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CHAPTER 1
HEALTH SYSTEMS INFORMATICS: A TRANSFORMATIONAL SCIENCE
Gordon D. Brown
Learning Objectives
After reading this chapter, you should be able to do the following:
• Understand the concept of open systems theory.
• Conceptualize health systems informatics and differentiate it from bioinformatics and biomedical informatics as an analytical framework.
• Explain the transformative power of information technology.
• Discuss the differences in concept but interdependencies in function between management information systems and health systems informatics.
• Apply clinical information technology to process improvement and system transformation.
Key Concepts
• Complex adaptive systems
• Conflict between business and clinical functions
• Health systems informatics and biomedical informatics
• Management information systems
• Transformational change
Introduction
Health systems informatics strives to align the disparate components of a healthcare organization — professional, financial, and organizational — to achieve optimal system performance. How did these components become so dysfunctional in an advanced nation such as the United States? The great irony is that we built the US health system that way, and we continue to maintain it that way.
Health systems informatics assumes a larger integrated systems perspective, according to systems theory (Encyclopedia.com 2001):
As a way of looking at things, the "systems approach" in the first place means examining objects or processes, not as isolated phenomena, but as interrelated components or parts of a complex. An automobile may be seen as a system; a car battery is a component of this system. The automobile, however, may also be seen as a component of a community or a national transportation system. Indeed, most systems can be viewed as subsystems of more encompassing systems.
This chapter examines the interdependencies among clinical, organizational, financial, and individual (patient) functions and the role of health systems informatics in guiding and connecting these functions to achieve optimal performance. Health systems informatics provides the decision-making logic that serves as the basis for designing, financing, managing, and evaluating the healthcare organization to improve performance. It enables and requires the transformation of the roles and behaviors of health professionals but does not diminish them. It views the clinical function from the perspective of complex adaptive systems. The result of these efforts is transformational change, wherein each function is fundamentally realigned to serve the more encompassing system.
Complex Adaptive Systems in Healthcare
Health systems informatics enables the conceptualization of an integrated clinical perspective, based on the theory of complex adaptive systems. Complex adaptive systems are characterized by large numbers of interdependent parts or agents — each with its own pattern relationships and interaction complexities — that adapt to and create their environments through coevolution (Akgün, Halit, and Byrne 2014; Birdsey, Szabo, and Falkner 2017; Lee and Mongan 2009). By nature, dynamic systems are transformational in that they call for new delivery models, professional roles, organizational structures, and system designs, but they are difficult to analyze. If systems reject or are slow to react to new enabling technologies, they will underperform and their very viability might be threatened. A systems perspective or systems theory recognizes the following:
1. The health of a population is not determined primarily by its health system, no matter how structured, but by its community and lifestyle.
2. Simple generalizations about how services should be provided or how change should be made — such as whether privatizing services will make them more efficient — are rejected. A systems perspective does not narrowly focus on legacy pieces of a system — such as how physicians should be educated, healthcare services financed, or information systems structured — but broadly considers how the overall system should function to obtain superior results.
3. The functions and strengths of both the private (investor-owned) and public sectors, as well as the nonprofit or "plural" sector that draws unique strengths from the private and public sectors (Mintzberg 2015), must be considered. The plural sector has long been the organizational basis for much of the US health system and might offer even greater potential for the future. A full exploration of the private, public, and plural sectors is beyond the scope of this book, but they are fundamental to an understanding of health system effectiveness and efficiency.
The design of health systems is not determined by traditional or prescriptive structures and roles of organizations, financing, health professionals, or information systems. Each of these functions is subordinate to optimal outcome measures of clinical quality, continuity, patient satisfaction, efficiency, and population health. This assumption is made more complex because no template or single pathway to achieving optimal performance exists. Systems theorists use the term equifinality to suggest that any given end can be achieved by many different means. However, the means do not define the system and are accountable to the desired end state. Each region and country envisions its own system structure; each will be different and each can be optimal. Information technology (IT) can provide the systems architecture that enables the structure of functions, but it cannot dictate them. Each function must pursue its own design, measured against the crucible of optimal system performance. IT can enable a national or even global health information exchange, consistent with a patient-oriented system. Such exchange is a goal, but each system must craft its own structure and not impose any preconceived or central planning (teleologic) design on the professions, organizations, or other functions. Central control that imposes rigid designs on health system structure and function has proven to be ineffective (Garrety et al. 2016). Commercial and business applications have demonstrated the ability to facilitate the accomplishment of corporate goals while allowing local autonomy and freedom to innovate.
