Ebook: Applied Interdisciplinary Theory in Health Informatics

Philip J. Scott^a,1, Nicolette F. de Keizer^b and Andrew Georgiou^c

^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: philip.scott@port.ac.uk

1. Purpose

Kurt Lewin, the pioneer of social psychology, famously said that ’there is nothing more practical than a good theory’ [1]. 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 [2]. 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 [3]. 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 [4] by emphasising theory-informed work that aims to “enrich our … understanding of this uniquely complex field” [5]. 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 [6] 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 [7] 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” [8] 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” [9]

• “the scientific discipline concerned with the systematic processing of data, information and knowledge in medicine and health care” [10]

Whereas IMIA prefers the phrase “biomedical and health informatics” (BMHI) [11], AMIA favours the term “biomedical informatics” (BMI), which it defines [12] 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” [13], but in its World Health Organization definition the global term “health” subsumes all aspects of physical, mental and social well-being [14]. 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 [15]). 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” [16] and is helpfully distinguished from “multidisciplinary” and “transdisciplinary” in the following summary [17]:

• 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?

Acknowledgements

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.

References

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This chapter introduces the idea of theories in health informatics, defines what we mean by theory and distinguishes theories from models, frameworks and predictive principles. After explaining why theories and predictive principles are needed to help us professionalize our discipline, the chapter offers five criteria for a successful predictive principle, discusses how to evaluate predictive principles and theories and links this with the emerging field of evidence-based health informatics. The chapter concludes with three actions needed to move the discipline of theory-based health informatics forward.

General System Theory was proposed in the post-war period as a unifying framework for interdisciplinary science based on the idea that systems have a set of similar properties and characteristics regardless of discipline. General System Theory laid the foundations for talking about things in terms of systems, many of its terms are now embedded in everyday language and it underpins a broad range of systems approaches and systems thinking. This chapter will describe the key elements of the original General System Theory (GST) including control, feedback, emergence, holism and the notion of a hierarchy of systems within systems. It will review the origin, content and foundational role of systems theory in biology, medicine, computer science, organizational theory and its central contribution to health informatics. In recent years, healthcare organizations have been encouraged to see themselves within the context of learning health systems (LHS) and to use emerging big data analytics techniques such as process mining to develop better, integrated and personalized pathways of care for patients. We use GST to reflect on these emerging approaches through a discussion and case study on recent work in urgent and emergency care. Our aim is to trace the influence of GST through emerging LHS ideas and use the framework of GST to reflect on the opportunities and limitations of our process mining approach. In particular, we will reflect on how GST can explain successes and failure in the application of process mining to care pathways and the challenges and opportunities ahead.

Information theory has gained application in a wide range of disciplines, including statistical inference, natural language processing, cryptography and molecular biology. However, its usage is less pronounced in medical science. In this chapter, we illustrate a number of approaches that have been taken to applying concepts from information theory to enhance medical decision making. We start with an introduction to information theory itself, and the foundational concepts of information content and entropy. We then illustrate how relative entropy can be used to identify the most informative test at a particular stage in a diagnosis. In the case of a binary outcome from a test, Shannon entropy can be used to identify the range of values of test results over which that test provides useful information about the patient’s state. This, of course, is not the only method that is available, but it can provide an easily interpretable visualization. The chapter then moves on to introduce the more advanced concepts of conditional entropy and mutual information and shows how these can be used to prioritise and identify redundancies in clinical tests. Finally, we discuss the experience gained so far and conclude that there is value in providing an informed foundation for the broad application of information theory to medical decision making.

Information value chain theory provides a straightforward approach to information system evaluation and design. It first separates the different benefits and costs that might be associated with the use of a given information technology at different stages along a value chain stretching from user interaction to real world outcome. Next, using classical decision theoretic measures such as probabilities and utilities, the resulting value chain can be used to create a profile for a particular technology or technology bundle. Value chain analysis helps focus on the reasons for system implementation success or failure. It also assists in making comparative assessments amongst different solutions, to understand which might be best suited for different clinical contexts.

mHealth can offer great potential for the self-management of health conditions and facilitating health services. It is therefore imperative that the design of mHealth systems afford optimum efficacy and effectiveness. Involving end users in collaborative decision making is an essential aspect of increasing acceptance of the treatment intervention. Involving users in the design and evaluation of mHealth systems helps to enable a better understanding of the complexity of user needs and how to incorporate this information effectively into the design process. This chapter discusses how Activity Theory can help to provide a theoretical lens for a User Centred Design framework in the design of mHealth systems. A general overview of Activity Theory and User Centred Design are provided, followed by their application in mHealth. Two use cases are provided that demonstrate how Activity Theory has helped provide a broader contextual analysis to a User Centred iterative approach to system design and evaluation.

Both the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) aim at understanding better why users accept or reject a given technology, and how user acceptance can be improved through technology design. Two case studies are presented where TAM and UTAUT were successfully used in a health care setting to predict technology adoption. Both models have found popularity in health care. However, recent reviews show that TAM and UTAUT failed to provide stable predictive capabilities for acceptance and use of technologies in health care. Reasons for this may be the specific context of health care, where not only the technology, but also socio-organizational and cultural factors influence technology acceptance.

