redesign are available, the best method is the one that complements the organization and supports the work of clinicians. Re- designing how people do work will evidentially create change; thus the nursing infor- maticist will need to apply change management principles for the new way of doing things to take hold.

Workflow analysis has been described in this chapter within the context of the most widely accepted tools that are fundamentally linked to the concepts of Six Sigma/Lean. Other methods of workflow analysis exist and may become commonly used to assess clinical workflow. An example of an alternative workflow analysis tool is the use of radio frequency badges to detect movement within a defined clinical area. Clinician and patient movements may be tracked using these devices, and correspond- ing actions may be documented, painting a picture of the workflow for a specific area (Vankipuram, 2010).

Another example of a workflow analysis tool involves the use of modeling soft- ware. An application such as ProModel provides images of the clinical work area where clinician workflows can be plotted out and reconfigured to best suit the needs of a specific area. Simulation applications enable decision makers to visualize realistic scenarios and draw conclusions about how to leverage resources, implement technol- ogy, and improve performance. Other vendors that offer simulation applications in- clude Maya and Autodesk.

Healthcare organizations need to consider how other industries have analyzed and addressed workflow to streamline business practices and improve quality outputs to glean best practices that might be incorporated into the healthcare industry’s own clinical and business approaches. First, however, each healthcare organization must step outside itself and recognize that not all aspects of patient care are unique; con- sequently, many aspects of care can be subjected to standardization. Many models of workflow redesign from manufacturing and the service sector can be extrapolated to health care. The healthcare industry is facing difficult economic times and can benefit from performance improvement strategies used in other industries.

Future Directions 259

Although workflow analysis principles have been described within the context of acute and ambulatory care in this chapter, the need to perform process analysis on a macro level will expand as more organizations move forward with health information exchanges and medical home models. A health information exchange (HIE) requires the nursing informaticist to visualize how patients move through the entire continuum of care and not just a specific patient care area.

Technology initiatives will become increasingly complex in the future. In turn, nursing informaticists will need greater preparation in the area of process analysis and improvement techniques to meet the growing challenges that technology brings and the operational performance demands of fiscally impaired healthcare organizations.

Summary Meaningful use (MU) reflected the rules and regulations arising from ARRA. MA- CRA has changed the game and how payment will be determined. EHR adoptions “represent a small step rather than a giant leap forward” (Murphy, 2013, para. 1). Workflows integrating technology provide the healthcare professional with the data necessary to make informed decisions. This quality data must be collected and cap- tured to meet MACRA measures. Nurses must be involved in “meaningful data col- lection and reporting. Documentation by nurses can tell what’s going on with the patient beyond physical exams, test results, and procedures” (Daley, 2013, para. 5).

Workflow redesign is a critical aspect of technology implementation. When done well, it yields technology that is more likely to achieve the intended patient outcomes and safety benefits. Nursing informatics professionals are taking on a greater role with respect to workflow design, and this aspect of practice will grow in light of MACRA-driven measures. Other initiatives that impact hospital performance will also drive informatics professionals to influence how technology is used in the context of workflow to improve the bottom line for their organizations. In an ideal world, nurse informaticists who are experts at workflow analysis will be core members of every technology implementation team.

260 CHAPTER 13 Workflow and Beyond Meaningful Use


1. What do you perceive as the current obstacles to redesigning workflow within your clinical setting?

2. Thinking about your last implementation, were you able to challenge the poli- cies and practices that constitute today’s workflow or were you able to create a workflow solution that eliminated non–value-added steps?

3. Is the workflow surrounding technology usage providing the healthcare orga- nization with the data it needs to make decisions and eventually meet MACRA criteria?

4. How does the current educational preparation need to change to address the skills necessary to perform workflow analysis and redesign clinical processes?

5. Describe the role of the nurse informaticist as the payment programs change related to MACRA.

References Agency for Healthcare Research and Quality (AHRQ). (n.d.). Workflow assessment for

health IT toolkit. Retrieved from http://healthit.ahrq.gov/health-it-tools-and-resources /workflow-assessment-health-it-toolkit

Agency for Healthcare Research and Quality (AHRQ). (2008). Patient safety and quality: An evidence-based handbook for nurses. Retrieved from http://www.ahrq.gov/qual/nurseshdbk

American Association for Justice. (2016). Medical errors. Retrieved from https://www.justice.org /what-we-do/advocate-civil-justice-system/issue-advocacy/medical-errors

American Nurses Association. (2015). Nursing informatics: Scope and standards of practice (2nd ed.). Silver Springs, MD: Author.

Bucur, A., van Leeuwen, J., Christodoulou, N., Sigdel, K., Argyri, K., Koumakis, L., . . . Stamatakos, G. (2016). Workflow-driven clinical decision support for personalized oncology. BMC Medical Informatics & Decision Making, 16, 151–162. doi:10.1186/s12911-016-0314-3

Campbell, E., Guappone, K., Sittig, D., Dykstra, R., & Ash, J. (2009). Computerized provider order entry adoption: Implications for clinical workflow. Journal of General Internal Medicine, 24(1), 21–26.

Centers for Medicare & Medicaid Services (CMS). (2016a). Electronic health records (EHR) incentive programs. Retrieved from https://www.cms.gov/Regulations-and-Guidance /Legislation/EHRIncentivePrograms/index.html

Centers for Medicare & Medicaid Services (CMS). (2016b). MACRA: Delivery system reform, Medicare payment reform. Retrieved from https://www.cms.gov/Medicare/Quality-Initiatives -Patient-Assessment-Instruments/Value-Based-Programs/MACRA-MIPS-and-APMs/MACRA -MIPS-and-APMs.html

Centers for Medicare & Medicaid Services (CMS). (2016c). CMS quality measure development plan: Supporting the transition to the merit-based incentive payment system (MIPS) and alternative payment models (APMs). Retrieved from https://www.cms.gov/Medicare/Quality -Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/MACRA-MIPS-and-APMs /Final-MDP.pdf

Clancy, T., Delaney, C., Morrison, B., & Gunn, J. (2006). The benefits of standardized nursing languages in complex adaptive systems such as hospitals. Journal of Nursing Administration, 36(9), 426–434.

Daley, K. (2013). Making HIT meaningful for nursing and patients. The American Nurse. Retrieved from http://www.theamericannurse.org/index.php/2011/08/01/making-hit -meaningful-for-nursing-and-patients

Earl, M., Sampler, J., & Sghort, J. (1995). Strategies for business process reengineering: Evidence from field studies. Journal of Management Information Systems, 12(1), 31–56.

Gugerty, B., Maranda, M. J., Beachley, M., Navarro, V. B., Newbold, S., Hawk, W., . . . Wilhelm, D. (2007). Challenges and Opportunities in documentation of the nursing care of patients. Baltimore, MD: Documentation Work Group, Maryland Nursing Workforce Commission. Retrieved from http://mbon.maryland.gov/Documents/documentation_challenges.pdf

Hagland, M. (2016). CMS announces long-awaited MACRA proposed rule; Program includes MU makeover for MDs. Healthcare Informatics. Retrieved from http://www.healthcare -informatics.com/article/breaking-news-cms-announces-plan-replace-meaningful-use -physicians-new-quality-reporting

Healthcare Information Management Systems Society (HIMSS). (2010). ME-PI toolkit: Process management, workflow & mapping: Tools, tips and case studies to support the understanding, optimizing and monitoring of processes. Retrieved from http://www.himss .org/me-pi-toolkit-change-management

References 261

Healthcare Information Management Systems Society (HIMSS). (2015). Nursing informatics impact study. Retrieved from http://www.himss.org/ni-impact-survey

Kotter, J. P. (1996). Leading change (pp. 33–147). Cambridge, MA: Harvard Business Press. Merriam-Webster Online Dictionary. (2016). Optimization. Retrieved from http://www.merriam

-webster.com/dictionary/optimization Murphy, K. (2013). Nursing approach to meaningful use, EHR adoption: CIO series. Retrieved

from https://ehrintelligence.com/news/nursing-approach-to-meaningful-use-ehr-adoption-cio -series

Qualis Health. (2011). Workflow analysis. Retrieved from http://www.qualishealthmedicare .org/healthcare-providers/improvement-fundamentals/workflow-analysis

Stokowski, L. (2013). Electronic nursing documentation: Charting new territory. Medscape. Retrieved from http://www.medscape.com/viewarticle/810573

Thompson, C., Kell, C., Shetty, R., & Banerjee, D. (2016). Clinical workflow redesign leveraging informatics improves patient outcomes. Heart & Lung, 45(4), 380–381.

Vankipuram, K. (2010). Toward automated workflow analysis and visualization in clinical environment. Journal of Biomedical Informatics. doi:10.1016/jbi.2010.05.015

Yuan, M., Finley, G., Long, J., Mills, C. & Johnson, R. (2013). Evaluation of user interface and workflow design of a bedside nursing clinical decision support system. Interactive Journal of Medical Research, 2(1), e4.

262 CHAPTER 13 Workflow and Beyond Meaningful Use

Nursing Informatics Practice Applications: Care Delivery Chapter 14 The Electronic Health Record and Clinical Informatics

Chapter 15 Informatics Tools to Promote Patient Safety and Quality Outcomes

Chapter 16 Patient Engagement and Connected Health

Chapter 17 Using Informatics to Promote Community/Population Health

Chapter 18 Telenursing and Remote Access Telehealth

section IV

Nursing information systems must support nurses as they fulfill their roles in delivering quality patient care. Such systems must be responsive to nurses’ needs, allowing them to manage their data and information as needed and providing access to necessary references, literature sources, and other networked departments. Nurses have always practiced in a field where they have needed to use their ingenuity, resourcefulness, creativity, initiative, and skills. To improve patient care and advance the science of nursing, clinicians as knowledge workers must apply these same abilities and skills to become astute users of available information systems.

In this section, the reader learns about clinical practice tools, electronic health records, and clinical information systems; informatics tools to enhance patient safety, provide consumer information, and meet education needs; population and community health tools; and telehealth and telenursing.

Information systems, electronic documentation, and electronic health records are changing the way nurses and physicians practice. Nursing informatics systems are also changing how patients enter and receive data and information. Some institutions, for example, are permitting patients to access their own records electronically via the Internet or a dedicated patient portal. Confidentiality and privacy issues loom with these new electronic systems. HIPAA regulations (covered in the Perspectives on Nursing Informatics section) and professional ethics principles (covered in the Building Blocks of Nursing Informatics section) must remain at the forefront when clinicians interact electronically with intimate patient data and information.

The material within this book is placed within the context of the Foundation of Knowledge model (Figure IV-1) to meet the needs of healthcare delivery systems, organizations, patients, and nurses. Readers should continue to assess their personal knowledge progression. The Foundation of Knowledge model challenges us to reflect on how our knowledge foundation is ever-changing and to appreciate that acquiring new information is a key resource for knowledge building. This section addresses the information systems with which clinicians interact in their healthcare environments as affected by legislation, professional codes of ethics, consumerism, and reconcep- tualization of practice paradigms, such as in telenursing. All of the various nursing roles—practice, administration, education, research, and informatics—involve the science of nursing.

264 seCtIoN IV Nursing Informatics Practice Applications: Care Delivery

Figure IV-1 Foundation of Knowledge Model Designed by Alicia Mastrian

KA - Knowledge acquisition KD - Knowledge dissemination KG - Knowledge generation KP - Knowledge processing



Information Information



Bytes Bytes



Bits Data



Bytes Bytes Bits

Bits Data Information






seCtIoN IV Nursing Informatics Practice Applications: Care Delivery 265

Key terms » Administrative

processes » American Recovery

and Reinvestment Act of 2009 (ARRA)

» Connectivity » Decision support

» Electronic communication

» Electronic health records

» Health information » Health Information

Technology for

Economic and Clinical Health Act of 2009 (HITECH)

» Interoperability » Meaningful use » Order entry


» Patient support » Population health

management » Reporting » Results


1. Describe the common components of an electronic health record.

2. Assess the benefits of implementing an electronic health record.

3. Explore the ownership of an electronic health record.

4. Evaluate the flexibility of the electronic health record in meeting the needs of clinicians and patients.


Introduction The significance of electronic health records (EHRs) to nursing cannot be underestimated. Although EHRs on the surface suggest a simple automation of clinical documentation, in fact their implications are broad, ranging from the ways in which care is delivered, to the types of interactions nurses have with patients in conjunction with the use of technology, to the research surrounding EHRs that will inform nursing practice for tomorrow. Although EHR standards are evolving and barriers to adoption remain, the collective work has a positive momentum that will benefit clinicians and patients alike.

A basic knowledge of EHRs and nursing informatics is now consid- ered by many to be an entry-level nursing competency. Various nursing workgroups have delineated nursing informatics competencies from entry level to nursing informatics specialists, and other groups have identified competencies specific to the EHR. The American Health Information Management Association (AHIMA) collaborated with the Health Profes- sions Network and the Employment and Training Administration to create a graphic depiction of competencies necessary for EHR interaction. The Electronic Health Records Competency Model is divided into six levels: Personal Effectiveness Competencies, Academic Competencies, Workplace Competencies, Industry-Wide Technical Competencies, Industry-Sector Technical Competencies, and a Management Competencies level shared with Occupation Specific Requirements. The EHR Competency Model can be viewed at: www.careeronestop.org/CompetencyModel/competency -models/electronic-health-records.aspx. Hovering over each block in the model provides a definition of each of the competencies covered by the model. For example, the industry-sector technical competencies section includes health information literacy and skills, health informatics skills using the EHR, privacy and confidentiality of health information, and health information data technical security. This drive to adopt EHRs was

the electronic Health Record and Clinical Informatics Emily B. Barey, Kathleen Mastrian, and Dee McGonigle


CHAPteR 14

underscored with the passage of the Health Information technology for economic and Clinical Health Act of 2009 (HIteCH). It is essential that EHR competency be developed if nurses are to participate fully in the changing world of healthcare information technology.

