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Assignment : answer Chapter 12 questions; at least one

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Chapter 12 HEALTHCARE  INFORMATION         

Chapter 12 questions

1.Justify the need for data mining in data analytics.  

2.   Examine what it means to aggregate data. Identify some of the sources of data for aggregation. Determine how the interpretation and evaluation of aggregated data support the strategic uses of health information.

Aggregate Data

Aggregate data is when individual, comparative, or other multiple sources of data are compiled and analyzed to draw conclusions about a specific topic or area. For example, in a focus group study, data, observation, and interview data were compiled into an aggregate format so that none of the individuals in the multiple healthcare organizations that participated could be identified in any way. Varying methods and skills of leadership among HIM leaders and facilities were compared and contrasted in order to generate conclusions. However, since the focus group sample was small, not all the conclusions could be generalized (Sheridan et al. 2016). In fact, any data compiled from samples of data have limitations since the sample of data may not accurately reflect the characteristics across that entire population. One way to reduce this is to compare the sample’s demographic characteristics to the population’s demographics (if this information is available); if the characteristics prove similar, it increases the reliability of the sample data.

HITT 1301 CHAPTER 12

Health Information Management Technology,

An Applied Approach

Nanette Sayles, Leslie Gordon

Copyright ©2020 by the American Health Information Management Association. All rights reserved.

Except as permitted under the Copyright Act of 1976, no part of this publication may be reproduced,

stored in a retrieval system, or transmitted, in any form or by any means, electronic, photocopying,

recording, or otherwise, without the prior written permission of AHIMA, 233 North Michigan Avenue,

21st Floor, Chicago, Illinois 60601-5809 (http://www.ahima.org/reprint).

ISBN: 978-1-58426-720-1

AHIMA Product No.: AB103118

Healthcare information is used to monitor the quality of patient care, conduct medical research, and accurately reimburse healthcare organizations. Healthcare information is based on personal health data about individuals primarily for ­provider use in the management of patient care. Data collection techniques include traditional methods such as paper health records as well as eHealth tools such as templates. “A template is an EHR documentation tool utilized for the ­collection, presentation, and organization of clinical data elements” (Buttner et al. 2015). The sources of health information include the healthcare provider through documentation in the health record and the individual through the use of a personal health record. A personal health record (PHR) is a record created and managed by an individual in a private, secure, and confidential environment. The personal health record will be covered later in this chapter. In addition, the federal incentives for the adoption of the electronic heath record (EHR) have progressed healthcare information exchange, including returning a patient care summary to the patient. Databases of healthcare information collected or maintained by healthcare providers, institutions, payers, and government agencies are of great importance to those who use them; for example, researchers or public health agencies. These databases are used for administrative purposes, including determination of payment for services provided, measurement of quality performance indicators, and research.

Per the Federal Health IT Strategic Plan for 2015-2020, the benefits of electronic health information include lower healthcare cost, increased healthcare quality, improved population health, and an improvement in consumer engagement. The Federal Health IT Strategic Plan is illustrated in figure 12.1.

Figure 12.1 Strategies to achieve health IT goals

Source: ONC 2014a

With the implementation of the EHR and the changes that result, the roles and career options for health information management (HIM) professionals is growing. Some of the new roles include data analytics, consumer engagement, and health information exchange (HIE). This chapter discusses HIE information from the perspective of data analytics and explores the strategic uses of health information. In addition, the consumer’s link to healthcare information—specifically their needs for information, ease of access, navigational tools, telehealth, and PHRs—is described. The various aspects of sharing and exchanging healthcare information are also addressed.

Role of Data Analytics in Healthcare Information

Data are needed to arrive at information. Health data are not health information until they are interpreted, evaluated, and appropriately displayed (RWJF 2015). The difference between data and information is described in chapter 3, Health Information Functions, Purpose, and Users. Data analytics is the science of examining raw data with the purpose of drawing conclusions about that information. For example, data analytics can help hospitals with staffing by predicting the number of patients treated at a healthcare organization each month. The raw data examined in this example are admissions data, such as admissions records, rates, and patterns, which are analyzed over a period of time. Data analytics of admissions data can lead to the development of a web-based interface that enables physicians, nurses, and hospital administrators to forecast visits and admission rates for the future (Sreenivasan 2018).

The role of data analytics depends on the type of data being captured, reviewed, and used for the purpose of turning them into healthcare information. Multiple types of data exist, two of which—administrative and clinical—are further explained in the next section. If the data are of a clinical nature, then the analytics revolve around the contents of the health record. Clinical data could include elements such as lab values, number of patients with pneumonia, and so on. Administrative data are focused on other components such as financial data. A type of data analytics that uses clinical data is a clinical decision support (CDS) system. A CDS is a type of data analysis since it takes information from more than one source and provides an avenue for clinicians to make observations and decisions. “Clinical decision support provides clinicians, staff, patients or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and healthcare” (ONC 2013).

Clinical data about an individual can also be combined with clinical data from other individuals to form population-based healthcare data. The resulting information may be used to improve the health of the public. For example, the occurrence of measles in one town could be combined with measles occurrence in a state or a region and that information could then be communicated on a ­national level if the rate of measles in children has increased from previous years. Analytics has the potential to play a role in leveraging data to improve healthcare quality and patient outcomes. For example, the data compare the health of a group from one region or state to another. The following is an introduction to analytics, its tools, and the knowledge areas for HIM professionals in data analytics.

Introduction to Analytics

There are different types of analytics. Descriptive analytics answers the question “what happened,” diagnostic analytics answers the question “why did it happen,” predictive analytics answers “what will happen,” and prescriptive analytics answers “how can we make it happen” (Laney et al. 2012). To further illustrate for clinical data analytics, descriptive analytics could be centered on the increase in the incidence of Legionnaires’ disease in individuals 65 years and older in a specific state over a five-year period of time. Diagnostic analytics would review the why of increased rates of Legionnaires’ disease. For predictive analytics, once the why is found, it could be extrapolated that an increase will be seen in other states if certain conditions are found. Using this same situation, prescriptive analytics would examine ways to reduce the potential rate of increase of Legionnaires’ disease in individuals over age 65 even if certain conditions (as found in the diagnostic phase) occur.

Analytics involves acquiring, managing, studying, interpreting, and transforming data into useful information. Types of data include clinical, financial, and operational data and the types of analytics include healthcare data analytics and clinical data analytics. Healthcare data analytics is the practice of using data to make business decisions in healthcare, whereas clinical data analytics is the process by which health information is captured, reviewed, and used to measure quality of care provided. What data are involved, the consumer of the information, and the decision the analysis supports influences the analytic process and choice of tools. However, there are certain steps that occur to prepare healthcare data for data analysis. The first step is data ­capture, which helps ensure the data needed are available and that the data are correct. Data collection is discussed later in this chapter. The second is data provisioning, which ensures that the data are in a format that can be manipulated for data analysis. For example, in the data field gender, male might be “1” and female “2.” Data analysis, where data are interpreted, is the final stage of transforming raw data into meaningful analytics.