Distributed cognition theory posits that our cognitive tasks are so tightly coupled to the environment that cognition extends into the environment, beyond the skin and the skull. It uses cognitive concepts to describe information processing across external representations, social networks and across different periods of time. Distributed cognition lends itself to exploring how people interact with technology in the workplace, issues to do with communication and coordination, how people’s thinking extends into the environment and sociotechnical system architecture and performance more broadly. We provide an overview of early work that established distributed cognition theory, describe more recent work that facilitates its application, and outline how this theory has been used in health informatics. We present two use cases to show how distributed cognition can be used at the formative and summative stages of a project life cycle. In both cases, key determinants that influence performance of the sociotechnical system and/or the technology are identified. We argue that distributed cognition theory can have descriptive, rhetorical, inferential and application power. For evidence-based health informatics it can lead to design changes and hypotheses that can be tested.

This chapter introduces Actor-Network Theory, a sociotechnical approach to studying health information technology implementation. The chapter is intended as a pragmatic introduction to the field, acknowledging that there are many contested features of an Actor-Network Theory informed methodology. Nevertheless, the approach can be usefully drawn on to help to focus data collection and sampling. A case study describing the application of Actor-Network Theory to study the “failed” implementation of national electronic health records in England as part of a national “top-down” implementation program illustrates the main tenets of the approach and provides concrete examples of how Actor-Network Theory may be applied. In doing so, this chapter offers a reflexive account of how Actor-Network Theory has provided a nuanced analysis of how the implementation of national electronic health records affected different stakeholders, organizations and technology.

High reliability organisations operate safely in situations of high risk by organising for collective mindfulness. They do so through five ongoing processes geared towards anticipating, containing, and making sense of the unexpected. The five processes are: preoccupation with failure, reluctance to simplify interpretations, sensitivity to operations, commitment to resilience, and deference to expertise. The theory of collective mindfulness builds on Hutchins’s theory of distributed cognition (the ‘collective mind’ of ship navigation teams) and on Langer’s theory of mindfulness about individuals’ interpreting information in context. However, in the theory of collective mindfulness, attention is paid not to individual cognition or decision making, but to collective processes of sensemaking emerging from individuals’ interactions in dealing with an equivocal environment. In health informatics, the theory of collective mindfulness can be used to explain health information technology (IT) development and implementation, across its life cycle, and inform guidance towards mindful management of IT projects. For example, applied to a case of electronic health record implementation in a hospital context, the theory explains how mindful management of the sense-making challenges of post-roll out adaptation processes contributes to a ‘successful’ IT project. Further, the theory challenges a static and linear understanding of success (or failure) of health IT initiatives, supporting instead an argument for outcomes – be it reliability and safety, or IT project success – as collective, complex and dynamic achievements of mindful organising practices.

The accumulation of medical knowledge, technology and expertise has provided people with more and more options to improve their health and increase longevity. However, healthcare options typically come with benefits as well as harms and often involve important and complex, high-stakes trade-offs. The ideal of Shared Decision Making (SDM), where a healthcare provider and a patient exchange information, bring in their respective professional and existential expertise and consider the options in light of what matters most from the patient’s perspective, is a paradigm that is increasingly viewed as a gold standard for high quality care nowadays. eHealth provides ample opportunities to foster personal health choices and SDM through digital information exchange and personal values clarification support. The boosting framework attempts to describe how to foster people’s competences to make choices. Its vision is to equip individuals with competences, for instance improved risk literacy, to empower them to make well-informed choices when facing a difficult choice, such as decisions about health issues. Application of the boosting framework to personal health choices and the SDM process unveils new and promising horizons for future research and could inform the design and evaluation of health informatics interventions such as decision support systems.

Inadequate communication is a factor in suboptimal junior doctor management of deteriorating ward patients. Junior doctors’ information and communication technology (ICT) systems are not the sole cause or cure for this. However, junior doctors are already dissatisfied with existing technologies for general hospital communication. The Deterioration Communication Management Theory (DCMT) provides a means to approach these issues by uniting two themes: 1) factors affecting the properties of ICT used to communicate to junior doctors; and 2) factors affecting junior doctor interpretation of communication about deteriorating hospital patients. ICT factors include how the combination of physical devices and mode of usage affect user perception of system reliability and efficiency. Junior doctors interpret clinician communication about patient deterioration in terms of risk, which is affected by their contextual responsibility and experience. Perceived risk and contextual experience in turn affects their communication efficiency. Combining these themes gives more options to explain junior doctor communication in this clinical context and to design ICT systems to improve it.