This chapter has four goals. First, it describes the common components of an EHR. Second, it reviews the benefits of implementing an EHR. Third, it provides an overview of successful ownership of an EHR, including nursing’s role in promoting the safe adoption of EHRs in day-to-day practice. Fourth, it discusses the flexibility of an EHR in meeting the needs of both clinicians and patients and emphasizes the need for fully interoperable EHRs and clinical information systems (CISs).

setting the stage The U.S. healthcare system faces the enormous challenge of improving the quality of care while simultaneously controlling costs. EHRs were proposed as one solution to achieve this goal (Institute of Medicine [IOM], 2001). In January 2004, President George W. Bush raised the profile of EHRs in his State of the Union address by outlining a plan to ensure that most Americans have an EHR by 2014. He stated that “by computerizing health records we can avoid dangerous medical mistakes, reduce costs, and improve care” (Bush, 2004). This proclamation generated an increased demand for understanding EHRs and promoting their adoption, but relatively few healthcare organizations were motivated at that time to pursue adoption of EHRs. The Healthcare Information and Management Systems Society (HIMSS) has been tracking EHR adoption since 2005 through its “Stage 7” award, and in 2013 reported that most U.S. healthcare organizations (77%) were in Stage 3, reflecting only implementation of the basic EHR components of laboratory, radiology, and pharmacy ancillaries; a clinical data repository, including a controlled medical vocabulary; and simple nursing documentation and clinical decision support (HIMSS, 2013). Higher stages of the electronic medical record adoption model include more sophisticated use of clinical decision support systems (CDSSs) and medication administration tools, with HIMSS Stage 7—the highest level—consisting of EHRs that have data sharing and warehousing capabilities and that are completely interfaced with emergency and outpatient facilities (HIMSS Analytics, 2013). Real progress is being made on the adoption of more robust EHRs. HIMSS Analytics (2015) reports that 1,313 hospi- tals in the United States have achieved Stage 6 with full physician documentation, a robust CDSS, and electronic access to medical images. Healthcare IT News (2015) reported that, to date, over 200 hospitals have achieved Stage 7 and are totally paper- less, and that more organizations reach this goal every day.

In President Barack Obama’s first term in office, Congress passed the American Recovery and Reinvestment Act of 2009 (ARRA). This legislation included the HITECH Act, which specifically sought to incentivize health organizations and providers to become meaningful users of EHRs. These incentives came in the form of increased reimbursement rates from the Centers for Medicare and Medicaid Services (CMS); ultimately, the HITECH Act resulted in payment of a penalty by any healthcare organization that had not adopted an EHR by January 2015. The final rule was published by the Department of Health and Human Services (USDHHS) in July 2010 for the first phase of implementation. Stage 1 meaningful use criteria focused on data capture and sharing (USDHHS, 2010a). Stage 2 criteria, implemented in 2014,

268 CHAPteR 14 The Electronic Health Record and Clinical Informatics

advanced several clinical processes and promoted health information exchange (HIE) and more patient control over personal data. Stage 3, which has a target implemen- tation date of 2016, focuses on improved outcomes for individuals and populations, and introduction of patient self-management tools (HealthIT.gov, 2013).

Components of electronic Health Records Overview Before enactment of the ARRA, several variants of EHRs existed, each with its own terminology and each developed with a different audience in mind. The sources of these records included, for example, the federal government (Certification Commission for Healthcare Information Technology, 2007), the IOM (2003), the HIMSS (2007), and the National Institutes of Health (2006; Robert Wood Johnson Foundation [RWJF], 2006). Under ARRA, there is now an explicit requirement for providers and hospitals to use a certified EHR that meets a set of standard functional definitions to be eligible for the increased reimbursement incentive. Initially, USDHHS granted two organiza- tions the authority to accredit EHRs: the Drummond Group and the Certification Com- mission for Healthcare Information Technology. In 2015, there were five recognized bodies for testing and certifying EHRs (HealthIT.gov, 2015a). These bodies are autho- rized to test and certify EHR vendors against the standards and test procedures devel- oped by the National Institute of Standards and Technology (NIST) and endorsed by the Office of the National Coordinator for Health Information Technology for EHRs.

The initial NIST test procedure included 45 certification criteria, ranging from the basic ability to record patient demographics, document vital signs, and maintain an up-to-date problem list, to more complex functions, such as electronic exchange of clinical information and patient summary records (Jansen & Grance, 2011; NIST, 2010). Box 14-1 lists the 45 certification criteria outlined by NIST in 2010. These criteria have been updated several times since 2010, with the 2015 version developed after going out for public comment (HealthIT.gov, 2015b). Each iteration of certification criteria and testing procedures seeks to make the EHR more robust, interoperable, and functional to meet the needs of patients and users.

Despite the points articulated in the ARRA, the IOM definition of an EHR also remains a valid reference point. This definition is useful because it has distilled all the possible features of an EHR into eight essential components with an emphasis on functions that promote patient safety—a universal denominator that everyone in health care can accept. The eight components are (1) health information and data, (2) results management, (3) order entry management, (4) decision support, (5) electronic communication and connectivity, (6) patient support, (7) administra- tive processes, and (8) reporting and population health management (IOM, 2003). These initial core components, as well as more recent modifications described by the Health Resources and Services Administration (HRSA, n.d.) and the components of a comprehensive EHR identified by HealthIT.gov (Charles, Gabriel, & Searcy, 2015), are described in more detail here. With the exception of EHR infrastructure func- tions, such as security and privacy management, controlled medical vocabularies, and interoperability standards, the 45 initial NIST standards easily map into the IOM categories (Jansen & Grance, 2011).

Components of Electronic Health Records 269

270 CHAPteR 14 The Electronic Health Record and Clinical Informatics


Criteria # Certification Criteria

§170.302 (a) Drug–drug, drug–allergy interaction checks

§170.302 (b) Drug formulary checks

§170.302 (c) Maintain up-to-date problem list

§170.302 (d) Maintain active medication list

§170.302 (e) Maintain active medication allergy list

§170.302 (f)(1) Vital signs

§170.302 (f)(2) Calculate body mass index

§170.302 (f)(3) Plot and display growth charts

§170.302 (g) Smoking status

§170.302 (h) Incorporate laboratory test results

§170.302 (i) Generate patient lists

§170.302 (j) Medication reconciliation

§170.302 (k) Submission to immunization registries

§170.302 (l) Public health surveillance

§170.302 (m) Patient-specific education resources

§170.302 (n) Automated measure calculation

§170.302 (o) Access control

§170.302 (p) Emergency access

§170.302 (q) Automatic log-off

§170.302 (r) Audit log

§170.302 (s) Integrity

§170.302 (t) Authentication

§170.302 (u) General encryption

§170.302 (v) Encryption when exchanging electronic health information

§170.302 (w) Accounting of disclosures (optional)

§170.304 (a) Computerized provider order entry

§170.304 (b) Electronic prescribing

§170.304 (c) Record demographics

§170.304 (d) Patient reminders

§170.304 (e) Clinical decision support

§170.304 (f) Electronic copy of health information

§170.304 (g) Timely access

§170.304 (h) Clinical summaries

§170.304 (i) Exchange clinical information and patient summary record

§170.304 (j) Calculate and submit clinical quality measures

§170.306 (a) Computerized provider order entry

§170.306 (b) Record demographics

§170.306 (c) Clinical decision support

§170.306 (d)(1) Electronic copy of health information

§170.306 (d)(2) Electronic copy of health information

Note: For discharge summary

§170.306 (e) Electronic copy of discharge instructions

§170.306 (f) Exchange clinical information and patient summary record

§170.306 (g) Reportable lab results

§170.306 (h) Advance directives

§170.306 (i) Calculate and submit clinical quality measures

Reproduced from National Institute of Standards and Technology (NIST). (2010). Meaningful use test method: Approved test procedures version 1.0. Retrieved from http://healthcare.nist.gov/use_testing/finalized_requirements.html

Health Information and Data Health information and data comprise the patient data required to make sound clinical decisions, including demographics, medical and nursing diagnoses, medication lists, allergies, and test results (IOM, 2003). This component of the EHR also includes care management data regarding details of patient visits and interactions with patients, medication reconciliation, consents, and directives (HRSA, n.d.). A comprehensive EHR will also contain nursing assessments and problem lists (Charles, Gabriel, & Searcy, 2015).

Components of Electronic Health Records 271

Results Management Results management is the ability to manage results of all types electronically, including laboratory and radiology procedure reports, both current and historical (IOM, 2003).

Order Entry Management order entry management is the ability of a clinician to enter medication and other care orders, including laboratory, microbiology, pathology, radiology, nursing, and supply orders; ancillary services; and consultations, directly into a computer (IOM, 2003). A comprehensive EHR will also contain nursing orders (Charles, Gabriel, & Searcy, 2015).

Decision Support Decision support entails the use of computer reminders and alerts to improve the diagnosis and care of a patient, including screening for correct drug selection and dosing, screening for medication interactions with other medications, preventive health reminders in such areas as vaccinations, health risk screening and detection, and clinical guidelines for patient disease treatment (IOM, 2003).

Electronic Communication and Connectivity electronic communication and connectivity include the online communication among healthcare team members, their care partners, and patients, including email, Web messaging, and an integrated health record within and across settings, institutions, and telemedicine (IOM, 2003). This component has been expanded to include in- terfaces and interoperability required to exchange health information with other providers, laboratories, pharmacies (e-prescribing), patients, and government disease registries (HRSA, n.d., para. 2)

Patient Support Patient support encompasses patient education and self-monitoring tools, including interactive computer-based patient education, home telemonitoring, and telehealth systems (IOM, 2003).

Administrative Processes Administrative processes are activities carried out by the electronic scheduling, billing, and claims management systems, including electronic scheduling for inpatient and outpatient visits and procedures, electronic insurance eligibility validation, claim au- thorization and prior approval, identification of possible research study participants, and drug recall support (IOM, 2003).

Reporting and Population Health Management Reporting and population health management are the data collection tools to support public and private reporting requirements, including data represented in a standard- ized terminology and machine-readable format (IOM, 2003).

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NIST has not provided an exhaustive list of all possible features and functions of an EHR. Consequently, different vendor EHR systems combine different compo- nents in their offerings, and often a single set of EHR components may not meet the needs of all clinicians and patient populations. For example, a pediatric setting may demand functions for immunization management, growth tracking, and more robust order entry features to include weight-based dosing (Spooner & Council on Clinical Information Technology, 2007). These types of features may not be pro- vided by all EHR systems, and it is important to consider EHR certification to be a minimum standard. See Figure 14-1 for a graphic depiction of EHR functions and communication capabilities.

Another group that focuses on EHR standards and functionality is Health Level Seven International (HL7). Founded in 1987, “Health Level Seven International (HL7) is a not-for-profit, ANSI-accredited standards developing organization dedicated to providing a comprehensive framework and related standards for the exchange, inte- gration, sharing, and retrieval of electronic health information that supports clinical practice and the management, delivery and evaluation of health services” (Health Level Seven International, n.d., para. 1). This group concentrates on developing the

Figure 14-1 EHR Functions and Communication Capabilities Reproduced from American Hospital Association. (2010). The road to meaningful use: What it takes to implement EHR systems in hospitals. Retrieved from http://www.aha.org/research/reports/tw/10apr-tw-HITmeanuse.pdf

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behind-the-scenes programming standards (Level Seven is the application level of the Open Systems Interconnection model) for interfaces to ensure interoperability and connectivity among systems.

Advantages of electronic Health Records Measuring the benefits of EHRs can be challenging. Possible methods to estimate EHR benefits include using vendor-supplied data that have been retrieved from their customers’ systems, synthesizing and applying studies of overall EHR value, creating logical engineering models of EHR value, summarizing focused studies of elements of EHR value, and conducting and applying information from site visits (HealthIT.gov, 2012; Thompson, Osheroff, Classen, & Sittig, 2007).

Early on, the four most common benefits cited for EHRs were (1) increased delivery of guidelines-based care, (2) enhanced capacity to perform surveillance and monitoring for disease conditions, (3) reduction in medication errors, and (4) decreased use of care (Chaudhry et al., 2006; HealthIT.gov, 2012). These findings were echoed by two similar literature reviews. The first review (Dorr et al., 2007) focused on the use of informatics systems for managing patients with chronic illness. It found that the processes of care most positively impacted were guidelines adherence, visit frequency (i.e., a decrease in emergency department visits), provider documentation, patient treatment adherence, and screening and testing.

The second review (Shekelle, Morton, & Keeler, 2006) was a cost–benefit analysis of health information technology completed by the Agency for Healthcare Research and Quality (AHRQ) that studied the value of an EHR in the ambulatory care and pediatric settings, including its overall economic value. The AHRQ study highlighted the common findings already described, but also noted that most of the data available for review came from six leading healthcare organizations in the United States, under- scoring the challenge of generalizing these results to the broader healthcare industry. As noted previously by the HIMSS Stage 7 Awards, the challenge to generalize results persists in the hospital arena, with fewer than 1% of U.S. hospitals or eight leading organizations providing most of the experience with comprehensive EHRs (HIMSS, 2010a). Finally, the literature reviews cited here indicated that there are a limited num- ber of hypothesis-testing studies of EHRs and even fewer that have reported cost data.

The descriptive studies do have value, however, and should not be hastily dismissed. Although not as rigorous in their design, they do describe the advantages of EHRs well and often include useful implementation recommendations learned from practi- cal experience. As identified in these types of reviews, EHR advantages include simple benefits, such as no longer having to interpret poor handwriting and handwritten orders, reduced turnaround time for laboratory results in an emergency department, and decreased time to administration of the first dose of antibiotics in an inpatient nursing unit (HealthIT.gov, 2012; Husk & Waxman, 2004; Smith et al., 2004). In the ambulatory care setting, improved management of cardiac-related risk factors in patients with diabetes and effective patient notification of medication recalls have been demonstrated to be benefits of the EHR (Jain et al., 2005; Reed & Bernard, 2005). Two other unique advantages that have great potential are the ability to use the EHR and decision support functions to identify patients who qualify for research

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studies or who qualify for prescription drug benefits offered by pharmaceutical companies at safety-net clinics and hospitals (Embi et al., 2005; Poprock, 2005).

