M1- Assignment

Additional Supporting Theories and Sciences

These additional theories and sciences include communication theory, information sciences, computer science, group dynamics, change theories, organizational behavior, learning theories, management science, and systems theory.

Theory/Science

Key Ideas

Information Communication Model

“The fundamental problem of communication is that of reproducing at one point, either exactly or approximately, a message selected at another point” (Shannon, 1948, p. 379).

Sender→Medium (Noise and Distortion)→Receiver

Encoder and Decoder

Focus—Analyze information transfer and communication effectiveness and efficiency

Information Sciences

Exploitation of scientific and technical information of all kinds and by all means.

Application of science and technology to general information handling.

Branches:

Information retrieval

Human-computer interaction

Information handling within a system

Computer Science

Engineering and technology of hardware, software, and communications.

Includes aspects of information and cognitive science.

Group Dynamics

Focuses on the nature of groups.

Influence of a group may rapidly become strong, influencing or overwhelming individual proclivities and actions.

Within every organization, there are formal and informal group pressures.

Change Theories

Change in people or social systems, such as healthcare organizations.

Informatics specialists are change agents.

Seek to manage impact of IS to yield positive results.

Two perspectives:

Planned Change—Kurt Lewin

Unfreezing

Moving

Freezing

Diffusion of innovations—E. Rogers

Process for communicating an innovation throughout a social system.

Innovators

Early adopters

Early majority

Late majority

Laggards

Rogers identified five perceived characteristics of an innovation that affect the rate of adoption:

Relative advantage

Compatibility

Complexity

Trialability

Observability

Adoption of an innovation by an individual is dependent on the perceptions the individual has of that innovation.

Organizational Behavior

Focuses on small groups and individuals within organizations.

Organizational health requires a balance, among participants, of:

Autonomy

Control

Cooperation

Guides plans for system implementation.

Learning Theories

Changes in knowledge, skills, attitudes and values. More than 50 major theories of learning.

Types of theories:

Behavioral

Cognitive

Adult learning

Learning styles

Management Science

Use mathematics and other analytical methods to help make better decisions of all kinds, including clinical decision-support applications.

Methods:

Forecasting

Decision analysis

Inventory models

Linear programming

Graph theory and network problems

Queuing theory and waiting line problems

Simulation

Systems Theory

Studies the properties of systems as a whole.

Focuses on the organization and interdependence of relationships.

Boundaries:

Open

Closed

Systems are constantly changing.

Dynamic homeostasis

Entropy

Negentropy

Specialization

Reverberation

Equifinality

Informatics Specialties within Healthcare

In general, informatics, as it applies to healthcare, is comprised of several specialties based on areas of application and inquiry. Historically, two terms were interchangeably used to refer to the field: medical informatics and bioinformatics. These terms reflected either a medical orientation of the profession (e.g., the use of information-technology tools and approaches by medical doctors) or a biological orientation focused on issues around basic biology (e.g., the human genome project that determined the sequence of human DNA and mapped all of the genes). Over time, with emergence of new health-informatics disciplines, such as nursing informatics or imaging informatics, both of the terms were used to refer to the new subfields. These traditional terms were also incorporated into the names of the major health informatics organizations, for example, International Medical Informatics Association (IMIA).

More recently, however, with a growing understating of the expanding body of work within the field, many organizations have revised their agendas and visions to incorporate a broader scope of informatics specialties. For the purposes of a more detailed description of specialties, the general view of informatics suggested by the American Medical Informatics Association (AMIA) will be used (Kulikowski et al., 2012). AMIA now refers to the discipline as biomedical informatics, defined as “the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, driven by efforts to improve human health” (Kulikowski et al., 2012, p. 933).

Figure 2-5 Biomedical informatics and its areas of application and practice, spanning the range from molecules to populations and society.

Source: From AMIA Board White Paper: Definition Of Biomedical Informatics And Specification Of Core Competencies For Graduate Education In The Discipline by Casimir A Kulikowski, Edward H Shortliffe, Leanne M Currie et.al. in Journal of American Medical Informatics Association. Used by permission of Oxford University Press/ on behalf of the sponsoring society if the journal is a society journal.