Leading transformational change in clinical decision making is not based on a knowledge of computers, information science, or medical informatics. Although these sciences are important, they traditionally have been applied within health professions, organizations, and systems that are themselves obsolete. Health systems informatics assumes a broader focus, including providing the science for guiding how organizations, systems, and professional and patient roles can be structured to improve system performance. Each function has its own identity and integrity. Although interdependent, functions are neither defined by nor subordinate to another function but to optimal system performance only. All must be aligned, or the system will be dysfunctional. For example, organizations might try to enforce the use of clinical guidelines and protocols through bureaucratic rules and sanctions based on organizational logic and reward. Neither organizations nor professions possess the dominant logic for system structure; this structure is based on optimal clinical quality, system efficiency, and population health.
Patients should have access to information and participate in the clinical decision process, but they cannot determine the design or content of clinical decision support systems (CDSSs). Increased information sharing and participation of patients in clinical decisions will transform the health system.
In addition, the role of the health professions in society is essential and protected, which is justified by their contribution not to the profession but to what society determines as optimal system performance (as discussed in chapter 3). Such protection assumes that as society changes, the professionals will change. Maintaining the historical domains and decision-making context of the medical profession, for example, violates the physicians' protected role in society. However, a changing role for physicians does not necessarily mean a diminished role; in fact, it might be enhanced. The structure of healthcare organizations has long been detached from the clinical function and operates under the principle that organizations could not interfere with the practice of medicine and the autonomy of physicians. A landmark case occurred in the 1930s when Drs. Ross and Loos were removed from the Los Angeles County Medical Association. The association also wanted these physicians' licenses revoked for violating professional ethics by engaging in the corporate practice of medicine (Starr 1982, 299–304). The Ross-Loos Group had established a group practice and the first managed care plan in the United States — a prepaid health plan that emphasized prenatal care and childhood immunization. To enable the health system to harness the power of IT, it must undergo an equally disruptive transformation, like the one Drs. Ross and Loos had taken. In this book, we explore the field of health systems informatics as a transformational science.
Bioinformatics
The term informatics is credited to A. I. Mikhailov, of the scientific information department of Moscow State University, who first used it in his 1968 book Oznovy Informatiki (Foundations of Informatics) (Collen 1995). It is adopted from the Russian term informatik or informatikii, defined as a study of the "structure and general properties of scientific information and the laws of all processes of scientific communication" (Collen 1995, 39). This definition establishes informatics, at its root, as the study of linguistics applied broadly to scientific language. As such, the field of informatics combines basic science with computational science, particularly computer science.
Bioinformatics can be defined as a form of computational linguistics — the statistical or rule-based modeling of scientific information. In 1976, the Oxford Dictionary defined informatics as the "discipline of science which investigates the structure and properties of scientific information, as well as the regularities of scientific information activity" (Collen 1995, 39). The focus of bioinformatics is on the management, analysis, and interpretation of data from biological experiments and observational studies (Moore 2007). The sequence analysis of the three billion chemical base pairs that make up human DNA would not be possible without complex algorithms and powerful computers. The standard language and the volume of data were a perfect match for the computer, and the level of analyses grew with the rapid increase in computer memory and processing speed.
One might regard bioinformatics, with its focus on computational biology, as peripheral or unrelated to the topic of health systems informatics. Yet, IT enables bioinformatics to transcend the laboratory, informing clinical decision making as well as individual patients and consumers. Clinical bioinformatics has emerged as a field of translational science that integrates genomics and proteomics data with clinical data to provide molecular diagnostics, pharmacogenomics, and evidence-based clinical outcomes. Bioinformatics continues to evolve by incorporating diverse technologies and methodologies from disparate fields to apply advanced computational and informational tools to biomedical research (Mattick et al. 2014; Sarkar et al. 2011).