The HIMSS Davies Award may be the best resource for combined quantitative and qualitative results of successful EHR implementation. The Davies Award recognizes healthcare organizations that have achieved both excellence in implementation and value from health information technology (HIMSS, 2010a). One winner demonstrated a significant avoidance of medication errors because of bar-code scanning alerts, a $3 million decrease in medical records expenses as a result of going paperless, and a 5% reduction of duplicate laboratory orders by using computerized provider order entry alerting (HIMSS, 2010b). Another winner noted a 13% decrease in ad- verse drug reactions through the use of computerized physician order entry; it also achieved a decrease in methicillin-resistant Staphylococcus aureus (MRSA) nosocomial infections from 9.8 per 10,000 discharges to 6.4 per 10,000 discharges in less than a year using an EHR flagging function, which made clinicians immediately aware that contact precautions were required for MRSA-positive patients (HIMSS, 2009). At both organizations, there was qualitative and quantitative evidence of high rates of end user adoption and satisfaction with use of the EHR.

A 2011 study of the effects of EHR adoption on nurse perceptions of quality of care, communication, and patient safety documented that nurses report better care outcomes and fewer concerns with care coordination and patient safety in hospitals with a basic EHR (Kutney-Lee & Kelly, 2011). In this study, nurses perceived that in hospitals with a functioning EHR, there was better communication among staff, especially during patient transfers, and fewer medication errors. Bayliss et al. (2015) demonstrated that an integrated care system utilizing an EHR resulted in fewer hos- pital readmissions and emergency room visits for over 12,000 seniors with multiple health challenges.

Without an EHR system, any of these benefits would be very difficult and costly to accomplish. Thus, despite limited standards and published studies, there is enough evidence to embrace widespread implementation of the EHR (Halamka, 2006; Heal- thIT.gov, 2012), and certainly enough evidence to warrant further study of the use and benefits of EHRs. Box 14-2 describes some of the specific CIS functions of an EHR.

A more recent description of the benefits of an EHR by HealthIT.gov (2014) emphasizes that EHRs hold the promise of transforming healthcare; specifically, EHRs will lead to:

• Better health care by improving all aspects of patient care, including safety, effectiveness, patient-centeredness, communication, education, timeliness, effi- ciency, and equity

• Better health by encouraging healthier lifestyles in the entire population, includ- ing increased physical activity, better nutrition, avoidance of behavioral risks, and wider use of preventative care

• Improved efficiencies and lower healthcare costs by promoting preventative medicine and improved coordination of healthcare services, as well as by reducing waste and redundant tests

• Better clinical decision making by integrating patient information from multiple sources (para. 4)

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Denise Tyler A CIS is a technology-based system applied at the point of care and designed to support care by providing instant access to information for clinicians. Early CISs implemented prior to the advent of EHRs were limited in scope and provided such information as interpretation of laboratory results or a medication formu- lary and drug interaction information. With the implementation of EHRs, the goal of many organizations is to expand the scope of the early CISs to become comprehensive systems that provide clinical decision support, an electronic pa- tient record, and in some instances professional development and training tools. Benefits of such a comprehensive system include easy access to patient data at the point of care; structured and legible information that can be searched easily and lends itself to data mining and analysis; and improved patient safety, especially the prevention of adverse drug reactions and the identification of health risk fac- tors, such as falls.


The ability to measure outcomes can be enhanced or impeded by the way an information system is designed and used. Although many practitioners can paint a very good picture of the patient by using a narrative (free text), employing this mode of expression in a clinical system without the use of a coded entry makes it difficult to analyze the care given or the patient’s response. Free-text reporting also leads to inconsistencies of reporting from clinician to clinician and patient information that is fragmented or disorganized. This can limit the usefulness of patient data to other clinicians and interfere with the ability to create reports from the data for quality assurance and measurement purposes. Moreover, not all clinicians are equally skilled at the free-text form of communication, yielding inconsistent quality of documentation. Integrating standardized nursing termi- nologies into computerized nursing documentation systems enhances the ability to use the data for reporting and further research.

According to the IOM (2012), “Payers, healthcare delivery organizations and medical product companies should contribute data to research and analytic con- sortia to support expanded use of care data to generate new insights” (para. 2). McLaughlin and Halilovic (2006) described the use of clinical analytics to pro- mote medical care outcomes research. The use of a CIS in conjunction with stan- dardized codes for patient clinical issues helps to support the rigorous analysis of clinical data. Outcomes data produced as part of these analyses may include length of stay, mortality, readmissions, and complications. Future goals include the ability to compare data and outcomes across various institutions as a means of developing clinical guidelines or best practices guidelines. With the implemen- tation of a comprehensive CIS, similar analyses of nursing outcomes could also be performed and shared. Likewise, such a system could aid nurse administrators in cross-unit comparisons and staffing decisions, especially when coupled with

acuity systems data. In addition, clinical analytics can support required data reporting functions, especially those required by accreditation bodies.


Evidence-based practice (EBP) can be thought of as the integration of clinical expertise and best practices based on systematic research to enhance decision making and improve patient care. References supporting EBP, such as clinical guidelines, are available for review at the click of a mouse or the press of a few keystrokes. The CIS’s prompting capabilities can also reinforce the practice of looking for evidence to support nursing interventions rather than relying on how things have been done historically. This approach enhances processing and understanding of the information and allows the nurse to apply the infor- mation to other areas, increasing the knowledge obtained about why certain conditions or responses result in prompts for additional questions or actions.

To incorporate EBP into the practice of clinical nursing, the information needs to be embedded in the computerized documentation system so that it is part of the workflow. The most typical way of embedding this timely information is through clinical practice guidelines. The resulting interventions and clinical outcomes need to be measurable and reportable for further research. The sup- porting documentation for the EBP needs to be easily retrievable and meaningful. Links, reminders, and prompts can all be used as vehicles for transmission of this information. The format needs to allow for rapid scanning, with the ability to expand the amount of information when more detail is required or desired. Balancing a consistency in formatting with creativity can be difficult but is worth the effort to stimulate an atmosphere for learning.

EBP is supported by translational research, an exciting movement that has enormous potential for the sharing and use of EBP. The use of translational research to support EBP may help to close the gap between what is known (research) and what is done (practice).

tHe CIs As A stAFF DeVeLoPMeNt tooL

Joy Hilty, a registered nurse from Kaweah Delta, came up with a creative way to provide staff development or education without taking staff away from the bedside to a classroom setting. She created pop-up boxes on the opening charting screens for all staff who chart on the computer. These pop-ups vary in color and content and include a short piece of clinical information, along with a question. Staff can earn vacations from these pop-ups for as long as 14 days by emailing the correct answer to the question. This medium has provided information, stimulation, and a definite benefit: the vacation from the pop-up boxes. The pop-up box educa- tion format has also encouraged staff to share their answers, thereby creating inter- action, knowledge dissemination, and reinforcement of the education provided.

Embedding EBP into nursing documentation can also increase the compliance with Joint Commission core measures, such as providing information on influenza

Advantages of Electronic Health Records 277

standardized terminology and the eHR As we inch closer to interoperable EHRs that provide for seamless health information exchange among providers and healthcare institutions, the need for standardizing terminologies becomes ever clearer. Consider also the trend toward value-based care reimbursements, in which healthcare data are mined “to demonstrate nursing’s contributions to improving the cost, quality, and efficiency of care, key elements of the value equation” (Adams, Ponte, & Somerville, 2016, p. 127). EHR data must be

and pneumococcal vaccinations to at-risk patients. In the author’s experience at Kaweah Delta, educating staff via classes, flyers, and storyboards was not successful in improving compliance with the documentation of immunization status or offering education on these vaccinations to at-risk patients. Embedding the prompts, information, and related questions in the nursing documentation with a link to the protocol and educational material, however, improved the compliance to 96% for pneumococcal vaccinations and to 95% for influenza vaccinations (Hettinger, 2007).

As more information is stored electronically, nurse informaticists must trans- late the technology so that the input and retrieval of information are developed in a manner that is easy for clinicians to learn and use. A highly usable product should decrease errors and improve information entry and retrieval. Nurse infor- maticists must be able to work with staff and expert users to design systems that meet the needs of the staff who will actually use the systems. The work is not done after the system is installed; the system must continue to be developed and improved, because as staff use the system, they will be able to suggest changes to improve it. This ongoing revision should result in a system that is mature and meets the needs of the users.

In an ideal world, all clinical documentation will be shared through a national database, in a standard language, to enable evaluation of nursing care, increase the body of evidence, and improve patient outcomes. With minimal effort, the information will be translated into new research that can be analyzed and linked to new evidence that will be intuitively applied to the CIS. Alerts will be meaningful and will be patient and provider specific. The steps required of the clinician to find current, reliable information will be almost transparent, and the information will be presented in a personalized manner based on user preferences stored in the CIS.


Hettinger, M. (2007, March). Core measure reporting: Performance improvement. Visalia, CA: Kaweah Delta Health Care District.

Institute of Medicine (IOM). (2012). Best care at lower cost. Retrieved from https://www .nap.edu/catalog/13444/best-care-at-lower-cost-the-path-to-continuously-learning

McLaughlin, T., & Halilovic, M. (2006). Clinical analytics, rigorous coding bring objectivity to quality assertions. Medical Staff Update Online, 30(6). Retrieved from http://med.stanford.edu/shs/update/archives/JUNE2006/analytics.htm

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formatted in a machine-readable manner in order to support interoperable exchange of information and data mining. An important distinction that needs to be made here is the difference between interface terminologies (NANDA, NIC, or NOC) and refer- ence terminologies (SMOMED-CT, LOINC).

While interface terminologies play an important role in promoting direct entry of categorical data by health care providers, both terminology developers and the standards community historically have focused on other types of terminologies, including reference and administrative (rather than on inter- face) terminologies. Such terminologies are generally designed to provide exact and complete representations of a given domain’s knowledge, includ- ing its entities and ideas and their interrelationships. For example, reference terminologies can support the storage, retrieval, and classification of clinical data; their contents correspond to the internal system representation storage formats to which interface terminologies are typically mapped. (Rosenbloom, Miller, Johnson, Elkin, & Brown, 2006, p. 278)

The various interface terminologies and their subsets are coded in the EHR and typically presented to the user in dropdown menus. Users may also be able to use a search function in the EHR to identify the most appropriate term that represents the patient’s condition(s). Bronnert, Masarie, Naeymi-Rad, Rose, and Aldin (2012) described the value of an interface terminology for clinician workflow:

Clinicians interact with interface terminology when documenting diagnoses and procedures in the patient’s electronic record. The physician performs searches using the search functionality in designated locations in the EHR, which returns terms to the provider to select the appropriate problem or procedure. The physician [nurse] selects the appropriate term to capture the clinical intent. The term(s) populate predetermined fields in the electronic record. The selected term contains mappings to one or more industry stan- dard terminologies, such as ICD or SNOMED CT. The “behind-the-scenes” mappings allow the physician to focus on patient care while at the same time capturing the necessary administrative and reference codes. (para. 17)

The National Library of Medicine has been designated as the central coordinating body for clinical terminologies by the USDHHS. (See Box 14-3 for a list and descrip- tion of administrative and reference terminologies used in an EHR.) Recall the ongo- ing work of nursing groups looking to standardize nursing terminologies to capture and codify the work of nursing. (See Chapter 6 for a list of approved nursing termi- nologies.) In 2015, the American Nurses Association reaffirmed its support for the use of standardized terminologies:

The purpose of this position statement is to reaffirm the American Nurses Association’s (ANA) support for the use of recognized terminologies sup- porting nursing practice as valuable representations of nursing practice and to promote the integration of those terminologies into information technol- ogy solutions. Standardized terminologies have become a significant vehicle for facilitating interoperability between different concepts, nomenclatures, and information systems. (para. 1)

Standardized Terminology and the EHR 279

Because no single model of standardized terminology for health care or nurs- ing can represent all of the contributions to the health of a patient, work is ongo- ing to map terminologies to one another. For example, Kim, Hardiker, and Coenen (2014) studied the degree of similarity between the International Classification for Nursing Practice (ICNP) and the Systematized Nomenclature of Medicine–Clinical Terms (SNOMED–CT); while they identified some areas of overlap, they cautioned that there is still more work to be done to truly represent nursing concepts in the EHR. Adams et al. (2016) issued a call to action to Chief Nursing Officers (CNOs): “CNOs must begin partnering with and influencing EHR developers and vendors to ensure the EHRs implemented in their organizations capture nursing content using a standardized taxonomy that is evidence based and mapped to SNOMED-CT and LOINC” (p. 127). Ongoing efforts to map nursing problem lists to SNOMED-CT are evident in the work of Matney and colleagues (2011) and on the National Library of Medicine website (www.nlm.nih.gov/hit_interoperability.html). It is probably safe to say that the number of different types of EHRs and the variability of EHRs are likely to contract and converge as the demand for robust systems supporting interoper- ability expands. Nurse informatics specialists and CNOs participating in the selection and implementation of EHRs must ask a critical question: To what extent are nursing care contributions visible, retrievable, and accurately represented in this EHR?

ownership of electronic Health Records The implementation of an EHR has the potential to affect every member of a health- care organization. The process of becoming a successful owner of an EHR has

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Administrative (Billing) Terminologies * ICD-10 (International Classification of Diseases, Version 10): Medical

diagnosis code set * CPT (Current Procedural Terminology): Used to code procedures for billing


• SNOMED CT (Systematized Nomenclature of Medicine—Clinical Terms): Comprehensive clinical terminology (mapping to this terminology is ongoing, including nursing-orders mapping)

• LOINC (Logical Observation Identifier Names and Codes): Universal codes for laboratory and clinical observations

• RxNorm: Terminology system for drug names, providing links to drug vo- cabularies and interaction software

• Unified Medical Language System (UMLS) and the Metathesaurus: Support terminology integration efforts and online searches (not a terminology system) See the U.S. National Library of Medicine website for more comprehensive

information: www.nlm.nih.gov/hit_interoperability.html

multiple steps and requires integrating the EHR into the organization’s day-to-day operations and long-term vision, as well as into the clinician’s day-to-day practice. All members of the healthcare organization—from the executive level to the clinician at the point of care—must feel a sense of ownership to make the implementation suc- cessful for themselves, their colleagues, and their patients. Successful ownership of an EHR may be defined in part by the level of clinician adoption of the tool, and this section reviews key steps and strategies for the selection, implementation and evalua- tion, and optimization of an EHR in pursuit of that goal.