Analytics Tools

The amount and types of data available for analysis have increased as more data are available electronically. In addition, as technology advances, the various tools available to perform analytics allow for new ways to study and present the data. A few of the more common tools are those used for visualization, to report on process measures, to capture the data, and for extracting and examining data from a database.

Data Visualization

Data visualization is the presentation of data using a graph, diagram, or chart. The graphic display of data can help the viewer understand the data trend. For example, it can identify areas that need action, such as addressing a decline in the number of patients or an increase in the infection rate. Types of data visualization tools include tables, charts, and graphs. Choosing one visualization method over another can mean the difference between correct or incorrect data representation and drawing an accurate or erroneous conclusion. For example, tables display exact values whereas graphs show trends.

Following established guidelines for data visualization results in the delivery of a clear message. Those overall guidelines for creating any visual presentation, including the following:

Understand the data

Evaluate the information to communicate and the way it should be visualized

Define your audience and examine how they process visual information

Display the intended information to the appropriate audience in the clearest, simplest form (SAS 2018)

Tables are used to organize quantitative data or data expressed as numbers. Charts (such as pie charts and bar charts) and graphs (such as line graphs) are appropriate when presenting relationships. For example, in figure 12.2 the first pie chart shows percentages that add up to more than 100 percent, while percentages in the second chart are a part of the whole and add up to 100 percent. Each tool has specific features to keep in mind when depicting the data. For more information on presenting statistical data using tables, charts, and graphs, see chapter 13, Research and Data Analysis.

Figure 12.2 Poor and improved data display

Source: ©AHIMA.

Figure 12.2 provides an example of a poor and an improved pie chart display.

Dashboard

The dashboard is a data analytics tool that is a computerized visual display of specific data points. Typically, a dashboard focuses on a process and the rate of achievement. A dashboard is different from a scorecard. A scorecard, which can also be a computerized visual display, focuses on outcome or goal achieved, such as money raised for an event or cause. Both a dashboard and a scorecard can involve key indicators. A key indicator is a quantifiable measure used over time to determine whether some structure, process, or outcome in the provision of care to a patient supports high-quality performance measured against best practice criteria. For example, a key indicator could monitor death rates or infections. Chapter 18, Performance Improvement, discusses scorecards in more detail.

Health information management professionals use dashboards to monitor a number of indicators to improve performance and meet quality goals such as reducing the infection rate. To track the process measure over time, metrics (way to measure something) or benchmarks are established. Information is displayed on a dashboard to show the status of predetermined benchmarks. Often dashboards use color such as red, yellow, and green in a stoplight scheme. Similar to a traffic light, red means stop and go back, yellow means caution, and green means all good. Dashboards provide early warning signals and alert the manager to areas in need of attention.

For example, a recent HIM trend is instituting a clinical documentation integrity (CDI) program. Since this is not a small undertaking, dashboards can assist in measuring whether the program is successful. A monthly dashboard might show the number of clarifications requested by a CDI specialist that impacted a diagnosis-related group based on a benchmark. The dashboard would show green if the metric is met, yellow if it is in progress or halfway met, and red if the metric is below standard.

Dashboards are also used to manage revenue cycle management performance. For example, the Healthcare Financial Management Association (HFMA) has a web-based application called MAP App for use by healthcare providers to check revenue cycle performance and evaluate against provider peer groups (HFMA 2019). The HFMA’s key performance indicators can be used to track, monitor, and improve revenue cycle performance.

Data Capture Tools

Data capture is the process of recording data in a health record system or database. A database is an organized collection of data, text, references, or pictures in a standardized format, typically stored in an information system for multiple applications. A database contains a large amount of data, often from multiple sources. Additionally, a database can provide comparisons using tools from within the database software. One of the most common healthcare databases is the relational database, which stores data in predefined tables consisting of rows and columns. Healthcare providers as well as patients may be the source of the data. There are several tools available for acquiring health-related data. Historically, data capture into a health record was via written notes or traditional voice dictation that was transcribed and typed into a paper report. Another method for data capture is scanning documents into electronic document management systems that create a picture of the scanned document, making it accessible electronically. Devices also include traditional keyboard or touch screen handheld computers or patient-generated health data devices (discussed later in this chapter). When the software application is run on a mobile platform such as a tablet or cellular phone, system and application software (often referred to as apps) is needed for the device to function and perform the desired tasks.

Electronic healthcare data capture is a fundamental function of the EHR (HealthIT 2018). The EHR is an information system with several components and data capture is an element in each component. The components include source systems (such as the laboratory information system), core clinical EHR systems (such as point-of-care charting), supporting infrastructure such as ­human–computer interfaces, and connectivity systems such as personal health records (Amatayakul 2013, 16–19). In point-of-care charting, the ­information is entered into the health record at the time and location of service. Nurses entering data using a tablet as they conduct patient assessments at the bedside is an example of point-of-care charting.

A human–computer interface is the device used by humans to access and enter data into an information system. A number of mobile devices are used for data entry into point-of-care charting systems. These handheld devices include tablet computers, laptop computers, and smartphones. These devices often contain built-in methods to facilitate the capture of structured data such as predefined or custom-built templates or forms with drop-down menus and point and click fields and word macros. These devices exist to make data collection easier.

The outcome of point-of-care charting can be unstructured or structured data. Unstructured data are nonbinary, human-readable data, whereas structured data are binary, machine-readable data in discrete fields. An example of unstructured data is free text that describes the patient’s description of his or her condition. An example of structured data is using checkboxes to indicate patient symptoms. Structured data has many advantages over unstructured data when it comes to data analytics and health information exchange. Structured and unstructured data are covered in more detail in chapter 6, Data Management.

The structured data’s entry fields and the potential entries in those fields are controlled, defined, and limited, resulting in discrete data. Discrete data represent separate and distinct values or observations; that is, data that contain only finite numbers and have only specified values. Stored in databases and data warehouses, these standardized data are available in a usable and accessible form. However, physicians and other healthcare providers may express frustration when limited to recording only certain data in specific fields. While a set format ensures consistency and provides standard meaning, it may limit details considered important by clinicians.

When considering methods for EHR data capture, follow these best practices:

Collect data at the point of care directly from the patient

· Facilitate data accuracy using guidelines for documentation per governmental and other stakeholder standards

· Create and evaluate data integrity policies

· Establish information governance guidelines (AHIMA 2019)

Additionally, key areas such as patient identification, the use of documentation templates, copy and paste functionality, making amendments and corrections, and the incorporation of data captured in other areas of a healthcare organization not networked to the EHR such as outpatient services should be part of the role of HIM (AHIMA 2019).