Figure 2-5 Full Alternative Text

As depicted in Figure 2-5, this definition suggests that biomedical informatics is a core discipline that provides methods, techniques, and theories to its subdisciplines including (1) bioinformatics and structural (imaging) informatics; (2) health informatics, including ­clinical informatics (with subfields of nursing, medical, and dental informatics) and public-health informatics (also referred to as population informatics to incorporate global health informatics); (3) and informatics in translational science with subfields of translational bioinformatics and ­clinical-research informatics. AMIA’s definition also suggests that biomedical informatics lends its approaches to solve problems across the spectrum, ranging from molecular and cellular levels to the patient and population levels. The following descriptions define each of the subdisciplines:

Bioinformatics is often defined as studying biology (e.g., physical and/or chemical structures of macromolecules) by applying informatics skills to understand and organize the information associated with these molecules on a large-scale. Bioinformatics is primarily concerned with three types of data from molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experimentation (e.g., gene expression data). Additional types of data that are often used in bioinformatics might include the scientific literature (e.g., large collection of articles from Pubmed on genomic associations), taxonomies and standard terminologies (e.g., gene taxonomies), and protein-protein interaction data. Informatics techniques are applied on these data to achieve clinically meaningful tasks, such as designing new drugs.

Structural (imaging) information refers to research and practical applications concerned with representing, managing, and using information about the physical organization of the body (Brinkley, 1991). The notion of structural in the subdiscipline name often refers to the structure of objects in space. Informatics methods are used to store, study, and use data from studies about human-body structure. For example, a chest computerized-tomography (CT) image can be classified via image recognition with machine learning to identify or rule out a presence of lung cancer (van Rikxoort & van Ginneken, 2013).

Nursing informatics is a subdiscipline of clinical informatics included in the general domain of health informatics. Nursing informatics uses nursing knowledge, along with information and communication technology to promote the health of individuals, families, and entire populations. For more information, see chapter 1 of this book.

Medical informatics is another subdiscipline of clinical informatics included in the general domain of health informatics. Medical informatics refers to research and practice in clinical informatics that focuses on disease and predominantly involves the role of physicians. This term was used interchangeably with other terms in the past to refer to the discipline of biomedical informatics as a whole (Kulikowski et al., 2012).

Dental informatics is yet another subdiscipline of clinical informatics included in the general domain of health informatics. It is defined as a multidisciplinary field that seeks to improve health care through the application of health-information technology and information science to dental-health delivery, information management, healthcare administration, research, and knowledge sharing.

Public-health informatics, included in the general domain of health informatics, is the science of applying information technology in areas of public health, including prevention, preparedness, health promotion, and surveillance. Public-health informatics takes a perspective of groups of individuals and focuses on work, neighborhoods, and environment of work and living places, among others. Some of the common areas in public-health informatics include biosurveillance (e.g., mentions of new spreading viruses on social media), epidemic-outbreak management, or ranking neighborhoods in one county in terms of health problems.

Translational bioinformatics, included within the domain of informatics in translational science, combines applications of health informatics, bioinformatics, and structural informatics to identify genomic and cellular mechanisms to explain and predict clinical phenomena. Translational bioinformatics develops innovative techniques for the integration of biological and clinical data to create a more personalized healthcare. The recent emergence of precision medicine, aimed at providing all individuals with access to personalized information for better health, builds heavily on translational-bioinformatics methods to develop accurate and personalized characterization of patient populations based on molecular, clinical, environmental exposures, lifestyle, and other patient information (Frey, Bernstam, & Denny, 2016).

Lastly, clinical-research informatics is primarily focused on methods supporting clinical and translational research. Its goals are discovery and management of new knowledge about diseases and health. Clinical-research informatics is often applied to identify ways for secondary research use of clinical data or to manage information related to clinical trials (Kulikowski et al., 2012).

All the subdisciplines of biomedical informatics interact among each other to provide a comprehensive suite of informatics tools for better healthcare practice and research. Nursing informatics draws on informatics disciplines, such as medical or public-health informatics to advance its goals of promoting health worldwide. On the other hand, other informatics subdisciplines need nursing informatics to achieve their goals; for example, medical-informatics problems will often depend on nursing data to identify appropriate solutions. For instance, physicians prescribing medications need to understand a patient’s adherence status to be able to match complex medication regimes for a specific patient.