Historically, many systems were developed locally by the information technology department of a healthcare organization. It was not unusual for software developers to be employed by the organization to create needed systems and interfaces between them. As commercial offerings were introduced and matured, it became less and less common to see homegrown or locally developed systems.

As this history suggests, the first step of ownership is typically a vendor selection process for a commercially available EHR. During this step, it is important to survey the organization’s level of interest, identify possible barriers to participation, docu- ment desired functions of an EHR, and assess the willingness to fund the implemen- tation (Holbrook, Keshavjee, Troyan, Pray, & Ford, 2003). Although clinicians, as the primary end users, should drive the project, the assessment should also include the needs and readiness of the executive leadership, information technology, and project management teams. It is essential that leadership understands that this type of project is as much about redesigning clinical work as it is about technically automating it and that they agree to assume accountability for its success (Goddard, 2000). In addition, this pre-acquisition phase should concentrate on understanding the current state of the health information technology industry to identify appropriate questions and the next steps in the selection process (American Organization of Nurse Execu- tives, 2009). These first steps begin to identify any organizational risks related to successful implementation and pave the way for initiating a change management process to educate the organization about the future state of delivering health care with an EHR system.

The second step of the selection process is to select a system based on the organi- zation’s current and predicted needs. It is common during this phase to see a demon- stration of several vendors’ EHR products. Based on the completed needs assessment, the organization should establish key evaluation criteria to compare the different vendors and products. These criteria should include both subjective and objective items that cover such topics as common clinical workflows, decision support, report- ing, usability, technical build, and maintenance of the system. Providing the vendor with these guidelines will ensure that the process meets the organization’s needs; however, it is also essential to let the vendor demonstrate a proposed future state from its own perspective. This activity is critical to ensuring that the vendor’s vision and the organization’s vision are well aligned (Konschak & Shiple, n.d.). It also helps spark dialogue about the possible future state of clinical work at the organization and the change required in obtaining it. Such demonstrations not only enable the organization to compare and contrast the features and functions of different systems, but also are a good way to engage the organization’s members in being a part of this strategic decision.

Ownership of Electronic Health Records 281

Implementation planning should occur concurrently with the selection process, particularly the assessment of the scope of the work, initial sequencing of the EHR components to be implemented, and resources required. However, this step begins in earnest once a vendor and a product have been selected. In addition to further refining the implementation plan, this is the time to identify key metrics by which to measure the EHR’s success. An organization may realize numerous benefits from implement- ing an EHR. It should choose metrics that match its overall strategy and goals in the coming years and may include expected improvements in financial, quality, and clini- cal outcomes. Commonly used metrics focus on reductions in the number of duplicate laboratory tests through duplicate orders alerting, reductions in the number of adverse drug events through the use of bar-code medication administration, meaningful use objectives and measures, and the EHR advantages mentioned earlier in this chapter. To ensure that the desired benefits are realized, it is important to avoid choosing so many that they become meaningless or unobtainable, to carefully and practically define those that are chosen, to measure before and after the implementation, and to assign accountability to a member of the organization to ensure the work is completed.

End-user adoption of the EHR is also essential to realizing its benefits. Clinicians must be engaged to use the EHR successfully in their practice and daily workflows so that data may be captured to drive the decision support that underlies so many of the advantages and metrics described. To promote adoption, a change management plan must be developed in conjunction with the EHR implementation plan. The most effective change management plans offer end users several exposures to the system and relevant workflows in advance of its use and continue through the go-live and post-live time periods. Successful pre-live strategies include end-user involvement as subject-matter experts to validate the EHR workflow design and content build, hosting end-user usability testing sessions, shadowing end users in their current daily work in parallel with the new system, and formal training activities. The goal of these pre-live activities is not only to ensure that the EHR implementation will meet end user needs, but also to assess the impact of the new EHR on current workflow and process. The larger the impact, the more change management is required above and beyond system training. For example, simulation laboratory experiences may be offered to more thoroughly dress rehearse a significant workflow change, executive leadership may need to convey their support and expectations of clinicians about a new way of working, and generally more anticipatory guidance is required to communicate to those impacted by the changes.

Training may be delivered in a variety of media. Often a combination of approaches works best, including classroom time, electronic learning, independent exercises, and peer-to-peer, at-the-elbow support. Training must be workflow based and reflect real clinical processes. It must also be planned and budgeted for through the post-live period to ensure that competency with the system is assessed at the go-live point and that any necessary retraining or reinforcements are made in the 30 to 60 days post-live. This not only promotes reliability and safe use of the system as it was designed but also can have a positive impact on end users’ morale: Users will feel that they are being supported beyond the initial go-live period and have an opportunity to move from basic skills to advanced proficiency with the system.

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Finally, the implementation plan should account for the long-term optimization of the EHR. This step is commonly overlooked and often results in benefits falling short of expectations because the resources are not available to realize them per- manently. It also often means the difference between end users of EHRs merely sur- viving the change versus becoming savvy about how to adopt the EHR as another powerful clinical tool, much as clinicians have embraced such technologies as the stethoscope (HealthIT.gov, 2012). Optimization activities of the EHR should be con- sidered a routine part of the organization’s operations, should be resourced accord- ingly, and should emphasize the continued involvement of clinician users to identify ways that the EHR can enable the organization to achieve its overall mission. Many organizations start an implementation of EHRs with the goal of transforming their care delivery and operations. An endeavor that differs from simply automating a previously manual or fragmented process, transformation often includes steps to improve the process so as to realize better patient care outcomes or added efficiency. Although some transformation is experienced with the initial use of the system, most of this work is done postimplementation and relies on widespread clinician adoption of the EHR. As such, it makes optimization a critical component to successful ownership of an EHR.

Flexibility and expandability Health care is as unique as the patients themselves. It is delivered in a variety of settings, for a variety of reasons, over the course of a patient’s lifetime. In addition, patients rarely receive all their care from one healthcare organization; indeed, choice is a cornerstone of the American healthcare system. An EHR must be flexible and expandable to meet the needs of patients and caregivers in all these settings, despite the challenges.

At a very basic level, there is as yet no EHR system available that can provide all functions for all specialties to such a degree that all clinicians would success- fully adopt it. Consider oncology as an example. Most systems do not yet provide the advanced ordering features required for the complex treatment planning under- taken in this field. An oncologist could use a general system, but he or she would not find as many benefits without additional features for chemotherapy ordering, lifetime cumulative dose tracking, or the ability to adjust a treatment day sched- ule and recalculate a schedule for the remaining days of the plan. Some EHRs do a good job of supporting the work of nursing staff and physicians, but are not as supportive of the work of clinicians such as dieticians, physical and occupational therapists, and other healthcare personnel. These systems will continue to evolve and support interprofessional collaboration as more healthcare professionals are exposed to the power of these systems to support their work and become better able to articulate their specific needs.

Further, most healthcare organizations do not yet have the capacity to implement and maintain systems in all care areas. As one physician stated, “Implementing an EMR is a complex and difficult multidisciplinary effort that will stretch an organiza- tion’s skills and capacity for change” (Chin, 2004, p. 47).

Flexibility and Expandability 283

These two conditions are improving every day at both vendor and healthcare organizations alike. Improvements in both areas were recently fueled by ARRA incen- tives (see Box 14-4).

ARRA has also set the expectation that despite the large number of settings in which a patient may receive care, a minimum set of data from those records must flow or “interoperate” among each setting and the unique EHR systems used in those settings. Today, interoperability exists through what is called a Continuity of Care Document (CCD). This dataset includes patient demographics, medication, allergy, and problem lists, among other things, and the formatting and exchange of the CCD is required to be supported by EHR vendors and healthcare organizations seeking ARRA meaningful use incentives. The document formatted according to HL7 stan- dards is both machine readable and human readable.

Despite this positive step forward, financial and patient privacy hurdles remain to be overcome to achieve an expansive EHR. Most health care is delivered by small community practices and hospitals, many of which do not have the financial or technical resources to implement robust, interoperable EHRs. USDHHS recently loosened regulations so that physicians may now be able to receive healthcare infor- mation technology software, hardware, and implementation services from hospitals to alleviate the financial burden placed on individual providers and to foster more wide- spread adoption of the EHR.

Finally, patient privacy is a pivotal issue in determining how far and how easy it will be to share data across healthcare organizations. In addition to the Health Insurance Portability and Accountability Act privacy rules, many states have regulations in place related to patient confidentiality. An experience of the state of Minnesota foreshadows what all states may encounter. In 2007, Governor Tim Pawlenty announced the cre- ation of the Minnesota Health Information Exchange (State of Minnesota, Office of the Governor, 2007). Although the intentions of the exchange were to promote patient

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BoX 14-4 CLoUDY eHRs

A paradigm shift from healthcare facility–owned, machine-based computing to offsite, vendor-owned cloud computing, Web browser–based log-in accessible data, software, and hardware could link systems together and reduce costs. Hos- pitals with shrinking budgets and extreme IT needs are exploring the successes in this area achieved in other industries, such as Amazon’s S3. As providers strive to implement potent EHRs, they are looking for cloud-based models that offer the necessary functionality without having to assume the burden associated with all of the hardware, software, application, and storage issues. However, in the face of the HITECH Act and its associated penalties, how can we overcome the challenges to realize the benefits of this approach? Cloud computing has both advantages and disadvantages, and while they explore this new paradigm, healthcare providers must relinquish control as they continue to strive to maintain security. The vendors that are responsible for developing and maintaining this new environment are also facing challenges originating from both legislatures and healthcare providers. As the vendors and healthcare providers work together to improve the implemen- tation and adoption of the cloud-based EHR, the sky is the limit!

safety and increase healthcare efficiency across the state, it raised significant concerns about security and privacy. New questions arose about the definition of when and how patient consent is required to exchange data electronically, and older paper-based processes needed to be updated to support real-time electronic exchange (Minnesota Department of Health, 2007). For health exchanges such as these to reach their full po- tential, members of the public must be able to trust that their privacy will be protected, or else the healthcare industry risks that patients may not share a full medical history, or worse yet, may not seek care, effectively making the exchanges useless.

Accountable Care organizations and the eHR EHRs with data-sharing capabilities are central to the support of Accountable Care Organizations (ACOs), a payment incentive program established by the CMS (2015). As discussed elsewhere, this program of shared medical and financial responsibility is designed to provide quality, coordinated care while limiting costs. Some of the core information technology requirements for an ACO are EHRs, HIEs, care management systems, and analytics and reporting systems (Mastagni, Welter, & Holmes, 2015). A robust EHR can support many of these functions:

EHR solutions that are interoperable across organizations can significantly reduce the cost and complication of IT infrastructure by creating full EHR vis- ibility between providers. This shared visibility reduces or eliminates the need to participate in HIEs or invest in solutions to integrate data across different EHR platforms. Many EHRs also can serve as a program’s care management system, eliminating the need for a separate system to document care management ef- forts and help care teams engage with patients. (Mastagni et al., 2015, para. 5)

See Figure14-2.

the Future Despite the challenges, the future of EHRs is an exciting one for patients and cli- nicians alike. Benefits may be realized by implementing stand-alone EHRs as de- scribed here, but the most significant transformation will come as interoperability is realized between systems. As the former national information technology coordina- tor in the USDHHS David Brailer predicted about the potential of interoperability:

For the first time, clinicians everywhere can have a longitudinal medical record with full information about each patient. Consumers will have bet- ter information about their health status since personal health records and similar access strategies can be feasible in an interoperable world. Consumers can move more easily between and among clinicians without fear of their in- formation being lost. Payers can benefit from the economic efficiencies, fewer errors, and reduced duplication that arises from interoperability. Healthcare information exchange and interoperability (HIEI) also underlies meaningful public health reporting, bioterrorism surveillance, quality monitoring, and advances in clinical trials. In short, there is little that most people want from health care for which HIEI isn’t a prerequisite. (Brailer, 2005, p. W 5-20)

The Future 285

The future also holds tremendous potential for EHR features and functions that will include not only more sophisticated decision support and clinical reporting capacity, but also improved support for all healthcare professionals, improved bio- medical device integration, ease of use and intuitiveness, and access through more hardware platforms.

Implementation of robust and interoperable EHRs is becoming more common- place. More organizations adopting EHRs will facilitate broader dissemination of implementation best practices, with the hope of further shortening the time required to take advantage of advanced EHR features.

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Figure 14-2 How EHRs Support Accountable Care Data from ECG Consultants. (2015). The use of technology in healthcare reform: IT considerations for accountable care. Retrieved from http://www.ecgmc.com/thought-leadership/articles/the -use-of-technology-in-healthcare-reform-it-considerations-for-accountable-care

Retain patient data and information

building a more complete picture of the patient for all care providers

to access EHR

Improve ease of care coordination

Stores data electronically for exchange and


Provide decision support

Enhance evidence-based


Improves practice workflows

Reduce errors

Is leveraged for clinical analysis

Standardizes care through use of

shared protocols

EHRs put accountability into nursing


Measure clinical risks

Automate clinical processes

Provides decision support to encourage

evidence-based practices

References Adams, J., Ponte, P. & Somerville, J. (2016). Assuring the capture of standardized nursing data:

a call to action for chief nursing officers. International Journal of Nursing Knowledge, 27(3), 127–128. doi:10.1111/2047-3095.12136.