Medical Informatics
A 1999 report of the Biomedical Information Science and Technology Initiative (BISTI), formed by the National Institutes of Health, described the field of informatics applied to healthcare and labeled it biomedical informatics (Friedman et al. 2004). The term was broadly applied to include bioinformatics, imaging informatics, clinical informatics, and public health informatics (exhibit 1.1). Imaging and clinical informatics have generally been included in the description of medical informatics, and we use the term medical informatics as the inclusive term. Although nursing informatics is considered its own area of scientific exploration (chapter 7), it is discussed here under the inclusive heading of medical informatics because it draws on the same informatics core competencies that are applied to clinical practice. Although the BISTI report included public health informatics in its paradigm because it also draws on the same core competencies, we discuss it separately given its primary focus on population health and not medical care. The BISTI report was an important contribution because it delineated a core body of knowledge for informatics applied to the range of areas. Our focus builds on it to explore how this technology helps enable the transformation of these areas.
The underlying theories, techniques, and methods that serve as the core competencies of medical informatics are algorithms, data structures, database design, ontology/vocabulary, knowledge representation, programming languages, software engineering, modeling, and simulation (Friedman et al. 2004). Medical informatics originated from the clinical area of pathology, which had developed standardized language applied to large data sets, requiring the distribution of standard test results to a range of clinical services. Physicians demanded tests that used standardized measures and processes so that the results could be interpreted on the basis of good science. Imaging informatics also benefited from standardized measures and language but lagged as a result of the limitations of the computer to store and process large amounts of data. In contrast, clinicians in other specialties valued flexibility in language (e.g., text over drop-down lists), tailoring medical records to individual choice and clinical decisions based on individual judgments and not the evidence derived from CDSSs.
The step from measuring and reporting laboratory findings to developing the electronic medical record (EMR) was a major advance. The idea of an EMR has been around for half a century, but it proved to be a complex challenge in part because the structure of clinical information lacked basic vocabularies and data standards essential for a unified language. Automating the medical record was greatly hampered by the lack of computing power and a common clinical vocabulary. The first initiatives in EMR development focused on enabling clinicians to record and retrieve clinical data to frame a diagnosis and treatment plan. The EMR could locate and present clinical information about previous conditions, tests, diagnoses, and treatment protocols. The operative question was whether the EMR would be developed by individual clinicians, departments, organizations, or the health system. Lacking a clear conceptual framework or vision of future applications, healthcare entities pursued all four approaches, resulting in EMRs that were not compatible — even within institutions. The valued priority was initially to maintain organizational autonomy and not integration. Developing a common clinical language was thus made more complex by the proliferation of different systems with different vocabularies and syntax.
The computer has become a tool for health professionals to record, store, retrieve, process, distribute, and integrate clinical information. As the health professions maintain and advance their own informatics perspectives, they inherently embrace collaboration and develop a greater team orientation, both of which are essential for improving clinical care. Digitizing information brought about changes in "cognitive and human factor interfaces," but these changes were limited to clinical decision making within fundamentally traditional roles. This form of change might be characterized as evolutionary or transactional as opposed to transformational or innovative (Havighurst 2008; Herzlinger 2006; Stange, Ferrer, and Miller 2009). The availability of electronic information enabled not only greater and better information to be processed in a more readable form but also clinical evidence to be displayed and shared to support decisions that allow health professionals to better serve patients. Through this process, disparate EMRs became more standardized and integrated, enabling them to share clinical information and to access evidence-based clinical guidelines, thus transforming them into electronic health records (EHRs).
Public Health Informatics
The first major conceptual development in informatics was in public health; although it used the same logic, it occurred long before the computer was envisioned. In the early 1800s, public health workers in many countries saw the need for a common vocabulary for classifying diseases and causes of death that would enable the establishment of surveillance programs locally, nationally, and even internationally. The first initiative to standardize clinical information was the development of the International Classification of Diseases (ICD), first by the International Statistical Congress in 1853 and later by the World Health Organization (2018).
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