Data capture may also occur with word processing software. The word processing copy and paste functionality in an EHR system must be carefully monitored and limited or prohibited to prevent data quality issues. Examples of data quality issues include copying outdated information or copying content from one patient to another that does not apply. Measures for preventing data quality problems include the following:

· Clearly label the information as copied from another source

· Limit the ability for data to be copied and pasted from other information systems

· Limit the ability of one author to copy from another author’s documentation

· Allow a provider to mark specific results as reviewed

· Allow only key, predefined elements of reports and results to be copied or imported

· Monitor a clinician’s use of copy and paste (AHIMA Work Group 2015)

For additional information on the copy and paste function and risks associated with it, refer to chapter 3, Health Information Functions, Purpose, and Users.

Two other technologies—speech recognition (speech-to-text) and natural language processing (NLP)—provide yet another way to acquire health data. NLP is a technology that converts human language (structured or unstructured) into data that can be translated and then manipulated by computer systems. Integration of these technologies within the EHR can result in the provision of clinical information needed by providers to inform decision-making.

Back-end speech recognition (BESR) is a specific use of speech recognition technology (SRT) in an environment where the recognition process occurs after the completion of dictation by sending voice files through a server. In BESR, an employee edits or corrects the dictation. Front-end speech recognition (FESR) is a process where the provider speaks into a microphone or headset attached to a PC and upon speaking, the words are displayed as they are recognized. The physician corrects misrecognitions at the time of dictation. Use of FESR integrated with an EHR provides the best outcome, as the provider is able to respond to prompts from the EHR resulting in more complete, accurate, and timely documentation (AHIMA 2013). Templates and macros are also tools used with SRT to capture data. Macros are used by transcriptionists to insert content into a transcribed document with just a few keystrokes. For example, the transcriptionist might create shortcuts to insert commonly used phrases or other content. As the output of SRT is digital text, combining it with NLP results in the conversion of the text or any free text narrative into data that can be translated and then manipulated by computer systems. Once transformed, it becomes searchable along with other structured data.

Data Mining

Data mining is the process of extracting and analyzing large volumes of data from a database for the purpose of identifying hidden and sometimes subtle relationships or patterns and using those relationships to predict behaviors. It is a key piece of analytics and of the knowledge discovery process. There are several knowledge discovery process models such as the Knowledge Discovery in Databases (KDD), Sample, Explore, Modify, Model, Assess (SEMMA), and Cross-Industry Standard Process for Data Mining (CRISP-DM) as well as hybrid models. Each has defined steps, with data mining being one of them.

The available data for analytics strategy and mining can come from EHRs and various databases such as a clinical data repository and clinical data warehouse. A clinical data repository is a central database that focuses on clinical information. The clinical data warehouse allows access to data from multiple databases and combines the results into a single query and reporting interface. Specific applications of data mining methods are customized for certain uses of the extracted data. For example, data mining may be used to extract clinical data directly from the EHR for the purpose of compiling content for reporting clinical quality measures. The clinical data warehouse lends itself to data mining as it encompasses multiple sources of data. The varying sources of data that feed a clinical data warehouse may include data sets, clinical data repositories, a case-mix system, laboratory information systems, or a health plans database. The data in the clinical data warehouse depends on how they will be used. For example, if the clinical data warehouse is going to be used to determine what treatment is most effective, then data would need to include data that would support that research. In this case, the clinical data warehouse might include blood pressure, test results, symptoms, treatments, and more. In the clinical data warehouse, the data from these sources can be “mined” to identify and implement better evidence-based solutions.

Systematically analyzing the data uncovers hidden patterns or trends for use in predicting behaviors. The information discovered from data mining databases aids clinical research. For example, data mining could be used to detect early signals of potential adverse drug events. Other data mining applications are used for the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and detection of fraud and abuse (Koh and Tan 2005).

HIM Professionals and Analytics

Analytics start with data and HIM professionals, with their understanding of healthcare data, help ensure correct and accurate data are captured. HIM professionals are also proficient in business operations and clinical processes. However, data analytics require going beyond these into competencies such as business intelligence (see chapter 6, Data Management), database administration, inferential and descriptive statistics (see chapter 13, ­Research and Data Analysis), health information technology (see chapter 11, Health Information Systems), and project management (see chapter 17, Management) (Sandefer et al. 2015).

AHIMA lists the following knowledge topics as important for data analytics:

· Clinical, financial, and operational data

· Understanding of database queries (such as structured query language [SQL])

· Understanding statistical software

· Data mining

· Quality standards, processes, and outcome measures

· Risk adjustment

· Business practices (for example, workflow or payer guidelines)

· Medical terminology

· Healthcare reimbursement methodologies

· Classification systems

· Source data

· Qualitative and quantitative analysis (AHIMA 2015a)

Strategic Uses of Healthcare Information

There are many reasons to collect data and turn it into information, including administrative uses such as claims submission, revenue cycle management, meeting quality measurement reporting requirements, assessing health status and outcomes, and performing clinical research. As health information technology (IT) systems evolve, the ability to aggregate the collected data improves and the information from it better supports strategic analytics and organizational decision-making. Through interpretation and evaluation of aggregated data from a variety of sources, development of strategies to improve patient care outcomes, reduce costs, and plan the future are possible through decision support, quality measurement, and clinical research, which are addressed in the following sections.

Decision Support

Information systems in healthcare are adopted for a variety of reasons. One of these is to improve the outcome in decision-making tasks. A decision support system (DSS) is an information system that gathers data from a variety of sources and assists in providing structure to the data by using various analytical models and visual tools to facilitate and improve the ultimate outcome in decision-making tasks associated with nonroutine and nonrepetitive problems. For example, the DSS can help administration decide whether to add an additional operating room. Management is the primary user of a DSS for operational as well as strategic decisions. It is not used for day-to-day decisions such as scheduling staff. A clinical decision support system (CDSS) is a “special subcategory of clinical information systems designated to help healthcare providers make knowledge-based clinical decisions” (Fenton and Biedermann 2014, 39). (Clinical information systems are discussed in more detail in chapter 11, Health Information Systems.) In DSS and CDSS, typically the problem in need of solving is unstructured or the circumstances are unknown. A CDSS could deliver targeted clinical decision support by supplying clinical reminders and alerts impacting the quality and efficiency of care. For example, within an EHR the clinician may receive a reminder that it is time for the patient’s annual gynecological exam.

With data, analytical models, and visual tools at their disposal, the user can perform simulations of patterns based on various assumptions, monitor and assess key indicators, or perform data comparisons to look for trends. For example, to evaluate the success or failure of interventions, track trends, and identify opportunities for improvement, a manager may monitor readmission rates using a scorecard generated by the DSS.

An executive information system (EIS), a type of DSS, facilitates and supports senior managerial decisions. Given that information is an enterprise strategic asset, an EIS is required to consider the broad needs of the healthcare organization. An EIS can transcend the organizational structure, transform the business by standardizing and describing solutions throughout the enterprise, and drive information-centric decision-making (3e Services LLC 2015).