American Nurses Association (ANA). (2015). Inclusion of recognized terminologies within ehrs and other health information technology solutions. Retrieved from http://www.nursingworld .org/MainMenuCategories/Policy-Advocacy/Positions-and-Resolutions/ANAPositionStatements /Position-Statements-Alphabetically/Inclusion-of-Recognized-Terminologies-within-EHRs.html

American Organization of Nurse Executives. (2009). AONE guiding principles for defining the role of the nurse executive in technology acquisition and implementation. Washington, DC: Author. Retrieved from http://www.aone.org/resources/technology-acquisition -implementation.pdf

Bayliss, E. A., Ellis, J. L., Shoup, J. A., Chan, Z., McQuillan, D. B., & Steiner, J. F. (2015). Effect of continuity of care on hospital utilization for seniors with multiple medical conditions in an integrated health care system. Annals of Family Medicine, 13(2), 123–129. doi:10.1370 /afm.1739

In the future, we can expect to see more EHRs housed in the cloud, usable patient portals as we move toward more patient-centered health care, better mobile applica- tions for the EHR, the expansion of telemedicine applications for rural patients and those with chronic illnesses, and precision medicine advances supported by data ana- lytics (Reisenwitz, 2016).

summary It is an important time for health care and technology. EHRs have come to the fore- front and will remain central to shaping the future of health care. In an ideal world, all nurses, from entry-level personnel to executives, will have a basic competency in nursing informatics that will enable them to participate fully in shaping the future use of technology in the practice at a national level and wherever care is delivered. Such initiatives as Technology Informatics Guiding Education Reform (TIGER) and the important nursing terminology work are imperative for better integration and, ultimately, more visibility of nursing contributions to health care.

References 287


1. What are the implications for nursing education as the EHR becomes the standard for caring for patients?

2. What are the ethical considerations related to interoperability and a shared EHR? 3. You are asked about a diagnosis with which you are unfamiliar. Where would

you start looking for information? How would you determine the validity of the information?

4. Think about the documentation and knowledge management functions of your specialty. If you had the opportunity to create a wish list, what would you include in an EHR to support your work?

Brailer, D. J. (2005, January). Interoperability: The key to the future healthcare system. Health Affairs—Web Exclusive, W 5-19–W 5-21. Retrieved from http://content.healthaffairs.org /cgi/reprint/hlthaff.w5.19v1

Bronnert, J., Masarie, C., Naeymi-Rad, F., Rose, E., & Aldin, G. (2012, July). Problem-centered care delivery: How interface terminology makes standardized health information possible. Journal of AHIMA, 83(7), 30–35.

Bush, G. W. (2004). State of the Union address. Retrieved from http://content.healthaffairs.org /cgi/reprint/hlthaff.w5.19v1

Centers for Medicare and Medicaid Services (CMS). (2015). Accountable care organizations. Retrieved from https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/ACO /index.html

Certification Commission for Healthcare Information Technology. (2007). Certification Commission announces new work group members. Retrieved from Retrieved from http://www.healthimaging.com/topics/health-it/cchit-announces-new-work-group -members

Charles, D., Gabriel, M., & Searcy, T. (2015). Adoption of electronic health record systems among U.S. non-federal acute care hospitals: 2008–2014. HealthIT.gov. Retrieved from http://www.healthit.gov/sites/default/files/data-brief/2014HospitalAdoptionDataBrief .pdf

Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., Roth, E., . . . Shekelle, P. (2006). Systematic review: Impact of health information technology on quality, efficiency, and costs of

medical care. Annals of Internal Medicine, 144(10), E-12–E-22. Chin, H. L. (2004). The reality of EMR implementation: Lessons from the field. Permanente

Journal, 8(4), 43–48. Dorr, D., Bonner, L. M., Cohen, A. N., Shoai, R. S., Perrin, R., Chaney, E., & Young, A. (2007).

Informatics systems to promote improved care for chronic illness: A literature review. Journal of the American Medical Informatics Association, 14(2), 156–163.

Embi, P. J., Jain, A., Clark, J., Bizjack, S., Hornung, R., & Harris, C. M. (2005). Effect of a clinical trial alert system on physician participation in trial recruitment. Archives of Internal Medicine, 165, 2272–2277.

Goddard, B. L. (2000). Termination of a contract to implement an enterprise electronic medical record system. Journal of American Medical Informatics Association, 7, 564–568.

Halamka, J. D. (2006, May). Health information technology: Shall we wait for the evidence? [Letter to the editor]. Annals of Internal Medicine, 144(10), 775–776.

Healthcare Information and Management Systems Society (HIMSS). (2007). Electronic health record. Retrieved from http://www.himss.org/ASP/topics_ehr.asp

Healthcare Information and Management Systems Society (HIMSS). (2009). HIMSS Davies Organizational Award application: MultiCare. Retrieved from http://www.himss.org /multicare-davies-enterpriseorganizational-award

Healthcare Information and Management Systems Society (HIMSS). (2010a). Davies Award. Retrieved from http://www.himss.org/davies

Healthcare Information and Management Systems Society (HIMSS). (2010b). Recipient list. Retrieved from http://www.himss.org/himss-enterprise-davies-award-recipients

Healthcare Information and Management Systems Society (HIMSS). (2013). HIMSS 2013 iHIT Study: Executive summary. Retrieved from http://www.himss.org/library/clinical-informatics /2013-ihitstudy-executive-summary

Healthcare Information and Management Systems Society (HIMSS) Analytics. (2013). Electronic medical record adoption model (EMRAM). Retrieved from http://www.himssanalytics.org /emram/index.aspx

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Healthcare Information and Management Systems Society (HIMSS) Analytics. (2014). EMRAM stage criteria. Retrieved from http://www.himssanalytics.org/research/emram-stage-criteria

Healthcare Information and Management Systems Society (HIMSS) Analytics. (2015). Validated Stage 6 & 7 Providers List. Retrieved from http://www.himssanalytics.org/case-study/validated -stage-6-7-providers-list

Healthcare IT News. (2015). 7 tips for EMR success from Stage 7 hospitals. Retrieved from http://www.healthcareitnews.com/news/7-tips-emr-success-stage-7-hospitals

HealthIT.gov. (2012). Benefits of EHRs: Why adopt EHRs? Retrieved from http://www.healthit .gov/providers-professionals/why-adopt-ehrs

HealthIT.gov. (2013). How to attain meaningful use. Retrieved from http://www.healthit.gov /providers-professionals/how-attain-meaningful-use

HealthIT.gov. (2014). What are the advantages of electronic health records? Retrieved from http://www.healthit.gov/providers-professionals/faqs/what-are-advantages-electronic-health -records

HealthIT.gov. (2015a). About the ONC Health IT certification program. Retrieved from http://www.healthit.gov/policy-researchers-implementers/certification-bodies-testing -laboratories

HealthIT.gov. (2015b). 2015 edition test method. Retrieved from http://www.healthit.gov /policy-researchers-implementers/2015-edition-draft-test-procedures

Health Level Seven International (HL7). (n.d.) About HL7 International. Retrieved from http://www.hl7.org

Health Resources and Services Administration (HRSA). (n.d.) Common EHR functions. Retrieved from http://www.hrsa.gov/healthit/toolbox/healthitimplementation/implementationtopics /selectcertifiedehr/selectacertifiedehr_5.html

Holbrook, A., Keshavjee, K., Troyan, S., Pray, M., & Ford, P. T. (2003). Applying methodology to electronic medical record selection. International Journal of Medical Informatics, 71, 43–50.

Husk, G., & Waxman, D. A. (2004). Using data from hospital information systems to improve emergency care. Academic Emergency Medicine, 11(11), 1237–1244.

Institute of Medicine (IOM). (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academies Press.

Institute of Medicine (IOM). (2003). Key capabilities of an electronic health record system: Letter report. Washington, DC: National Academies Press.

Jain, A., Atreja, A., Harris, C. M., Lehmann, M., Burns, J., & Young, J. (2005). Responding to the rofecoxib withdrawal crisis: A new model for notifying patients at risk and their healthcare providers. Annals of Internal Medicine, 142(3), 182–186.

Jansen, W., & Grance, T. (2011). National Institute of Standards and Technology (NIST): Guidelines on security and privacy in public cloud computing. Retrieved from https://cloudsecurityalliance.org/wp-content/uploads/2011/07/NIST-Draft-SP-800-144 _cloud-computing.pdf

Kelly, T. (2016). Electronic health records for quality nursing & health care. Lancaster, PA: DEStech Publications, Inc.

Kim, T. Y., Hardiker, N., & Coenen, A. (2014). Inter-terminology mapping of nursing problems. Journal of Biomedical Informatics, 49, 213–220. doi:10.1016/j.jbi.2014.03.001

Konschak, C., & Shiple, D. (n.d.). System selection: Aligning vision and technology. Retrieved from http://divurgent.com/wp-content/uploads/2013/12/White-Paper.Vendor-Selection .vfinal.pdf

Kutney-Lee, A., & Kelly, D. (2011). The effect of hospital electronic health record adoption on nurse-assessed quality of care and patient safety. Journal of Nursing Administration, 41(11), 466–472. doi: 10.1097/NNA.0b013e3182346e4b

References 289

Mastagni, E., Welter, T., & Holmes, M. (2015). The use of technology in healthcare reform: IT considerations for accountable care. ECG Management Consultants. Retrieved from http://www.ecgmc.com/thought-leadership/articles/the-use-of-technology-in-healthcare -reform-it-considerations-for-accountable-care

Matney, S. A., Warren, J. J., Evans, J. L., Kim, T. Y., Coenen, A., & Auld, V. A. (2012). Development of the nursing problem list subset of SNOMED CT. Journal of Biomedical Informatics, 45(4), 683–688. doi:10.1016/j.jbi.2011.12.003

Minnesota Department of Health. (2007, June). Minnesota Health Records Act—HF 1078 fact sheet. Minneapolis, MN: Author. Retrieved from http://www.health.state.mn.us /e-health/summit/summit2007/beyerkropuenske07.pdf

National Institute of Standards and Technology (NIST). (2010). Meaningful use test measures: Approved test procedures. Retrieved from http://healthcare.nist.gov/use_testing/finalized _requirements.html

National Institutes of Health. (2006, April). Electronic health records overview. McLean, VA: Mitre Corporation.

Poprock, B. (2005, September). Using Epic’s alternative medications reminder to reduce prescription costs and encourage assistance programs for indigent patients. Presented at the Epic Systems Corporation user group meeting, Madison, WI.

Reed, H. L., & Bernard, E. (2005). Reductions in diabetic cardiovascular risk by community primary care providers. International Journal of Circumpolar Health, 64(1), 26–37.

Reisenwitz, C. (2016, January). Top 5 EHR trends for 2016. Capterra Medical Software Blog. Retrieved from http://blog.capterra.com/top-5-ehr-trends-for-2016-2/

Robert Wood Johnson Foundation (RWJF). (2006). Health information technology in the United States: The information base for progress. Retrieved from http://www.rwjf.org /programareas/resources/product.jsp?id=15895&pid=1142&gsa=1

Rosenbloom, S. T., Miller, R. A., Johnson, K. B., Elkin, P. L., & Brown, S. H. (2006). Interface terminologies: Facilitating direct entry of clinical data into electronic health record systems. Journal of the American Medical Informatics Association, 13(3), 277–288. http://doi.org/10.1197/jamia.M1957

Shekelle, P. G., Morton, S. C., & Keeler, E. B. (2006). Costs and benefits of health information technology. Evidence report/technology assessment, No. 132 [Prepared by the Southern California Evidence-based Practice Center under Contract No. 290-02-0003]. Agency for Healthcare Research and Quality Publication No. 06-E006. Rockville, MD: Agency for Healthcare Research and Quality.

Smith, T., Semerdjian, N., King, P., DeMartin, B., Levi, S., Reynolds, K.,... Dowd, J. (2004). Nicholas E. Davies Award of Excellence: Transforming healthcare with a patient-centric electronic health record system. Evanston, IL: Evanston Northwestern Healthcare. Retrieved from http://www.himss.org/content/files/davies2004_evanston.pdf

Spooner, S. A., & Council on Clinical Information Technology. (2007). Special requirements of electronic health record systems in pediatrics. Pediatrics, 119, 631–637.

State of Minnesota, Office of the Governor. (2007). New public–private partnership to improve patient care, safety and efficiency. Retrieved from http://www.governor.state.mn.us /mediacenter/pressreleases/2007/PROD008303.html

Technology Informatics Guiding Education Reform (TIGER). (2006). Evidence and informatics transforming nursing. Retrieved from http://www.amia.org/sites/amia.org/files/Malin-AMIA -Tiger-Team-Testimony.pdf

Thompson, D. I., Osheroff, J., Classen, D., & Sittig, D. F. (2007). A review of methods to estimate the benefits of electronic medical records in hospitals and the need for a national database. Journal of Healthcare Information Management, 21(1), 62–68.

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Turk, M. (2015). Electronic health records. Brooklyn Law Review, 80(2), 565–597. U.S. Department of Health and Human Services (USDHHS). (2010). Medicare and Medicaid

programs: Electronic health record incentive program. Retrieved from http://www.ofr.gov /OFRUpload/OFRData/2010-17202_PI.pdf

U.S. National Library of Medicine. (2016). Supporting Interoperability – Terminology, subsets and other resources from NLM. Retrieved from https://www.nlm.nih.gov/hit _interoperability.html

References 291

Key Terms » Adverse events » Agency for Health-

care Research and Quality (AHRQ)

» Alarm fatigue » Applications (apps) » Bar-code medication

administration (BCMA)

» Clinical decision support (CDS)

» Computerized physician order entry (CPOE)

» Electronic medication administration system (eMAR)

» Failure modes and effects analysis (FMEA)

» Government Accountability Office (GAO)

» High-hazard drugs

» Human factors engineering

» Just culture » Never events » Radio frequency

identifier (RFID)

» Root-cause analysis » Safety culture » Smart pump » Smart rooms » Systems

engineering » Wearable

technology » Workarounds

1. Explore the characteristics of a safety culture. 2. Examine strategies for developing a safety culture.

3. Recognize how human factors contribute to errors. 4. Appreciate the impact of informatics technology

on patient safety.


