Philip J. Scotta,1, Nicolette F. de Keizerb and Andrew Georgiouc
a Centre for Healthcare Modelling & Informatics, University of Portsmouth, UK
b Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Institute, The Netherlands
c Centre for Health Systems and Safety Research, Macquarie University, Sydney, New South Wales, Australia
1 Corresponding Author: Philip Scott, E-mail: email@example.com
Kurt Lewin, the pioneer of social psychology, famously said that ’there is nothing more practical than a good theory’ . We agree and hope that readers of this book will come to share this view. Our aim is to provide a scientific knowledge base to support education, research and implementation. The editors came together as proponents of evidence-based health informatics within the European Federation of Medical Informatics (EFMI) Working Group on Health IT Evaluation and the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development . We have the shared belief that theory is insufficiently considered in our field along with the collegiate aim to improve the status quo. We want to move theory from a niche interest to a core concern of health informatics, to contribute to the maturity of the discipline and above all to improve care by effective health IT interventions. Specifically, this book was motivated by the outcome of a workshop at Medinfo 2015 that called for a “theory toolbox”, as elaborated in a paper at Medical Informatics Europe 2016 . There are distinct audiences and corresponding benefits from taking a theoretically-informed approach to health informatics. For implementers, application of theory can help adoption of best practice and work towards demonstrating improved outcomes of health informatics interventions. For researchers and evaluators, knowledge of theory can help to identify gaps in knowledge and hence prioritise, justify and guide research and evaluation where they are most needed. For educators, using theory can instil a scientific approach in their students. Importantly, we believe that all of these groups can be termed “practitioners” of health informatics (as our book title suggests). Overall, the purpose of the book is to move forward the agenda of evidence-based health informatics  by emphasising theory-informed work that aims to “enrich our … understanding of this uniquely complex field” . We have not set out to offer an exhaustive or comprehensive coverage of theory in health informatics. As our final chapter elaborates, we know that there are important topics that we have not been able to include in this volume. However, we do believe that this book discusses some of the most important and commonly used theories relevant to health informatics and that this collection marks a significant milestone on the journey. We want this book to constitute a first iteration of a consolidated knowledge base that can advance the science of our field.
To introduce the textbook, let us first clarify its scope: What is “theory”? What is “health informatics”? Why “interdisciplinary” theory?
2.Varying Perceptions of “Theory”
Our recurring experience in the production of this book has been the diversity of opinion about what exactly “theory” means. From our initial discussions, through the process of defining scope, commissioning chapters, inviting peer reviews and appraising author revisions we have repeatedly had to step back to reflect and question our own common understanding and that of our numerous contributors.
We found the Nilsen theory categories  a helpful anchor point to specify various types of theory (discussed further in the next chapter) and we cited the Nilsen paper in our brief to authors. Even then, we found that authors and reviewers did not always apply the categories uniformly or in line with our own editorial perceptions. We think that this tells us something about our field. While there will inevitably be some continuing academic pedantry and diverse schools of thought around particular concepts in the metaphysics of epistemology and methodology, we were surprised by the degree of divergence. Of course, there is also “theory” in the more generic sense of “a body of knowledge”, such as “social theory” or “economic theory”, but that is a different level of abstraction to our subject matter of specific theories (though not always a distinction that can be neatly maintained). Our experience is that health informatics is not a field that has a recognized common language to talk about its foundational ideas. Hence, we recall Kuhn’s seminal discussion of the progression of science and his reference to the “paradigm” of a discipline  and must question whether health informatics is yet a “mature” science. We return to this discussion in our final chapter.
There are “soft” and “hard” definitions of theory. To some extent these may reflect their respective disciplinary research tradition as primarily qualitative or quantitative in approach, but that is by no means a fixed rule and in any case is not unique to health informatics. The interdisciplinary nature of health informatics necessarily brings together people with varying cultural and practice norms, as we discuss further below. A “soft” definition might be that a theory comprises a hypothesis or a set of general principles within a defined conceptual model (a “determinant framework” in Nilsen’s terminology). A “hard” definition might be that a theory will make testable and quantitatively measurable predictions (a “classic theory” in Nilsen’s description). If we can accept a spectrum of theory types that incorporates both “soft” and “hard” definitions, then we have an approach that is broad enough to include everything from a theorised qualitative explanation (such as a “grounded theory”) through to equations that predict the relative clinical utility of particular laboratory tests. For this textbook, we have pragmatically adopted a flexible and inclusive view of theory. We asked chapter authors to work with the theory description: “abstract enough to permit generalization, but concrete enough to permit testing”. After Merton, we characterised these as “middle-range”  theories, not grand “theories of everything” but “special theories from which to derive hypotheses that can be empirically investigated”. By “testability” and “empirical investigation”, we mean simply that the given theory can be shown to have made a difference in some aspect of a health informatics lifecycle: design, validation, verification, implementation and evaluation.