The EIS is the source for identifying high-level strategic, operational, financial, or clinical issues. Rather than managing at the individual departmental level, an EIS can pull together financial, operational, and clinical information, with ­enterprise-wide policies and guidelines, to help the executive find actionable insights to drive enterprise performance. Organization-wide operational and informational processes improve with an EIS because business problems can be exposed, or business opportunities discovered. Examples of organization-wide operational and informational process key indicators executives may monitor include surgical volume and patient satisfaction. Figure 12.3 provides an example of a dashboard.

Figure 12.3 Example of dashboard

Source: © AHIMA Virtual Lab dashboard created with Tableau Software. Used with permission.

Quality Measurement

Using healthcare information to improve the quality of healthcare is not a new strategic initiative. What has changed, however, is the health IT ­available to collect and analyze the data for the purpose of turning it into healthcare information. For example, instead of manual data abstraction, which is the identification of data elements by an individual through health record review, data mining can extract clinical data directly from the EHR using standards and guidelines. Then the mined data can be compiled and used to report clinical quality measures. Healthcare information can also be used to improve care effectiveness; for example, alerts can be sent to administrators and physicians when measures related to quality and patient safety fall outside a normal range along with notifications of what may be causing these abnormalities. Also, health system effectiveness (for example, knowing which intervention was ineffective) could result in better healthcare outcomes for patients based on standards of care.

Clinical Research

Besides patient care, one of the original reasons for collecting data and analyzing its information is to research and study diseases and interventions. Information systems can support research by supplying the health data needed to inform clinical research programs and population and public health surveillance. In these cases, multiple sources of data are integrated into a central repository where it is possible to find early markers of disease, and historical data can be used to simulate and model trends in long-term care needs. For example, healthcare information such as an individual’s genetic profile and local trends in disease prevalence may be used in patient-centered outcomes research. (Chapter 13, Research and Data Analysis, covers research in more detail.)

Consumers and Healthcare Information

Consumers have become the focus when it comes to healthcare as a result of healthcare ­reform initiatives and the growth of digital technology. For example, quality reporting is often available to patients as is information on diseases that is written in a way that the average person can understand. The Office of the National Coordinator for Health Information Technology (ONC) has a strategic initiative to focus on technology and information to provide a higher quality of health. Terms such as patient-centered care, patient-centric care, and even person at the center are utilized and indicate the shift to the individual as the focal point when it comes to healthcare. The stated mission of the ONC is to make a positive impact on health at the community level and individual level by engaging consumers and making health information accessible (Executive Summary, n.d.).

What follows is a brief introduction to consumer health informatics, and an overview of information access and navigation tools such as patient portals. Social media in relation to health information is discussed. Information sharing specific to personal health records (PHRs) is then discussed.

Introduction to Consumer Health Informatics

Health informatics is the field of information science concerned with the management of all aspects of health data and information through the application of computers and computer technologies (Fenton and Biedermann 2014, 2). Adding consumers to health informatics makes them the focus for the technology that acquires, manages, maintains, and uses the data and information. Thus, “consumer informatics is the field devoted to informatics from multiple consumer or patient views” (AMIA n.d.). Consumer health informatics is a subtype of health informatics. A patient portal to a provider’s website where a PHR can be developed and maintained is an example of consumer health informatics. Clinical email communication, such as a physician reviewing lab results with a patient by sending the patient an email is another example of consumer health informatics.

With any number of computer technologies available to the consumer, such as mobile health (mhealth), health information is only a click away. Mobile health includes applications available for smartphones that provide health information. For example, wearable devices that show how many steps a person takes in a day or the distance they walk is a type of mhealth device. The focus of a mhealth tool is on patient self-care. Patients can engage in their care through numerous health IT technologies designed for information access and navigation as well as those that allow the sharing of information. These health IT technologies improve patient–provider communication, allow for closer patient monitoring, and increase information access, all of which facilitate patient involvement with care providers. For example, through social networking sites consumers can connect with others who have the same condition and learn about their experiences.

Health Literacy

An important piece of patient-centered healthcare is health literacy. Over the years, the definition of health literacy has evolved. Previously health literacy was thought of as merely a person’s ability to read health information (Cutilli and Bennett 2009). Current definitions of health literacy focus on specific skills needed to navigate the healthcare system and the importance of clear communication between healthcare providers and their ­patients. Health literacy is “the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions” (HHS 2010). People’s ability to navigate, share, and engage in their own healthcare is impacted by health literacy skills. Second to privacy and security concerns, health literacy is the leading barrier to lack of consumer use of patient portals and mhealth technologies (Arcury et al. 2017).

Today’s healthcare consumers are empowered to take part in managing their own health by becoming more health literate. However, many adults may not be proficient in health literacy and may lack the skills needed to manage their health and prevent disease (USDHHS 2008). Many factors contribute to the current state of inadequate health literacy, including “lack of coordination among health care providers, confusing forms and instructions, limited use of multimedia to convey information, insufficient time and incentives for patient education, differences in language and cultural preferences and expectations between physicians and patients, overuse of medical and technical terms to explain vital information” (HHS 2010, 25).

Health information management professionals support health literacy by ensuring patients’ ability to understand and act on health information (JC 2010). According to the National Action Plan to Improve Health Literacy, strategies that health information professionals can endorse to improve health information, communication, informed ­decision-making, and access to clinical and public health services include the following:

· “Help to train all health care staff in the principles of health literacy and plain language

· Create collections or repositories of materials (for example, insurance forms and instructions, informed consent and other legal documents, aftercare and medication instruction, and patient education materials) in several languages and review the materials with members of the target population

· Help to disseminate existing communication tools and resources for patients” (HHS 2010, 30)

Health information management professionals support health literacy as they take on the responsibility for encouraging the development of competent healthcare consumers. Health literacy actions that HIM professionals engage in include providing consumers, or their designee, access to their personal health information in “useable standardized electronic form” (Heubusch 2010) or explaining to patients and families what their health information says and how to use it (Czahor et al. 2016). Furthermore, HIM professionals can educate consumers on the importance of compiling and maintaining a PHR, along with what type of information to include and how to obtain the information (Grebner 2015). During a patients’ initial navigation on a patient portal, HIM professionals can serve as patient advocates by educating patients on HIPAA compliance with web-based and mobile device PHR applications (Grebner 2015). Health literacy training programs can also be developed by HIM professionals to give healthcare consumers the ability to understand these topics as well as where to find additional reputable information about their health conditions.

Health information can be overwhelming, even for people with advanced literacy skills (HHS 2008). As medical science continues to evolve rapidly, information learned during the school years often becomes outdated or is forgotten. Furthermore, health information provided to patients in a traumatic or unfamiliar situation is not likely to be retained.

Studies have shown that people who are more health literate are less likely to be misinformed about the body and natural causes of disease and their relationships to lifestyle factors. For example, without knowledge, patients may not understand how lifestyle factors such as diet and exercise affect many health outcomes.