Introduction Nursing professionals have an ethical duty to ensure patient safety. Ac- cording to Lavin et al. (2015), “Direct care nurses, at their core, are risk managers. They attach meaning to what is and anticipate ‘what might be’” (para. 8). As the media and patients circulate stories about the lack of safety in healthcare institutions, it is no wonder that healthcare consum- ers are skeptical and providers are wary. A study out of Johns Hopkins University (Johns Hopkins Medicine, 2016) suggested that medical errors are the third-leading cause of death in the United States. Versel (2016) reminded us, however, that “it’s not the first time someone has called medical error the No. 3 cause of death in the U.S. John T. James, founder of a group called Patient Safety America, did that in a 2013 report in the Journal of Patient Safety.” (para. 2). Increasing demands on professionals in complex and fast-paced healthcare environments may lead them to cut corners or develop workarounds that deviate from accepted and expected practice protocols. These deviations are not carried out deliberately to put patients at risk, but rather are often practiced in the interest of saving time or because the organizational culture is such that risky behaviors are commonplace. Occasionally, these inappropriate actions or omissions of appropriate actions result in harm or significant risk of harm to patients. Consider the following case scenario:

A 19-year-old obese woman who had recently undergone C-section delivery of a baby presented in the emergency depart- ment (ED) with dyspnea. Believing the patient had developed a pulmonary embolism, the physician prescribed an IV hepa- rin bolus dose of 5,000 units followed by a heparin infusion at 1,000 units/hour. After administering the bolus dose, a nurse started the heparin infusion but misprogrammed the pump to run at 1,000 mL/hour, not 1,000 units/hour (20 mL/hour). By the time

Informatics Tools to Promote Patient Safety and Quality Outcomes Dee McGonigle and Kathleen Mastrian



the error was discovered, the patient had received more than 17,000 units (5,000 unit loading dose and about 12,000 units from the infusion) in less than an hour since arrival in the ED. A smart pump with dosing limits for heparin had been used. Thus, the programming error should have been rec- ognized before the infusion was started. However, the nurse had elected to bypass the dose-checking technology and had used the pump in its standard mode. It was quite fortunate that the patient did not experience adverse bleeding as her aPTT values were as prolonged as 240 seconds when initially measured and 148 seconds two hours later. (Institute for Safe Medication Practices, 2007, para. 2)

The smart pump used in this scenario was equipped with dose calculation soft- ware that compares the programmed infusion rate to a drug database to check for dosing within safe limits. This technology is particularly important when high-alert or high-hazard drugs are being administered. In this case, however, the available dose-checking technology had been turned off and the pump was operated in stan- dard mode. A subsequent analysis of the error event revealed that many nurses in the institution were bypassing the safety technology afforded by the smart pump to save time. Even though it has been more than a decade since this error occurred, we continue to see alerts and safety checks being worked around, ignored, or turned off. This chapter focuses on some of the recommended organizational strategies used to promote a culture of safety and some of the specific informatics technologies designed to reduce errors and promote patient safety.

What Is a Culture of Safety? The 2000 Institute of Medicine report To Err Is Human is widely credited for launch- ing the current focus on patient safety in health care. This report was followed in 2001 by the Institute of Medicine’s Crossing the Quality Chasm report, which brought to national attention healthcare quality and safety. This national attention resulted in a $50 million grant by Congress to the Agency for Healthcare Research and Quality (AHRQ) to launch initiatives focused on safety research for patients. Other initiatives prompted by these seminal reports were the Joint Commission’s National Patient Safety Goals (2002); the National Quality Forum’s adverse events and “never events” list (2002); the creation of the Office of National Coordinator for Health Information Technology (HIT) to computerize health care (2004); the formation of the World Health Organization’s Alliance for Patient Safety (2004); the Institute for Healthcare Improvement’s (IHI) 100,000 Lives campaign (2005) and 5 Million Lives campaign (2008); Congressional authorization of patient safety organizations cre- ated by the Patient Safety and Quality Improvement Act to promote blameless error reporting and shared learning (2005); the “no pay for errors” initiative launched by Medicare (2008); and the $19 billion Congressional appropriation to support elec- tronic health records (EHRs) and patient safety (Wachter, 2010). In 2013, the Patient Safety Movement Foundation launched the Open Data Pledge, and later announced three new patient safety challenges in 2016 (Patient Safety Movement, 2016). The most pressing challenges they identified—venous thromboembolism, mental health,

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and pediatric adverse drug events—reflect those where patient death could be pre- vented with the proper protocols in place during the provision of patient care (Patient Safety Movement).

The AHRQ (2012) safety culture primer laid the foundation for and suggested that organizations should strive to achieve high reliability by being committed to improving healthcare quality and preventing medical errors and to demonstrate an overall commitment to patient safety. That is, everyone and every level in an organiza- tion must embrace the safety culture. Key features of a safety culture identified by the AHRQ are as follows:

• Acknowledgment of the high-risk nature of an organization’s activities and the determination to achieve consistently safe operations

• A blame-free environment where individuals are able to report errors or near misses without fear of reprimand or punishment

• Encouragement of collaboration across ranks and disciplines to seek solutions to patient safety problems

• Organizational commitment of resources to address safety concerns (AHRQ, 2012, para. 1)

An important part of the safety culture is cultivating a blame-free environment. Er- rors and near misses must always be reported so that they can be thoroughly ana- lyzed. All organizations can learn from mistakes and change their organizational processes or culture to ensure patient safety. The Patient Safety and Quality Improve- ment Act of 2005 mandated the creation of a national database of medical errors and funded several organizations to analyze these data with the goal of developing shared learning to prevent medical errors. Organizations themselves can engage in root-cause analysis or failure modes and effects analysis (FMEA) to examine medical errors closely and to determine the system processes that need to be changed to prevent similar future errors (Harrison & Daly, 2009). A tool for implementing root-cause analysis developed by the U.S. Department of Veteran’s Affairs National Center for Patient Safety (2015) had three goals: to determine “what happened, why did it hap- pen and how to prevent it from happening again” (para. 4). Everyone is encouraged to submit actual medical errors and/or patient safety issues to the Patient Safety Net- work (PSNet, 2016a). Similarly, the IHI has a website dedicated to FMEA. “Failure Modes and Effects Analysis (FMEA) is a systematic, proactive method for evaluating a process to identify where and how it might fail, and to assess the relative impact of different failures in order to identify the parts of the process that are most in need of change” (IHI, 2016b, para. 1). If one embraces a blame-free environment to encour- age error reporting, then where does individual accountability fit in? According to the AHRQ, one way to balance these competing cultural values (blameless versus accountability) is to establish a “just culture” where system or process issues that lead to unsafe behaviors and errors are addressed by changing practices or work- flow processes, and a clear message is communicated that reckless behaviors are not tolerated. The “just culture” approach accounts for three types of behaviors leading to patient safety compromises: (1) human error (unintentional mistakes); (2) risky behaviors (workarounds); and (3) reckless behavior (total disregard for established policies and procedures).

What Is a Culture of Safety? 295

Strategies for Developing a Safety Culture Strategies for achieving a safety culture have been addressed frequently in the litera- ture. The focus here is limited to those strategies described by two key organizations, the AHRQ and the IHI. The AHRQ (2016), based on data from the Hospital Survey on Patient Safety Culture, suggested that teamwork training, executive walk-arounds, and unit-based safety teams have improved safety culture perceptions but have not led to a significant reduction in error rates. The AHRQ recommended seven steps of action planning: “1. Understand your survey results. 2. Communicate and discuss survey results. 3. Develop focused action plans. 4. Communicate action plans and de- liverables. 5. Implement action plans. 6. Track progress and evaluate impact. 7. Share what works” (p. 61). Informatics can assist with the analysis, trending, synthesis, and dissemination of the action plan results.

The IHI (2016a) stressed that organizational leaders must drive the culture change by making a visible commitment to safety and by enabling staff to share safety information openly. Some of the strategies suggested by the IHI include ap- pointing a safety champion for every unit, creating an adverse event response team, and reenacting or simulating adverse events to better understand the organizational or procedural processes that failed. Barnet (2016) reported that 49 companies had signed the open data pledge with Patient Safety Movement. Radick (2016) believed that senior leaders must be involved in order to sustain patient safety improvements. Leadership oversight and support is critical to ongoing sharing and, most impor- tantly, collaborative solution development to provide safe care and achieve quality outcomes for all patients.

A systems engineering approach to patient safety, in which technology manufac- turers partner with organizations to identify risks to patient safety and promote safe technology integration, has been advocated by Ebben, Gieras, and Gosbee (2008). They noted that human factors engineering is “[t]he discipline of applying what is known about human capabilities and limitations to the design of products, processes, systems, and work environments,” and its application to system design improves “ease of use, system performance and reliability, and user satisfaction, while reducing operational errors, operator stress, training requirements, user fa- tigue, and product liability” (p. 327). For example, Ebben et al. described the feel of an oxygen control knob that rotated smoothly between settings, suggesting to the user that oxygen flows at all points on the knob, when in fact oxygen flowed only at specifically designated liter flow settings. Human factors engineering testing would most likely reveal this design flaw, and the setting knob could be improved to include discrete audio or tactile feedback (click into place) to the user to in- dicate a point on the dial where oxygen flows. Ebben et al. also emphasized that testing human use factors provides more objective safety data than the subjective responses gained from user preference testing. “Understanding how the equip- ment shapes human performance is as important as evaluating reliability or other technical criteria” (p. 329). Organizations that are purchasing medical technology devices should avail themselves of shared safety data on equipment maintained by several key organizations, including the Joint Commission, the Food and Drug

296 CHAPTER 15 Informatics Tools to Promote Patient Safety and Quality Outcomes

Administration, and the Medical Product Safety Network. Many healthcare prac- titioners feel that we have not made great strides in either sharing our data or ac- cessing the available data to enhance patient safety interests. According to WISH Patient Safety Forum (2015), the patient safety premises that harms are inevitable, data silos are natural, and heroism is the norm “have inadvertently provided ex- cuses for not addressing patient safety comprehensively” (p. 9). This forum also stated that

[t]he belief that data silos are acceptable in healthcare settings is an irrespon- sible view regarding the role of data; it lacks an understanding of the current operational setting. Healthcare is a complex, multidisciplinary environment that requires collaboration and sharing of data across an integrated stake- holder community. (WISH Patient Safety Forum, p. 9)

As HIT evolves, refinements in HIT continue to improve patient safety. Banger and Graber (2015) stated that the

ONC is involved in a number of initiatives in support of this goal, includ- ing plans for a new national Health IT Safety Center to coordinate these efforts. Combined with the active engagement from the private sector, there is every reason to be optimistic that health IT will continue to improve the quality and safety of health care beyond the accomplishments realized to date. (p. 10)

According to the PSNet (2015), “busy health care workers rely on equipment to carry out life-saving interventions, with the underlying assumption that technol- ogy will improve outcomes” (para. 2). PSNet provided the following descriptions of equipment issues:

An obstetric nurse connects a bag of pain medication intended for an epi- dural catheter to the mother’s intravenous (IV) line, resulting in a fatal car- diac arrest. Newborns in a neonatal intensive care unit are given full-dose heparin instead of low-dose flushes, leading to three deaths from intracranial bleeding. An elderly man experiences cardiac arrest while hospitalized, but when the code blue team arrives, they are unable to administer a potentially life-saving shock because the defibrillator pads and the defibrillator itself cannot be physically connected. (para. 1)

See also Figure 15-1. Once the technology is integrated into the organization, biomedical engineers

can become valuable partners in promoting patient safety through appropriate use of these technologies. For example, in one organization, the biomedical engineers helped to revamp processes associated with the new technology alarm systems after they discovered several key issues: slow response times to legitimate alarms and mul- tiple false alarms (promoting alarm fatigue) created by alarm parameters that were too sensitive. Strategies for addressing these issues included improving the nurse

Strategies for Developing a Safety Culture 297

call system by adding Voice over Internet Protocol telephones that wirelessly receive alarms directly from technology equipment carried by all nurses, thus reducing response times to alarms; feeding alarm data into a reporting database for further analysis; and encouraging nurses to round with physicians to provide input into alarm parameters that were too sensitive and were generating multiple false alarms (Joint Commission, 2013; Williams, 2009). Research Brief 1 describes three investi- gations spanning from 2009 to 2016: a study of intelligent agent (IA) technology to improve the specificity of physiologic alarms, an integrative review of alarms, and default alarm setting changes coupled with in-service education. The Case Scenario, Well-Intentioned Providers, demonstrates how well-intentioned healthcare providers can cause harm. An audit conducted at one of their customer sites by Philips Health- care (2013) revealed that a

Telemetry Charge Nurse was found to be receiving and responding to an av- erage of 3.7 alarms per minute over the duration of the audit. Even allowing for minimal time to respond to each alarm, it is clear that this situation was problematic. A majority of that nurse’s time was spent responding to alarms, and inevitably some were missed. (para. 1)

The Joint Commission (2016) released the 2016 Hospital National Patient Safety Goals, and one category, Use Alarms Safely, stated that hospitals must “make im- provements to ensure that alarms on medical equipment are heard and responded to on time” (para. 4).