3. Definitions of Health Informatics
There is still no single universally agreed definition of health informatics, but we now seem to have a converging set of ideas. Although the principal professional societies still use the older and narrower term “medical informatics” in their organizational names (e.g. EFMI, IMIA and the American Medical Informatics Association, AMIA), they each pro- mote more inclusive wording in their official publications. Early definitions of medical informatics were:
“the field that concerns itself with the cognitive, information processing, and communication tasks of medical practice, education, and research, including the information science and the technology to support these tasks” 
“the scientific discipline concerned with the systematic processing of data, information and knowledge in medicine and health care” 
Whereas IMIA prefers the phrase “biomedical and health informatics” (BMHI) , AMIA favours the term “biomedical informatics” (BMI), which it defines  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, motivated by efforts to improve human health”.
In this definition, BMI is the “scientific core” that is applied in the domains of bioinformatics and imaging informatics, health informatics (comprising clinical and public health informatics) and translational informatics. It has been argued that the more holistic term “biopsychosocial” would be a better adjective than “biomedical” , but in its World Health Organization definition the global term “health” subsumes all aspects of physical, mental and social well-being . Therefore, we use the term “health informatics” as a simple and inclusive descriptor to cover both BMHI and BMI. We find the AMIA definition particularly helpful in its articulation of the three “foundational domains” of health informatics: health science, information science, and social science and their various overlaps (see Figure 1, from ). We have used this model to structure the content of this textbook around the major subject areas.
4. Meaning and Importance of Interdisciplinarity in Health Informatics
Whatever label we choose to adopt for our field, it is unquestionably “interdisciplinary” as noted in the AMIA definition. Reflecting the three foundational domains, it is always the case that health informatics needs both healthcare and information science knowledge and skills. Increasingly often, the importance of the social sciences is also recognized. Interdisciplinary is defined as “contributing to or benefiting from two or more disciplines”  and is helpfully distinguished from “multidisciplinary” and “transdisciplinary” in the following summary :
Multidisciplinarity draws on knowledge from different disciplines but stays within their boundaries. Interdisciplinarity analyzes, synthesizes and harmonizes links between disciplines into a coordinated and coherent whole. Transdisciplinarity integrates the natural, social and health sciences in a humanities context, and transcends their traditional boundaries.
We do not suggest that health informatics cannot be transdisciplinary or multi-disciplinary at times. However, we do propose that interdisciplinarity is the term that best describes most good health informatics work today. We do not want to stay within disciplinary boundaries, but we do aspire to offer coherent synthesis across disciplines. Transdisciplinarity may sometimes be attained, but we suggest it is perhaps too high a goal and not necessarily a priority for most resource-limited health informatics work [18,19].
5. Structure of the Book
We set our overall learning aim for the textbook as: What theories have been applied in health informatics and what difference have they made? The specific objectives were:
To show where and how interdisciplinary theories have been applied in health informatics
To identify theory developed specifically within health informatics
To highlight where further work is necessary to develop theory-based approaches.
We undertook a consultative exercise with IMIA and EFMI Working Group members and potential chapter authors about relevant topics to feature, based around the three foundational domains of health science, information science and social science. When we invited authors to submit chapters, we proposed a standard structure to aid navigation and so that each chapter could be used as a standalone entity for educational use.
There is inevitably some debate about which theory belongs to which domain of knowledge, but we have ended up with sections that address only two of the three foundational domains in the AMIA model. The omission of theories from health sciences is not by design, as we discuss further in the final chapter. Section 1 deals with theories from information science and technology, such as general system theory, technology adoption models and Shannon’s information theory. Section 2 addresses theories from the social and psychological sciences such as distributed cognition, resilience theory and normalisation process theory. In Section 3, we offer two kinds of synthesis. Firstly, we consider the ambitious framework described by Greenhalgh and Abimbola that aims to integrate several theoretical approaches to the adoption and sustainability of health informatics interventions. Secondly, as editors we offer our own overview of theory within the overall health informatics body of knowledge and propose a research agenda. In this chapter we highlight where further work is necessary to develop theory-based approaches and mature the health informatics discipline.
6. Suggested Use in Teaching
We suggest that the specified learning objectives in each chapter might be used to construct a teaching plan for a given lecture or seminar. Students could be assigned, individually or in small groups, to produce reflective reports based upon directed reading of one or more of the chapter references. The questions for reflection at the end of the chapter might be featured in coursework or in interactive seminars. Students could be asked to find additional illustrations of the theory’s usage in health informatics, contrasting examples in other fields, or how alternative theories were applied in analogous scenarios. We encourage reflection on how the use (or non-use) of theory can explain relative success or failure in health informatics and on the maturity of theory in the field. Doctoral students may like to study the gaps or weaknesses in theory: where can new contributions be made?
The editors gratefully acknowledge all our colleagues who gave formative advice in the planning of this book and, of course, all our chapter authors and peer reviewers.
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