Skills necessary to be health literate include reading, listening, analytical and decision-making skills, as well as the ability to apply these skills to various healthcare situations. For example, it includes the ability to know when to seek medical care, understand instructions on prescription drug bottles, appointment information, medical education brochures, physician’s directions and consent forms, and the ability to navigate complex healthcare systems (NNLM, n.d.). Another important health literacy skill is numeracy, the ability to understand and use numbers. Examples of numeracy skills include understanding nutrition labels, measuring medications, and calculating cholesterol and blood sugar levels. Each of these tasks requires mathematical skills. Another example is electing a health plan or comparing prescription drug coverage, which requires calculating premiums, co-pays, and deductibles (HHS n.d.a). Figure 12.4 shows the four levels of literacy.

Figure 12.4 Four levels of Literacy

Source: AHIMA 2010.

Addressing health literacy issues is not the sole responsibility of those providing healthcare services. Healthcare policymakers, purchasers and payers, regulatory bodies, healthcare consumers, and patients themselves all play important roles in ensuring health literacy. Culture is also a very important part of health literacy. Recognizing the role that culture plays in how people communicate, understand, and respond to health information helps better to understand health literacy (HHS n.d.b).

Telehealth

The use of technology to connect a patient and a clinician across a distance is the chief component of telehealth (see chapter 11, Health Information Systems, for more information.). Telehealth can also be used to send clinical information on the daily status of a patient’s health to a physician via technology. In the evolving area of consumer health informatics, telehealth is being focused on to increase access to and provide quality healthcare. Telehealth is an option utilized to monitor chronic disease in patients and to provide access to medical care in locations that are lacking in clinical staff. This allows for patients as consumers to be active in decisions related to their health and to use digital technology to gain access to healthcare (Demiris 2016). Telehealth can be used to track vital signs and monitor other clinical information such as blood sugar. Telehealth provides a way to interact with patients and caregivers and to engage them in short-term and long-term health-related decision-making. Short-term conditions are nonurgent or nonemergency medical situations. For example, a short-term condition might be an ear infection or an upper respiratory infection. Long-term use of telehealth can be focused on more intensive healthcare intervention such as cardiac monitoring. As the consumer, an individual with a chronic condition can be more involved in their healthcare. Another factor of telehealth for the consumer may be the cost. Telehealth medical visits may cost less than an office visit, making them advantageous for the consumer (Wicklund 2018). One barrier is patient adoption of telehealth options. The ONC recognized this barrier in a published white paper, Designing the Consumer-Centered Telehealth & eVisit Experience: Consideration for the Future of Consumer Healthcare (Bobinet and Petito n.d.). With the ability to access urgent care and emergency centers, the role of telehealth is still being examined by stakeholders especially in areas where remote access may not be warranted. In the white paper, the ONC identified nine important principles of consumer-focused telehealth design and incorporation as a part of the healthcare option tools. Several of these principles focus on the technology aspects. For the consumer, the vital principles center on the experience for the patient and ensuring there is a balance between accessibility, data overload, meaningful care, and quality of care (Bobinet and Petito n.d.).

Information Access and Navigational Tools

The Medicare and Medicaid EHR Incentive Programs funded by the American Recovery and Reinvestment Act of 2009 stimulated the healthcare industry to adopt EHRs. One of the objectives to achieve Meaningful Use (MU) (now Promoting Interoperability) for certified EHR technology is to provide patients with the ability to electronically view, download, and transmit their health information within a certain number of days of the information being available to the eligible professionals (physicians and other healthcare professionals identified by the law). By providing patients access to an electronic copy of their health information they and their caregivers can be more engaged in their care. (Promoting Interoperability is explained in more detail in chapter 16, Fraud and Abuse Compliance.)

Consumer health IT applications for information access and navigation include hardware, software, and applications accessed via a computer, tablet, or phone. The ability to use mobile devices, patient portals, and social networking websites allows consumers to not only manage their health information electronically but also to participate in their healthcare via electronic means.

Mobile Devices

Portable, wireless computing devices or mobile devices include tablet computers, laptop computers, and smartphones. These devices combined with mobile medical apps can help consumers gain access to useful information wherever they may be and whenever it is needed. Apps for smartphones include pharmaceutical references with information about side effects and dosage amounts, access to licensed healthcare professionals allowing video chats about a medical problem, and guides providing step-by-step first aid instructions.

According to the US Food and Drug Administration (FDA), a mobile medical app is a mobile app that meets the definition of device in the Federal Food, Drug, and Cosmetic Act and either is intended “to be used as an accessory to a regulated medical device; or to transform a mobile platform into a regulated medical device” (FDA 2015).

The FDA considers the mobile app’s intended use in determining whether the definition of a device has been met. The FDA guidance states a mobile app intended for use in performing a medical device function (such as for diagnosis of disease or other conditions) is a medical device, regardless of the platform on which it is run (FDA 2015). An example would be mobile apps intended to run on smartphones to analyze and interpret EKG waveforms to detect heart function irregularities.

Patient Portals

A patient portal is an information system that ­allows consumers to log in to a secure online website to gain access to personal health information and navigate around it once inside the information system. The types of patient portals and the modules implemented will offer the following ­different functionalities:

Accessing a subset of the patient’s health records (for example, medical history, health issues, medication list, test results, care plans, allergy list)

Sending a secure message to the patient’s healthcare provider(s)

Uploading clinical information and telemetry (for example, blood pressure, blood glucose values, and weight measured at home)

Completing forms electronically instead of on paper (e-forms)

Accessing a child’s or elderly parent’s records with appropriate authorization (proxy access)

Scheduling appointments

Requesting medication refills

Accessing billing records

Paying bills online (Carayon et al. 2015)

A patient portal is a way to engage patients by giving them an avenue to access and review their health history (AHIMA 2016). Patient portals are one element in the focus on increasing communication, using technology, and engaging patients (AHIMA 2016). For example, a patient may come to the patient portal to learn more about symptoms he or she is experiencing. An interactive decision tool would help the patient assess the symptoms through a series of questions. If the patient visits the portal to better understand a current diagnosis, a link to educational material about the condition is available. Either scenario may result in communication with the provider via secure messaging about what was learned.

Social Media

Social media is defined as websites or applications that provide an avenue for personal networking and the sharing of information. A number of healthcare-focused social networks are available to consumers as individuals come together to interact and receive support from others with similar interests. Online communities specific to a condition or disease provide the consumer with information about the condition and which treatments may have greater success than others.

Providers use social media to inform consumers about diseases, conditions, and treatments. For example, Mayo Clinic’s website contains patient care and health information on many diseases and conditions. In addition, the site has a symptom checker; a list of tests and procedures that includes a definition of each, how the test or procedure is performed, and how to prepare; risks and results; and details about drugs and supplements. Large well-known healthcare institutions such as the Cleveland Clinic and Johns Hopkins Medicine publish photos and videos. For example, the Cleveland Clinic provides patient education videos that contain actual surgery images.

Government agencies also use social media to inform consumers about healthcare. The Centers for Disease Control and Prevention (CDC) lists a number of mobile social activities including apps, a CDC video streaming station, and infographics. The Department of Health and Human Services Office of Disease Prevention and Health Promotion has initiatives aimed at healthcare consumers, which contain evidence-based health information and tools to help consumers make informed health choices along with their healthcare providers.