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Figure 15-1 User–Technology–Patient Safety Scheme



Patient Safety

Patient/Guardian/ Caregiver

Healthcare Provider

Ancillary Personnel



User/Healthcare Provider

IT Personnel

No data entry errors

Timely data delivery

Intact data – no missing data

Appropriate use of default values

Only use electronic systems for patient care

Capture newest information for reporting

Integrity of patient data (data associated with the correct patient)

Strategies for Developing a Safety Culture 299


The investigators in one study used simple reactive IA technology to develop and test decision algorithms for improving the sensitivity and specificity of physi- ologic alarms. The IA technology was tested in a 14-bed cardiothoracic unit over 28 days and was implemented in parallel to the usual physiologic patient moni- tor that provided measures such as systolic blood pressure, mean arterial pres- sure, central venous pressure, and cardiac index. Alarm data generated by both systems were compared and classified as to whether the alarm represented a true medical event requiring clinician intervention or a false-positive alarm. A total of 293,049 alarms were generated by the usual physiologic monitoring system, and 1,012 alarms were generated by the IA system after raw physiologic data were filtered using rule-based IA technology. The IA filtering system shows promise for improving the specificity of physiologic alarms and decreasing the number of false-positive alarms generated by artifacts, thus reducing the incidence of alert fatigue in clinicians.

The full article appears in Blum, J., Kruger, G., Sanders, K., Gutierrez, J., & Rosenberg, A. (2009). Specificity improvement for network distributed physiologic alarms based on a simple deterministic reactive intelligent agent in the critical care environment. Journal of Clinical Monitoring and Computing, 23(1), 21–30.

Another study conducted an integrative review of monitor alarm fatigue. The study’s evidence-based practice recommendations for technology included incorpo- rating short delays to increase response rates, creating a set of standardized alarms to enhance the staff’s ability to quickly determine what the alarm is for, and ani- mated troubleshooting on monitoring equipment. The author concluded that lack of response to alarms has caused harm and death and stated that, because a focus on patient outcomes is needed, outcomes research must be performed.

The full article appears in Cvach, M. (2012). Monitor alarm fatigue: An integrative review. Biomedi- cal Instrumentation & Technology, 46(4), 268–277. doi.org/10.2345/0899-8205-46.4.268

A pilot project was conducted to investigate if “(1) a change in de- fault alarm settings of the cardiac monitors and (2) in-service nursing education on cardiac monitor use in an ICU” would decrease alarm rates and improve the attitudes and practices of nurses in relation to clinical alarms (para. 2). This quality improvement project examined 39 nurses in a 20-bed transplant/cardiac ICU. Nurses received an in-service on monitor use, an audit log of alarms was collected, and the nurses’ attitudes and clinical practices were assessed using a pre- and postintervention survey. The authors concluded that “changing de- fault alarm settings and standard in-service education on cardiac monitor use are insufficient to improve alarm systems safety” (para. 5).

The full article appears in Sowan, A. K., Gomez, T. M, Tarriela, A. F., Reed, C. C., & Paper, B. M. (2016). Changes in default alarm settings and standard in-service are insufficient to improve alarm  fatigue in an intensive care unit: A pilot project. JMIR Human Factors, 3(1), e1.


Even well-intentioned healthcare providers can cause harm. Consider what should have been done differently in the case example below. Laura, a 25-year-old woman, arrived at the ER complaining of chest pain. She has two young children at home: a 6-year-old boy and a 4-year-old girl. She stated that she has been experiencing severe fatigue and fluttering in her chest for weeks but felt that she needed rest and it was probably nothing. Today, she had the fluttering with chest pain, and even her teeth and jaw hurt. This scared her, so she decided to go to the hospital. However, she had to wait 2 hours for her mother to arrive to watch the children. Her husband is on a business trip and will not be returning for 4 days. The initial ECG revealed normal sinus rhythm and all lab values were normal. The ER physician decided to keep her for observa- tion and sent her to the telemetry unit.

Laura was moved to telemetry and, as she stated, “wired for sound.” The nurse described the equipment and told her that in addition to all of the monitoring equipment, they would check her vital signs every hour as well. The nurse no sooner returned to the nurse’s station when Laura’s cardiac monitor alerted her that Laura was experiencing severe bradycardia (heart rate of less than 40 beats per minute). When the nurse arrived at Laura’s bedside, she found Laura sound asleep. She woke her gen- tly and told her that her monitor was alarming and that she was going to check her. Laura stated that she felt tired and was enjoying the peaceful sleep. Laura’s vital signs were fine and her heart rate was 72 beats per minute. The nurse reset the monitor, by which point Laura had already fallen back to sleep. The monitor alarmed the same way three more times within the next hour. Each time the nurse woke Laura and everything was fine. The nurse decided to contact the resident. While she was waiting for the resident, it alarmed twice again, but she just reset it and let Laura sleep. The resi- dent came and examined Laura. The resident felt everything was OK and that this young

mother needed her rest. The resident suggested that the nurse stop the hourly vitals, call and have the equipment examined by the biomedi- cal department, and in the meantime to turn the alarm off. The nurse agreed, turned off the alarm, placed a call to the biomedical techni- cian on duty, and left a message.

The nurse had another patient who also had frequent alarms, but his corresponded to actual medical events. As a result, the nurse was spending a great deal of time with this elderly gentleman and his wife. Each time she walked by Laura’s bed, the nurse noted that Laura was sleeping. She realized that it had been 2 hours since she turned off the alarm and called the biomedical technician, so she decided to check on Laura; however, her other patient’s alarm went off and, since Laura was sleeping, the nurse went to the other pateint’s bedside. At 4 hours after the alarm had been turned off, the biomedical technician arrived and apologized because there was a call-off in their department and they were running shorthanded. The nurse explained what had happened and the biomedical technician went to check Laura’s monitoring equipment. The biomedical technician called for the nurse as the patient was unresponsive. The nurse could not wake Laura, and the monitor was show- ing asystole. A code was initiated and Laura was pronounced dead 5 hours after she ar- rived on the telemetry unit.

This situation was assessed by the patient safety officer and the patient safety committee.

Because the monitor was integrated and all functions ran through the same controller, the nurse did not realize she was turning off all of the monitors (pulse oximetry, blood pres- sure, etc.). This was found to be an issue with the equipment itself because the alarm settings are too close together and not clearly labeled; however, the nurse should never have turned the alarms off. With the hourly checks cancelled and all of the monitoring equipment silenced, Laura was not being monitored at all. Well- intentioned providers were allowing this young mother to sleep, but with fatal consequences.

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It is evident from Research Brief 1 and the Case Scenario that we have yet to find a solution to the problem of alarm fatigue and related issues that negatively impact patient safety.

Clearly, there is more work to be done to create safety cultures in complex health- care organizations and to reduce the incidence of errors. Many organizations are looking to informatics technology to help manage these complex safety issues by us- ing smart technologies that provide knowledge access to users, provide automated safety checks, and improve communication processes. Harrison (2016) stated that “as nurse leaders in a clinical setting where smart tools are leveraged to increase the qual- ity and safety of patient care, we have certain responsibilities to ensure safe imple- mentation, training, and monitoring” (p. 21). To best utilize the available technology, nurse leaders and administrators must be able to use data. More and more graduate programs for nursing administrators are realizing the need for these emerging nursing leaders to be skilled in nursing informatics. These leaders must be able to use data, in- formation, and knowledge efficiently and effectively to assess and manage their clini- cal settings and ultimately apply these informatics skills to improve patient outcomes and the quality of patient care (Figure 15-2).

On a much higher level, the Government Accountability Office (GAO) selected and as- sessed six hospitals, from which it identified three challenges in implementing patient safety practices. The number one challenge was “obtaining data to identify adverse reactions in their own hospitals” (GAO, 2016, para. 2). Nursing informatics skills and knowledge can address this challenge.

The GAO interviewed patient safety experts and the related literature to identify three key gaps where better information could help guide hospital officials in their continued efforts to implement patient safety practices. These gaps involve a lack of “(1) information about the effect of contextual factors on implementation of patient safety practices, (2) sufficiently detailed information on the experience of hospitals that have previously used specific patient safety implementation strategies, and (3) valid and accurate measurement of how frequently certain adverse events occur” (p. 22). Once again, implementing solid nursing informatics practices, skills, and knowledge can close these gaps.

Informatics Technologies for Patient Safety Healthcare technologies are frequently designed to improve patient safety, streamline work processes, and improve the quality and outcomes of healthcare delivery. How- ever, technology is not always the answer to patient safety; as the Joint Commission (2008) cautioned, “the overall safety and effectiveness of technology in health care ultimately depends on its human users, and . . . any form of technology can have a negative impact on the quality and safety of care if it is designed or implemented improperly or is misinterpreted” (para. 2). As we continue to look to HIT to advance patient safety initiatives, we must realize that integrating HIT presents other chal- lenges and can add to the patient safety issues. For example, Singh and Sittig (2016) stated that HIT has the “potential to improve patient safety but its implementa- tion and use has led to unintended consequences and new safety concerns. A key

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302 CHAPTER 15 Informatics Tools to Promote Patient Safety and Quality Outcomes

Figure 15-2 Data and Quality Connection: There are many ways to obtain data and information. The skill is in knowing how to access, select, and use the data and information, applying nursing informatics to inform practice and improve patient care.

We need to know how to access data and information.

Next, we judiciously select and retrieve the data and information necessary to provide safe, high quality nursing care.

We must be able to search through the available data and information.

challenge to improving safety in health IT–enabled healthcare systems is to develop valid, feasible strategies to measure safety concerns at the intersection of health IT and patient safety” (p. 226).

Although technology may certainly help to prevent or reduce errors, one must always remember that technology is not a substitution for safety vigilance by the healthcare team in a safety culture. Harrison (2016) stated that “[p]atient safety should always be at the center of the design and adoption of any technology intro- duced into patient care settings. Technology that’s designed to improve patient safety is only as good as the person using the device. It doesn’t replace critical thinking, solid nursing practice, and careful patient monitoring” (p. 21).

The Wired for Health Care Quality Act of 2005 began a series of funding streams to promote HIT, promote sharing of best practices in HIT, and help organizations implement HIT (Harrison & Daly, 2009). Many early adopters opted to focus technology and safety initiatives on medication ordering and administra- tion processes. Medication errors are the most frequent and the most visible errors because the medication administration cycle has many poorly designed work processes with several opportunities for human error. Thus computerized physician order entry (CPOE), automated dispensing machines, smart pump technologies for IV drug administration, and bar-code medication administration (BCMA) frequently preceded the adoption of the EHR in many institutions because of the costs as- sociated with implementing these technologies. In an ideal world, the EHR would be adopted concurrently as part of an interoperable HIT system. In the early EHR systems, clinicians were prompted by electronic alerts reminding them of important interventions that should be part of the standard of care, but these alerts tended to be generalized and not patient specific—for example, “Did you check the al- lergy profile?” or “Has the patient received a pneumonia immunization?” These early alert and care reminders are now evolving into more sophisticated clinical decision support (CDS) systems to promote accurate medical diagnoses and suggest appropriate medical and nursing interventions based on patient data. Ganio and colleagues (2016) stated that

current EHR software is typically very customizable and may be adapted to multiple purposes. There are often several ways to accomplish a goal using vendor tools already available. Systems analysts should explore the options and weigh the pros and cons of each while consulting with end users to determine which tools meet their needs and are compatible with their workflows. (p. 629)

With the addition of triggers to detect adverse events, diagnostic errors, adverse drug events, hospital-acquired infections, and delays in diagnoses have been identified. “Trigger algorithms are frequently applied to EMRs for automated surveillance, and increasingly to prospectively identify patients at risk” (Rosen & Mull, 2016, p. 3).

The National Patient Safety Foundation (2016) listed the top patient safety issues as wrong-site surgery, hospital-acquired infections, falls, hospital readmissions, diagnostic errors, and medication errors. Many of these issues can be prevented or detected in their early stages using informatics technologies, although we still continue to struggle with these same safety issues. Other technologies designed to

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promote patient safety include wireless technologies for patient monitoring, clinician alerts, point-of-care applications, apps, and radiofrequency identification applications. Each of these is reviewed here, and the chapter concludes with a section discussing future technologies for patient safety.

Technologies to Support the Medication Administration Cycle The steps in the medication administration cycle (assessment of need, ordering, dispensing, distribution, administration, and evaluation) have been relatively stable for many years. Each of the steps depends on vigilant humans to ensure patient safety, resulting in the five rights of medication administration: (1) the right patient, (2) the right time and frequency of administration, (3) the right dose, (4) the right route, and (5) the right drug. Human error can be related to many aspects of this cycle. Distrac- tions, unclear thinking, lack of knowledge, short staffing, and fatigue are a few of the factors that cause humans to deviate from accepted safety practices and com- mit medication errors. Integration of technology into the medication administration cycle promises to reduce the potential for human errors in the cycle by performing electronic checks and providing alerts to draw attention to potential errors. Research Brief 2 describes high-risk and preventable drug-related complications.


A cluster-randomized, step-wedge trial was conducted involving 33 primary practices that were randomly assigned start dates during a 48-week interven- tion involving education, informatics, and financial incentives to conduct chart reviews; 33,334 patients in the preintervention period and 33,060 at-risk in the intervention period were included. The main outcome was patient-level exposure to high-risk prescribing of nonsteroidal anti-inflammatory drugs (NSAIDs) or se- lected antiplatelet agents (e.g., NSAID prescription in a patient with chronic kid- ney disease or co-prescription of an NSAID and an oral anticoagulant without gastroprotection). Secondary outcomes included the incidence of related hospital admissions. The analyses were conducted based on the intention-to-treat prin- ciple, with the use of mixed-effect models to account for clustering in the data.

Targeted high-risk prescribing was significantly reduced, from a rate of 3.7% (1,102 of 29,537 patients at risk) immediately before the intervention to 2.2% (674 of 30,187) at the end of the intervention (adjusted odds ratio, 0.63; 95% confidence interval [CI], 0.57 to 0.68; P<0.001). The rate of hospital admissions for gastrointestinal ulcer or bleeding was significantly reduced from the prein- tervention period to the intervention period (from 55.7 to 37.0 admissions per 10,000 person-years; rate ratio, 0.66; 95% CI, 0.51 to 0.86; P=0.002), as was the rate of admissions for heart failure (from 707.7 to 513.5 admissions per 10,000 person-years; rate ratio, 0.73; 95% CI, 0.56 to 0.95; P=0.02), but admissions for acute kidney injury were not (101.9 and 86.0 admissions per 10,000 person- years, respectively; rate ratio, 0.84; 95% CI, 0.68 to 1.09; P=0.19).