This chapter presents an overview of Resilient Health Care (RHC), introducing two aspects of RHC that are important for designing sustainable digital health systems and for considering implementation outcomes: (1) understanding how normal variation in everyday work can affect implementation of digital health interventions, and (2) the role of information systems in coping with unexpected events. The importance of considering how variation in everyday work can lead to wanted and unwanted outcomes when designing information systems is illustrated through a case study of implementation of a telehealth intervention. We examine how normal variation in everyday work can lead to both safety and error, and discuss how consideration of system resilience when designing and implementing health informatics applications can contribute to improving safety for patients in the future. How health information systems can assist organisations in coping with the unexpected is illustrated through a second case study, of a thunderstorm asthma event in Melbourne, Australia. We briefly present the thunderstorm asthma case, and discuss the role of healthcare informatics in preparing for future unexpected events affecting population health.

The rising use of the Internet and information technology has made computerized interventions an attractive channel for providing advice and support for behaviour change. Health behaviour and behaviour change theories are a family of theories which aim to explain the mechanisms by which human behaviours change and use that knowledge to promote change. Among the best-known of these theories are the Social Learning and Social Cognitive theories, the Health Belief Model, the Theory of Reasoned Action and its successors the Theory of Planned Behaviour and the Reasoned Action Approach, and the Transtheoretical model. We discuss three examples of how behaviour change theories have been applied in computer-based interventions: a system to aid users to quit smoking, a decision aid for choice of breast cancer therapy, and an internet-based exercise program for reducing cardiovascular risk. We also discuss misapplication of theory, and reflect on how these theories can best be used. Behaviour change theory can be applied in health informatics interventions in several ways; for example, to select participants for a particular intervention, to shape the content of the intervention to effectively influence behaviour, or to tailor content to individual needs. Application of these theories to provide personalized advice (“decision support”) is a young but promising area of research, and could inform other decision support interventions, including those that provide support for clinicians.

Control theory is about the processes underlying the behaviour of self-regulating agents. It proposes that behaviour is regulated by a negative feedback loop, in which the agent compares the perception of its current state against a goal state and will strive to reduce perceived discrepancies by modifying its behaviour. Although studies in health informatics often do not report the use of this theory, the principle of a negative feedback loop underlies many applications in the field. This chapter describes how control theory fits within health informatics, discussing its role in the development and assessment of audit and feedback interventions in healthcare. Control theory has been used to synthesise evidence of audit and feedback, and to design and evaluate interventions to improve the quality of blood transfusion practice, cardiac rehabilitation, and intensive care. This has driven progress in our understanding of the underlying mechanisms of audit and feedback for improving health care, and has helped to design better interventions.

Successful implementation of health informatics systems depends not only on efficient performance of intended tasks, but also integration into existing working relationships and environments. Implementation is an understudied area in health informatics research, and relevant empirical evidence is often absent from strategic decision making. Implementation theories such as Normalization Process Theory (NPT) can help address this gap by providing explanations for relevant phenomena, proposing important research questions, and framing collection and analysis of data. NPT identifies, characterizes, and explains mechanisms that have been empirically demonstrated to affect implementation processes and outcomes. These explanations are generalizable and facilitate comparative investigations. The first section of this chapter introduces the four main constructs of NPT (coherence, cognitive participation, collective action, and reflexive monitoring) and their constituent components. Each component is discussed with reference to a real-world example, and relationships between the four constructs are explored. The second section explores how NPT has been applied in both prospective planning of interventions and their evaluation, as well as retrospective exploration of factors promoting or inhibiting successful implementation. We examine two examples from published literature: firstly, prospective planning of an evaluation study on implementation of a digital health intervention for Type-2 diabetes; and secondly an evaluation of implementation of a new electronic preoperative information system within a surgical pre-assessment clinic. The chapter concludes with reflections on some limitations of NPT as a theoretical framework.

Technologies are often viewed as the route to better, safer and more efficient care, but technology projects rarely deliver all the benefits expected of them. Based on a literature review and empirical case studies, we developed a framework (NASSS) for studying the non-adoption, abandonment and challenges to scale-up, spread and sustainability of technology-supported change efforts in health and social care. Such projects meet problems usually because they are too complex – and because the complexity is sub-optimally handled. NASSS consists of six domains – the illness or condition, the technology, the value proposition, the individuals intended to adopt the technology, the organisation(s) and the wider system – along with a seventh domain that considers how all these evolve over time. The NASSS framework incorporates a number of other theories and analytic approaches described elsewhere in this book. It is not intended to offer a predictive or formulaic solution to technology adoption. Rather, NASSS should be used to generate a rich and situated narrative of the multiple influences on a complex project; to identify parts of the project where complexity might be reduced; and to consider how individuals and organisations might be supported to handle the remaining complexities better.

In this chapter, we reflect on the aim and objectives of the textbook and address known gaps in our theory coverage. We reinforce the importance of theory in health informatics and review the varying disciplinary origins of the theories considered in the book. We discuss the question of what makes a good theory and how to know which one is relevant for a given study. We recognize the limitations of the body of theory that we have presented and suggest what might be regarded as “native” theory that is original to health informatics. Finally, we propose topics to form a research agenda for theory in health informatics.