Personal Health Records

An important piece of patient-centered healthcare is information sharing. Patients generate data outside of provider settings. Sharing it with their providers expands the depth, breadth, and continuity of information resulting in the potential for improved healthcare outcomes.

Health IT tools connect patients and providers and allow them to share information, which strengthens the consumer engagement experience. Patient portals discussed previously are one such tool. Another tool is the personal health record. A PHR is a record created and managed by an individual in a private, secure, and confidential environment. It differs from an EHR, which is created and managed by the healthcare provider. A PHR can be about the individual’s health or the health of someone in his or her care and be used as a tool to collect, track, and share past and current information. Sharing the contents of a PHR with providers can enhance existing data, fill in information gaps, and provide a more complete picture of a patient’s health. Other benefits of a PHR are improved patient ­engagement and enhanced provider–patient communication.

Data from a PHR is patient-generated health data (PGHD). ONC identified PGHD as an important issue for advancing patient engagement because patients may become more involved with their care when patient–provider communication includes the use of the patient-generated data as part of healthcare decision-making. According to ONC, PGHD are “health-related data created, recorded, or gathered by or from patients (or family members or other caregivers) to help address a health concern” (ONC 2018a). Examples of PGHD include health and treatment history and data from a wearable monitor, such as an exercise-tracking device

Information in Personal Health Records

PHRs can contain information from several sources including patients and healthcare providers. While there is not a standard set of data and reports to include in a PHR because specific content depends on the type of healthcare received, the following reports are common to most health records:

· Identification sheet. Form originated at the time of registration that contains demographic information

· Problem list. List of significant illnesses and operations

· Medication record. List of medications prescribed or administered

· History and physical. Past and current illnesses and surgeries, current medications and family history, as well as a physical exam performed by the physician

· Progress notes. Notes made by the physicians, nurses, therapists, and social workers that reflect their observations, the patient’s response to treatment, and plans for continued treatment

· Consultation. Opinion about the patient’s condition made by a physician other than the attending physician

· Physician’s orders. Physician’s directions to nurses and other members of the healthcare team regarding medications, tests, diets, and treatments

· Imaging and x-ray reports. Findings of x-rays, mammograms, ultrasounds, and scans

· Lab reports. Results of tests conducted on body fluids

· Immunization record. Documentation of immunizations given for diseases such as polio, measles, mumps, rubella, and the flu

· Consent and authorization forms. Consents for admission, treatment, surgery, and release of information (AHIMA 2015b)

Other health information such as exercise and diet plans, health goals, and home monitoring system results such as blood pressure levels may also be a part of the PHR.

Models of Personal Health Records

A PHRs can be as simple as paper documents placed into a folder. However, an electronic PHR is better because of the accessibility factor and to gather, update, integrate, and manipulate the information more easily.

The two main types of electronic PHRs are the following:

1. Stand-alone. Patients fill in information they want to share with their healthcare provider. The information is stored on patients’ computers or through an online system. Some stand-alone PHRs accept data from external sources, such as healthcare providers and laboratories. Patients choose with whom they share the information.

2. Tethered or connected. A type of PHR that is linked to a specific healthcare organization’s EHR. A tethered PHR allows patients to access their records through a secure portal (HealthIT 2014).

There are many sources of PHRs. In addition to those listed, employers and independent vendors offer PHRs. Connecting the PHR to the patient’s legal health record protects it under the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule (ONC n.d.). (Chapter 9, Data Privacy and Confidentiality, provides more ­detail on HIPAA.)

Patient Safety

The World Health Organization (WHO) defines patient safety as “the prevention of errors and adverse effects to patients associated with health care” (WHO 2018). Sharing the contents of a PHR with providers can enhance existing data, fill in information gaps, and provide a more complete picture of a patient’s health, creating an opportunity to improve patient safety. For example, a PHR with information about allergies, medications, and adverse drug reactions compiled from multiple sources can be used by a provider to reconcile the information against what is contained in the EHR, thus preventing medication errors or adverse events leading to patient harm. PHRs also support telehealth capabilities where access to the health information could impact clinical decision-making. In a medical emergency situation, a PHR may provide information when the patient cannot. Telehealth is covered in more detail in chapter 11, Health Information Systems.

Health Information Exchange

Health information exchange (HIE) is an important part of the healthcare industry. While there are several definitions of HIE, all of them note the exchange of information is done electronically, and the capacity exists for different information systems and software applications to exchange data. These definitions are the following:

· A HIE is the exchange of health information electronically between providers and others with the same level of interoperability, such as labs and pharmacies.

· A HIE “allows physicians, nurses, pharmacists, other healthcare providers, and patients to appropriately access and securely share a patient’s vital medical information electronically—improving the speed, quality, safety and cost of patient care” (ONC 2018b).

· A HIE “provides the capability to electronically move clinical information among disparate healthcare information systems and maintain the meaning of the information being exchanged” (HIMSS 2014).

Determining a course of treatment having only the information contained in a single health record or encapsulated by a single provider of care is shortsighted and could result in duplicative treatments. While not an easy task, moving away from an ownership view of health data to a continuity of care perspective facilitates coordinated patient care. Successfully exchanging and integrating the information into clinical work practice fills information gaps and provides a more complete picture of a patient’s health situation resulting in more informed clinical decisions by the healthcare team.

The remaining portion of this chapter provides an introduction to HIE, lists its forms, describes the benefits and users of HIE, explains eHealth exchange, states the challenges with sharing healthcare information, and identifies HIE roles for HIM professionals.

Impact of HIE

Health Information Technology for Economic and Clinical Health (HITECH) legislation and MU ­regulations mandate HIE functionality and its use. A qualified EHR under HITECH includes, as one of the criteria, that the EHR has the capacity to exchange electronic health information with and integrate such information from other sourcs (45 CFR 170.102). A health information exchange organization is one that supports, oversees, or governs the exchange of health-related information among organizations according to nationally recognized standards. These health information exchange organizations provide the means for HIE to occur. The health information exchange organization compiles data from a number of healthcare providers so that the physician currently treating the patient has a complete picture of the patient’s medical history and treatment including all current medications.

With the introduction of the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA), the MU mandate for participating in the Medicare EHR Incentive Program transitioned to become one of four components of a new Merit-Based Incentive Payment System (MIPS) (HealthIT 2019). The focus of the new MIPS remains on quality, cost, and use of certified EHR technology (CEHRT) in a cohesive program that avoids redundancies (HHS 2016). Ultimately the MACRA establishes new ways to pay physicians for caring for Medicare beneficiaries (NRHI n.d.) and further enables data sharing. For details about the MACRA and the MIPS, refer to chapter 11, Health Information Systems.