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CPOE is an electronic prescribing system designed to support physicians and nurse practitioners in writing complete and appropriate medication and care orders for patients. When CPOE is part of an EHR with a CDS system, the medication order is electronically checked against specific data in the patient record to prevent errors, such as ordering a drug that might interact with a drug the patient is already taking, ordering a dose that is too large for the patient’s weight, or ordering a drug that is contraindicated by the patient’s allergy profile or renal function. Because it is impos- sible for, and unreasonable to expect, a clinician to remember each of the more than 600 drugs that require a dose adjustment in the case of renal dysfunction, for exam- ple, safe dosing parameters are provided by the CPOE (Bates & Gawande, 2003). In a stand-alone CPOE system without a CDS system, the medication orders are simply checked by the computer against the drug database to ensure that the dose and route specified in the order are appropriate for the medication chosen. Specific benefits of CPOE include the following:

• Prompts that warn against the possibility of drug interaction, allergy, or overdose

• Accurate, current information that helps physicians keep up with new drugs as they are introduced into the market

• Drug-specific information that eliminates confusion among drug names that look and sound alike

• Reduced healthcare costs caused by improved efficiencies • Improved communication among doctors, nurses, specialists, pharmacists, other

clinicians, and patients • Improved clinical decision support at the point of care (Steele & DeBrow, n.d.)

CPOE solves the safety issues associated with poor handwriting and unclear or incomplete medication orders. Orders can be entered in seconds and from remote sites, eliminating the use of verbal orders that are especially subject to interpretation errors. Orders are then transmitted electronically to the pharmacy, reducing the po- tential for the transcription errors commonly encountered in the paper-based system, such as lost or misplaced orders, delayed dosing, or unreadable faxes. Thus CPOE changes workflows for all clinical staff and physicians as well as health team com- munication patterns (Doshi, 2015). As with any technology integration, introduction

The researchers concluded that their complex intervention combining education, informatics, and financial incentives reduced the rate of high-risk prescribing of antiplatelet medications and NSAIDs and may have improved clinical outcomes.

The full article appears in Dreischulte, T., Donnan, P., Grant, A., Hapca, A., McCowan, C., &  Guthrie, B. (2016). Safer prescribing—A trial of education, informatics, and financial incentives. New England Journal of Medicine, 374(11), 1053–1064. doi:10.1056/NEJMsa1508955

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of CPOE is associated with a resistance to change and a learning curve to gain profi- ciency, and users must learn to trust the system. Manor (2010) urges careful planning and training during implementation with plenty of staff support. Manor also reports on the need for a paper-based backup system in the case of network or electrical out- ages or system maintenance.

The verification and dispensing functions of the pharmacy can also be assisted by technology. The pharmacist begins by verifying the allergy status of the patient and the medication reconciliation information to ensure that the new medication is compatible with other medication in the care regimen. This verification func- tion is computer based, and the medication order is electronically checked via the knowledge database. If the order is verified as safe and appropriate, the pharma- cist proceeds to the dispensing process. Bar-code medication labeling at a unit dose level was mandated by the Food and Drug Administration in 2004, with targeted compliance to be achieved by 2006. A bar code is a series of alternating bars and spaces that represents a unique code that can be read by a special bar-code reader. Bar-code technology spans both the medication dispensing and administration steps in the medication administration cycle. In the pharmacy, the bar code helps to ensure that the right drug and the right dose are dispensed by the pharmacy. Medications that are labeled with bar codes can also be dispensed by robots capable of reading the codes or by automated dispensing machines. In this way, bar-code technology helps with the processes of procurement, inventory, storage, preparation, and dispensing (University of Rochester Medical Center, Department of Pharmacy, 2016).

The processes of drug storage, dispensing, controlling, and tracking are easily carried out via automated dispensing machines (also known as automated dispensing cabinets, unit-based cabinets, automated dispensing devices, and automated distribu- tion cabinets). These devices have benefits for both the user and the organization, specifically in the areas of access security (especially with narcotics administration tracking), safety, supply chain, and charge functions (Institute for Safe Medication Practices, 2016).

Applications (apps) or mobile apps are being used by and prescribed for patients. The apps used for patient education can engage and inform our patients; an educated patient is believed to be “more likely to understand risks and if there is an adverse event, may less likely file a lawsuit” (Diamond, 2016, para. 2). While there are ben- efits to their use, we must be judicious in our use of apps. If apps are prescribed for patients, then it is our responsibility to educate the patient and/or family on proper use. It is important that patients and their families understand the benefits and risks of using the app, as well as how to receive help when needed. If data are being ex- changed, our patients must comprehend what data will be collected and where, when, and with whom it will be shared.

There are apps for healthcare personnel as well. iScrub is an app used to monitor hand hygiene, which could help prevent healthcare-associated infections (University of Iowa, 2015). The Patient Safety Manual was designed as a resource to treat pa- tients quickly, safely, and effectively (Apkpure, 2016). Apps will continue to be used by providers and patients, so we must all assume the responsibility of making sure the apps are both appropriate to use and used appropriately.

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Radio frequency identifier (RFID) technology is rapidly gaining a foothold in healthcare technology and may soon be used in the medication administration cycle. Although more expensive than bar coding for packaging, the RFID tags are reprogrammable and issues associated with bar-code printing imperfections and bar-code scanner resolution can be mitigated (Vecchione, 2016). As discussed later in this chapter, RFID technologies may also be an important component of a medication compliance system for patients.

BCMA systems help to ensure adherence to the five rights of medication administra- tion. Whether BCMA is part of the larger EHR or a free-standing electronic medication ad- ministration system (eMAR), bar-code technology provides a system of checks and balances to ensure medication safety. The nurse begins by scanning his or her name badge, thereby logging in as the person responsible for medication administration. Next, the bar code on the patient’s identification bracelet is scanned, prompting the electronic system to pull up the medication orders. Next, the bar code on each of the medications to be administered is scanned. This technology check ensures that the five rights of medication administra- tion are met. If there is a discrepancy between the order and the medication that was scanned or a contraindication for administration, an alert is generated by the system. For example, in an EHR system with CDS, the nurse may be prompted to check the most recent laboratory results for electrolytes before administering a potassium supplement. In a free-standing eMAR without CDS or EHR links, if the medication orders have recently been changed, the nurse is alerted to the change. When an alert is generated, the nurse must chart the action taken in response to that alert. For example, an early dose might need to be given if the patient is leaving the unit for a diagnostic test.

Despite the promising advances in patient safety afforded by this technology, it is not fail safe (Cochran, Jones, Brockman, Skinner, & Hicks, 2007). Medications that are labeled individually by the in-house pharmacist increase the potential for human error if the medication is given an incorrect bar code, such as one signifying a wrong dose or even the wrong medication. In addition, the bar-code printers themselves may generate unreadable labels, leading to staff workarounds in the interest of saving time. Cochran et al. make the following recommendations to reduce BCMA errors:

• Purchase unit-of-use medications with manufacturer bar codes whenever possible.

• Double-check all hospital-generated bar-code labels, including those for com- pounded injectable medications, before the product leaves the pharmacy.

• Carefully review all BCMA override reports. Address system workarounds through process change and staff education.

• Minimize false-positive warnings to reduce the likelihood that staff will ignore warnings for real errors.

• Ensure that an urgent need exists for all “stat” orders, as pharmacy review and advantages of bar-code administration are usually circumvented in such cases.

• Establish institutional policies and procedures that can be easily implemented when products fail to scan. Processes in pharmacy will likely be different than processes at the point of care (p. 300).

Smart pump technologies are designed for safe administration of high-hazard drugs and to reduce adverse drug events during IV medication administration. Smart pumps have software that is programmed to reflect the facility’s infusion parameters

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and a drug library that compares normal dosing rates with those programmed into the pump. Discrepancies generate an alarm alerting the clinician to a safety issue. A soft alarm can typically be overridden by a clinician at the bedside, but a hard alarm requires the clinician to reprogram the pump so that the dosing falls within the fa- cility’s IV administration guidelines for the drug to be infused. All alarms generated by the smart pump are tracked along with the clinician’s responses to them (Cum- mings & McGowan, 2015; Dulak, 2005; University of Alabama at Birmingham, 2013). Smart pumps can be seamlessly integrated into BCMA systems, and data can be fed directly into the EHR. The IHI (2012) recommends the following steps to en- sure safe implementation of smart pump technology:

• Prior to deploying these pumps, standardize concentrations within the hospital. Asking the nurse to choose among several concentrations increases the risk of selection error.

• Prior to deploying these pumps, standardize dosing units for a given drug (for example, agree to always dose nitroglycerin in terms of mcg/min or mcg/kg/min, but not both). Asking the nurse to choose among several dosing units increases the risk of selection error.

• Prior to deploying these pumps, standardize drug nomenclature (for example, agree to always use the term KCl, but not potassium chloride, K, pot chloride, or others). Asking the nurse to remember and choose among several possible drug names increases the risk of selection error.

• Perform a failure modes and effects analysis on the deployment of these devices. • Ensure that the concentrations, dose units, and nomenclature used in the pump

are consistent with that used on the medication administration record (MAR), the pharmacy computer system, and the EHR.

• Meet with all relevant clinicians to come to agreement on the proper upper and lower hard and soft dose limits.

• Monitor overrides of alerts to assess whether the alerts have been properly con- figured or whether additional quality intervention is required.

• Be sure the “smart” feature is utilized in all parts of the hospital. If the pump is set up volumetrically in the operating room but the “smart” feature is used in the ICU, an error may occur if the pump is not properly reprogrammed.

• Be sure there are upper and lower dose limits for bolus doses, when applicable. • Engage the services of a human factors engineer to identify new opportunities

for failure when the pumps are deployed. • Identify a procedure for the staff to follow in the event a drug that is not in the

library must be given or when its concentration is not standard. • Deploy the pump in all areas of the hospital. If a different pump is used on one

floor and the patient is later transferred, this will create new opportunities for failure. Also, there may be incorrect assumptions about the technology available to a given floor or patient.

• Consider using “smart” technology for syringe pumps as well as large-volume infusion devices (para. 7).

Cummings and McGowan (2015) cautioned that we must never solely rely on the pump to identify and alert us to problems. Nurses must always engage in best

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practices and follow all patient safety practices. There is no substitute for nursing as- sessment of patients as a key safety tool.

A CDS can enhance the medication administration cycle by promoting safety and improving patient outcomes. The CDS is guided by targeted information delivery, ensuring that the five rights of CDSs are implemented: the right information provided to the right person in the right format through the right channel at the right time in workflow. For example, during medication selection, a CDS helps a clinician select an appropriate medication based on client data, such as clinical condition, weight, renal function, concurrent medications, and cost. This system ensures that the order is complete by performing checks for drug interactions, duplications, or allergy contra- indications and ensures the right dose and right route are specified. During the verifi- cation and dispensing phase of the medication administration cycle, the CDS provides double checks for interactions, allergies, and appropriate dose orders. Consideration is also given to potential infusion pump programming issues, incompatibilities dur- ing infusion, and proper notation and dispensing when portions of a dose must be wasted. During the administration phase, the CDS assists with patient identification and current assessment parameters (i.e., blood pressure, glucose level) that may con- traindicate the use of the medication at that point in time. In addition, checks for interactions with foods or other medications and timing and monitoring guidelines are provided to the clinician administering the medication. The CDS has patient education guidelines and printable handouts to assist clinicians in educating patients about their medications. The monitoring functions of the CDS provide a structured data reporting system to track side effects and adverse events across the population (Healthcare Information and Management Systems Society [HIMSS], 2009a).

Several promising technologies may become available in the future to assist pa- tients with medication compliance after discharge. For example, eMedonline collects patient medication compliance data by scanning package bar codes or RFID medi- cation tags and using personal digital assistant or smartphone technology to send compliance data to the server. Clinicians review the medication compliance data and provide education and feedback to patients to increase their compliance with proper medication administration (eMedonline, n.d.). The SIMpill Medication Adherence System uses Web-based technology to monitor patient compliance and provide re- minders about taking medications or refilling prescriptions by sending text messages to the patient or caregivers (SIMpill, 2008). Caps of pill bottles may contain RFID tags that monitor and collect data on when the bottle is opened, or that contain flashing time reminders when a dose is due (Blankenhorn, 2010). Smart inhalers track asthma medication compliance using a microprocessor that records and stores medication compliance. They may also include visual and audio reminders to use the inhaler (Adherium, 2010).

In addition, several potential technologies are still being tested. For example, InPen is a smart insulin pen that couples with a smartphone to calculate insulin dosages and track injections (Medgadget, 2016a), PillDrill helps people adhere to a medication sched- ule and also includes a “mood cube” to track how patients are feeling (Medgadget, 2016b), and Proteus smart pills have sensors attached to medications to track when the pill is actually ingested (Medgadget, 2015). These are just a sampling of the newer technologies for medication adherence; more are expected to emerge in the future.

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Additional Technologies for Patient Safety CDS systems have safety uses beyond the medication administration cycle. The robust data collection and data management functions help to ensure quality approaches to patient health challenges based on research evidence and clinical guidelines. A CDS may also ensure cost-effectiveness by alerting clinicians to duplicate testing orders, or by suggesting the most cost-effective diagnostic test based on specific patient data (HIMSS, 2009b). Consider this description of the features of a CDS based on screen captures performed by a CDS system:

The patient is a 75-year-old male with coronary artery disease (CAD), diabetes mellitus (DM), and elevated creatine kinase (CK). Assessment