Health information exchanges are achieving national healthcare reform goals of better-quality care, improved population health, and lower costs. Recent studies show when physicians, nurses, pharmacists, and other healthcare providers can share a patient’s computerized medical information electronically, decreased duplicated procedures, reduced imaging, lowered healthcare costs, and improved patient safety occur (Landi 2018).

An acronym that is sometimes confused with HIE is HIX, or health insurance exchange. A HIX is a marketplace where patients can choose a health insurance plan based on price. A HIX evolved as a result of the ACA in efforts to assist with the health insurance market reform. While the Affordable Care Act (ACA) itself refers to these entities as exchanges, the endorsed term when referring to Americans using the exchange is health insurance marketplace. Health insurance exchanges are also known as marketplaces, health benefits exchange, health care exchange, health insurance marketplace, and affordable insurance exchanges (Karl 2012; Obamacare Facts 2018). Health insurance exchanges are discussed in more detail in chapter 15, Revenue Management and Reimbursement.

Interoperability

Health information exchange and health information interoperability are not the same. Interoperability is defined as the ability of computers to share information. An interoperable health IT environment is one in which seamless health information exchange is possible across diverse EHR systems and the information is understood and shared with those in need of it at the time it is needed. There needs to be some exchange for interoperability to occur. What happens to the information after it is exchanged determines whether interoperability occurs. If the information is accepted—for example, an email is sent from one computer to another—then there was an exchange. However, if the information exchanged is understood by both computer systems and no meaning is lost when exchanged resulting in seamless use of it, this series of events meets the definition of interoperability. For additional information on interoperability, see chapter 6, Data Management.

Forms of Health Information Exchange

The three key forms of HIE. Standards, policies, and information technology serve as the foundation for the following three forms:

1. Directed exchange is the “ability to send and receive secure information electronically between care providers to support coordinated care” (ONC 2018b). Examples of patient information include ancillary test orders and results, patient care summaries, and consultation reports. The encrypted patient information is sent electronically and securely between parties with an established relationship. For example, directed exchange is used to report public health data to the state health department.

2. Query-based exchange is the “ability for providers to find and/or request information on a patient from other providers, often used for unplanned care…. Query-based exchange is used to search and discover accessible clinical sources on a patient” (ONC 2018b). For example, a query-based exchange can assist a provider in obtaining a health record on a patient who is visiting from another state, resulting in more informed decisions about the care of the patient.

3. Consumer-mediated exchange is the “ability for patients to aggregate and control the use of their health information among providers” (ONC 2018b). For this form of exchange the patient, not the provider, is the driver. For example, a patient portal may allow personal health information to be uploaded for provider access.

Benefits of Health Information Exchange

There are many benefits to HIE. One of the primary benefits is enhanced patient care coordination. Other potential benefits for patients, providers, payers, and communities include the following:

· Reduction of duplicative treatments

· Elimination of redundant or unnecessary testing

· Fewer medication and medical errors, which can be costly and have a negative impact on the patient

· Increased patient safety

· Achievement of a basic level of interoperability

· More informed decision-making for more effective care and treatment

· Improved public health reporting and monitoring

· Improved transitions of care

· Improved population health

· Improved efficiency in the healthcare system

· Reduction in paperwork, allowing more time for discussions about health concerns and treatments

Users of Health Information Exchange

Essential to changing from a fragmented provider-centric healthcare system to a patient-centered one are the users of the health information. Physicians, laboratories, hospitals, pharmacies, consumers, health plans, payers, and communities are all examples of users of electronically exchanged information. For example, a primary care provider electronically sends a clinical summary that includes basic clinical information regarding the care provided such as medications, problems, upcoming appointments, or other instructions to the patient portal.

Health information exchange requires a team effort to be successful. Technologically capable and willing exchange partners need to exist. Functionality within the EHR needs to exist so a conversation with the vendor is necessary to determine HIE capability or the time frame for availability. Even if the functionality is there, a lack of cooperation among EHR vendors can hinder exchange. In addition, how the data are integrated into existing records and workflow can be challenging for providers. There may also be state laws blocking access to patient data. Other barriers to HIE users are competing priorities, financial concerns, issues related to data ownership, and privacy and security. Also, there must be a mechanism to allow patients to opt in or opt out of participating in HIE.

eHealth Exchange

The HIE can occur at the local, state, regional, and national levels. The eHealth Exchange is a nationwide community of exchange partners. The community of federal and state agencies, large provider networks, hospitals, medical groups, pharmacies, technology vendors, payers, and ­others agree to securely share information via the internet using a common set of standards and specifications. Components of an eHealth Exchange include the ­following:

· A Legal/Trust Framework, where participants agree to one set of legal/trust documents in order to be able to exchange data with all other participants

· A governance model that incorporates a broad spectrum of perspectives by involving a representative set of participants from industry and government

· Defined operating policies and procedures, so that all participants know what is expected of them and their end users, and in turn, they know what they can expect from other participants

· Technical services, such as a service registry directory of the other Exchange participants, a security layer based upon a public key infrastructure, and interoperability testing

· Operational support, such as interoperability subject matter expertise, convening capabilities and meeting support, governance expertise, technical expertise and services, and outreach (The Sequoia Project 2016)

The eHealth Exchange has been successful in interoperable sharing of clinical information such as care summaries and quality data. In 2012 the eHealth Exchange transitioned its management to The Sequoia Project, a nonprofit 501(c)(3) to advance the implementation of secure, interoperable nationwide health information exchange. The eHealth Exchange has become the nation’s largest health data sharing network, supporting 120 million patients (HealthIT n.d.).

Challenges with Sharing Healthcare Information

ONC’s principal objective for electronic health information exchange is for “information to follow a patient where and when it is needed, across organizational, health IT developer and geographic boundaries” (ONC n.d.). For example, health ­information that can follow a patient as they encounter healthcare at a physician office, in an acute-care hospital, and at a skilled nursing facility; all are examples of possible times when health information would cross cities and different types of healthcare organizations. While this is a creditable goal, there are challenges to sharing health information among stakeholder groups from a cultural as well as technical standpoint. Two such challenges are patient identity and data standards.

Patient Identity

When it comes to patient identity and HIE, integrity is of prime importance to linking the patient to the correct information. The ability to match patients and health information begins with complete and accurate data collection. Errors identified should be corrected immediately to prevent issues with patient care that can result in poor data quality. Sophisticated algorithms such as those discussed in chapter 3, Health Information Functions, Purpose, and Users, should be used to help confirm a patient’s identity (AHIMA 2017).

Matching patient records to the correct person becomes increasingly difficult as organizations share records electronically using diverse information systems, and in a mobile culture where patients seek care in many healthcare settings. Some healthcare organizations use multiple information systems for clinical, administrative, and specialty services, which increases the chances of identity errors occurring when matching health records. Also, many regions experience a high number of individuals who share the exact name and birthdate, leading to the need for additional identifying attributes to be used when matching patient records.

Other issues and circumstances that lead to unmatched or mismatched health records include differences in how names and addresses are formatted in various information systems, the quality of data as it is entered into information systems at patient registration, and the creation of duplicate records for the same patient within an information system (ONC 2014).

There are two ways in which patient records fail to match accurately:

1. “Records for different patients are mistakenly matched. When medical records for different patients are mistakenly matched (known as a “false positive”), it can present safety and privacy concerns for patients. For example, a provider may inadvertently use information about the wrong patient, such as diagnoses or medication lists, to make clinical decisions. In addition, if the wrong patient’s medical information is added to a patient’s record, it could result in disclosure of that information to a provider or patient who is not authorized to view it” (United States Government Accountability Office 2019).

2. “Records for the same patient are not matched. When medical records for the same patient are not matched (known as a “false negative”), it can affect patient care. For example, providers may not have access to a relevant part of the patient’s medical history—such as current allergies or prior diagnostic test results—which could help them avoid adverse events and also provide more efficient care, such as by not repeating laboratory tests already conducted” (United States Government Accountability Office 2019).

Because of its complexity, establishing and maintaining patient identity and integrity is fraught with challenges, some of which include the following:

· Not requiring proof of identification at the time data are collected

· Not making accurate registration a priority in the emergency department

· Data quality issues with patient identification data stored and managed in siloed legacy systems

· Not correcting data errors in a timely and comprehensive manner (AHIMA Work Group 2014)

Data Standards

Data standards are the agreed-upon specifications for the values acceptable for specific data fields. Data standards allow healthcare organizations to exchange health information in a format that ensures the data remain comparable. A number of different types of data standards are used in healthcare to capture all of the administrative and clinical data that is needed. Some of these include the following:

· Logical Observations Identifiers Names and Codes (LOINC) is a standard that provides the structure for lab tests and clinical observations to be shared electronically.

· Clinical Document Architecture (CDA) creates documents in the health record such as discharge summaries and progress notes so that the information can be shared electronically with other healthcare providers.

· Continuity Care Record identifies key data that is needed as the patient moves from one healthcare provider to another for continued care. The data is what the healthcare provider needs to continue patient care.

· Digital Imaging and Communications in Medicine (DICOM) is the standard used to exchange images used in radiology (Sayles and Kavanaugh-Burke 2018).

ONC is harmonizing the standards utilized in healthcare. Harmonization is reviewing similar standards and working out the differences in the standards through a committee (ASTM International 2005). An example of a specific criterion developed by ONC in the data standard selection process is whether the standard is used by federal agencies to electronically exchange health information with organizations engaging in the eHealth Exchange. An outcome of this work is the publication of best available lists. Table 12.1 shows examples from these lists.

HIM Roles

The roles for HIM professionals in healthcare information can be expanded to include positions focused on HIE and consumer informatics. For the area of consumer information, the roles include working within a healthcare organization, physician practice group, or directly with consumers. Specific HIM roles could include patient portal representative, consumer advocate, PHR liaison, or patient information coordinator. Figure 12.5 lists the recommended best practices for HIM practitioners in a consumer or patient engagement role.

Figure 12.5 Recommended best practices for consumer or patient engagement

• Establish or participate in an organizational committee, council, or information governance board whose charge is to address facilitation of patient engagement. This group should review all existing and proposed policies and procedures related to health information access with an eye toward gaps and barriers to patient engagement.

• When health information is accessed electronically by patients through portals, ensure requests for clarifications, corrections, or ­amendments can be supported by automated workflow that confirms receipt of the request and routes the requests to the appropriate place and person.

• Reach out to community groups as a speaker on patient engagement.

• Work with clinicians to include a comprehensive set of clinical information, including physicians’ notes and other forms of ­documentation, within the patient portal that goes beyond limited information such as appointment dates and lab results.

• Take on a leadership role with the patient portal managing portal processes.

• Establish a central and convenient (to patients) location for receiving and processing requests for all types of health information ­regardless of media, department, or source. This means establishing a one-stop shop for archived paper records, compact discs, ­diagnostic imaging media, pathology slides, and such.

• Create policies and design workflows for accepting and managing patient-generated health information.

• Eliminate fees to patients for providing them with electronic copies of their health information.

• Stay up to date with public policy proposals and standards development that addresses and supports consumer engagement.

Source: AHIMA 2010.

The roles for HIM professionals in HIE include defining the data exchange model, developing guidelines for data stewardship and data governance, developing data integrity and quality standards, identifying strategies to ensure accurate patient identity, ensuring that privacy and security requirements are met, and performing provider and patient education about why HIE is important. A study conducted on trends in HIE organizational staffing found the data integration and master patient and client index roles as the primary staffing challenge and top jobs in demand (AHIMA and HIMSS 2012). Figure 12.6 lists additional HIM skills of value to HIE ­leadership.

Figure 12.6 HIM contributions to HIE

HIM professionals can bring a variety of much-needed skills to HIEs. HIE leadership can look to HIM principles to provide support and guidance in the following areas:

• Drafting data gove4rnance and stewardship policies, including data ownership, data integrity, and data quality

• Creating release of information policies, procedures, and practices

• Managing master patient index and enterprise master patient index data conversions, development, and maintenance

• Addressing state and federal requirements for patient confidentiality

• Developing and implementing HITECH privacy and security rule requirements

• Meeting breach notification requirements

• Developing and implementing HIPAA privacy and security rule requirements

• Integrating data elements from multiple systems, organizations, and providers

• Identifying best practices in information management and records retention

Source: Washington 2014.

Real-World Case 12.1

A research project conducted by Geisinger Health System looked at the interaction between patients and their providers and pharmacists with medication lists were made available through a patient portal. For this ­research project, prior to an upcoming appointment, patients were able to review their medication lists and submit changes if the content was inaccurate. A pharmacist followed up with the patient either by phone or secure online messaging. The pharmacist reviewed the information submitted by the patient via the portal, revised the medication record, and informed the patient’s provider. The revision was also documented in the EHR along with the source of the change (Deering 2013).

At McGill University Health Centre, a medication reconciliation research project was conducted using a newly developed web-based software tool, RightRx. The objective of reconciliation at the hospital level was focused on patient safety and to align the community and hospital medication lists (Tamblyn et al. 2018). RightRx provided a way to automate the drug information and to sort drug orders from physicians. This study concluded that “future development should focus on standardization of medication administration data, order sentences to support dose-based prescribing, and patient-friendly information about medication changes” (Tamblyn et al. 2018). Both studies focused on the use of electronic tools in the area of pharmacological management.

Real-World Case 12.2

A local community hospital is planning a health fair. At the event, the clinical staff will provide free blood-pressure screening and basic dental exams. There will also be a 20-minute exercise clinic and information on healthy eating, smoking cessation, and maintaining a healthy lifestyle. Also, information will be presented via brochures on some of the most commonly seen diseases in the United States: diabetes, hypertension, Alzheimer’s disease, and chronic kidney disease. The team planning this event includes HIM professionals. When reviewing the planned events at the health fair and brochures, the HIM committee members are concerned about health literacy for this community outreach event.