Complexity in Primary Care

Understanding its Value

Keiran Sweeney

• Publisher: CRC Press
• Year: 2017
• eBook ISBN: 9781315345116
• Edition: 1
• Topic: Medicine
• Subtopic: Medical Theory, Practice & Reference

The daily work of General Practitioners can seem at once simple and immensely complicated. The routine nature of the consultation appears on the surface to be straightforward, but carries within it myriad layers of meaning. The options for diagnosis and treatment within a single consultation, or when combined in the overall pattern of the day, can seem huge. A basic understanding of complexity theory can provide GPs with a way to face the more bewildering aspects of their job. This book provides a concise and clear introduction to complexity, tailored specifically for the primary care environment. GPs and their colleagues throughout primary care will find this book assists them in working more efficiently, more effectively and more enjoyably.

Chapter 1 Why bother? The need to understand explanatory models

A single consultation started the train of thought which has led to this book.

Some years ago, our practice nurse asked me to see Mrs B, an 85-year-old widow who as I recall, at the time of consultation, had been registered as a patient with me for about 15 years. I knew her well. Her husband, a pleasant chap who had been a builder, had died 5 years previously. Mrs B was pretty much estranged from her two grown-up sons, who were recurrent petty criminals, both serving prison sentences at the time of the consultation. Box 1.1 shows the conditions from which Mrs B suffered, and Box 1.2 shows her test results, which the nurse wanted me to review with her.

Box 1.1 Mrs B’S Comorbidity

Diabetes

Hypertension

Osteoarthritis

Macular degeneration

Halluxvalgus

Box 1.2 Mrs B’s test results

Glycosylated haemoglobin	9.7%
Blood pressure	180/96 mmHg
Total cholesterol	8.0 mmol/l
Body mass index	29 kg/m^2

Mrs B is not unusual, and my guess is that many people reading this book will know patients like her. When we met, at the practice nurse’s request, I rehearsed the abundant evidence supporting interventions to lower her blood pressure, to improve the control of her diabetes and to reduce her lipid levels. I remember even thinking where the reference for this all lay (with a resumé in Clinical Evidence). I confess to feeling just a shade confident as I explained the abnormalities and how we could ‘help’ to reduce her risk. After a few moments I stopped – resting my case, as a barrister might say.

Mrs B remained silent for a moment or two. Then she said, ‘Well, Jack’s dead and the boys have gone.’

This has remained one of the most privileged communications I have ever received. As she delivered her words I sensed that she was saying something very profound, although its full implications eluded me for a number of years. Certainly on the day, the consultation changed tack and, looking back, we muddled through with a compromise strategy, and agreed to review the situation later. As Mrs B left, I sensed that the balance of influence in the consultation had rested firmly with her.

Analysing the consultation: ‘Jack’s dead, and the boys have gone’

This is really the pivotal sentence, out of which many of the concerns explored in this book arose. At the simplest level, one can say that the consultation, at the point when Mrs B made this contribution, moved from being doctor centred to being patient centred. It moved, one could say, from the biomedical domain to the biographical domain, or from clinical, evidence-based medicine to a consultation predicated on narrative-based evidence. But the shift was profound. When the consultation moved from its biomedical phase, it shed its parameters of P-values, absolute risk and numbers needed to treat. These were replaced by the parameters of the biographical phase of the consultation – led by Mrs B. Here despair, hopelessness, regret, guilt perhaps, and defeat were the parameters. Physical parameters had been replaced by metaphysical ones – two intellectual worlds seemed to have collided.

It is clear that, when Mrs B offered her contribution, the consultation took off in another direction. Up until that point, a fairly straightforward consultation was proceeding, drawing on scientific evidence gleaned from good clinical trials, many of them randomised and controlled, in the great tradition of scientific medicine. The remainder of the consultation, led by Mrs B, had nothing to do with that way of thinking, and arose from her lived experience. Yet in that context Mrs B’s narrative evidence had more impact on the outcome of the interaction between Mrs B and myself than the clinical evidence-based observations with which I led the consultation. There were, one could argue, two ways of explaining things which were competing for influence – two explanatory models which at first sight did not seem to overlap much. At a deeper level, there were two types of knowledge jostling for influence. Two different ways of viewing and making sense of the world were at stake. But what constituted these three levels of understanding? This is what this book tries to explore.

Explanatory models, types of knowledge and world-views

Why bother? Why interrogate the explanatory model in medicine? An explanatory model provides a framework from which one can explore the receptive context within which professionals and patients conduct their conversations during consultations. Its propositions create boundaries within which these conversations can take place and also, in so doing, create constraints. For example, the postulates of homeopathy do not conventionally feature in these conversations, because they are not supported by the paradigm within which the current explanatory model in medicine is located. However, to describe medicine’s explanatory model adequately, one needs to consider the world-view upon which it is based, and the type of knowledge constructed to populate and make sense of that world-view. Let me try to clarify succinctly the proposition that I want to explore.

The nature of an explanatory model, I argue, betrays a predilection for a certain type of knowledge – the collection of ‘facts’ which populate one’s explanatory model. Medicine’s conventional explanatory model is based on the scientific tradition. It populates that model with ‘facts’ arising from that tradition, in the shape of the results of scientific experiments, among which, for clinical medicine, the randomised controlled trial stands at the pinnacle. That preference, for one type of model over another, expresses the world-view that underpins the explanatory model. For medicine, I propose that the basis of the world-view underpinning that model is scientific positivism.

Before going any further, let me clarify some terminology and some initial standpoints.

I am using the term ‘explanatory model’ in a pretty straightforward, dictionary-based way. Thus I am taking ‘model’ to mean a set of postulates which serves to represent an entity that cannot be observed, and ‘explanation’ (similarly derived) to indicate the process of arriving at a mutual understanding or reconciliation (Kirkpatrick, 1994). In the context of this book, then, an explanatory model in medicine consists of a series of sense-making postulates that are located within the contemporary medical paradigm (using that term in its original Kuhnsian sense) (Kuhn, 1970). I am arguing from the viewpoint that the contemporary explanatory model in medicine is dominated by science – that is, science occupies a hegemonic role in that model. This is what I want to explore and reconsider.

In this book I will use the term ‘science’ as Chalmers (1982) does, in what he calls the ‘widely held common-sense view of science.’ Chalmers states:

Scientific knowledge is proven knowledge. Scientific theories are derived in some rigorous way from the facts of experience acquired by observation and experiment. Science is based upon what we can hear, see and touch. Science is objective. Scientific knowledge is reliable knowledge because it is objectively proven knowledge.

This is similar to the way in which another philosopher of science, James Brown, uses the term ‘normal science.’ ‘Most science,’ Brown says, ‘is normal science. It is what is done by all scientists who agree on the basics, that is what the world is made of, how things interact with it: normal science is a puzzle-solving activity’ (Brown, 2001). Scientists, Brown goes on to say, work within a particular paradigm, or an accepted set of beliefs within a background of unquestioned theory. The scientific paradigm (Kuhn, 1970) involves some associative practices, including a basic agreement about ontology (what the world is made of) and epistemology (the nature of knowledge, its possible scope and general basis).

I can now put the central proposition of this book in slightly more formal, philosophical language. An explanatory model, one can say, is an expression of a particular epistemological standpoint, and this in turn reflects the ontological view held by the person who is supporting the model itself. An explanatory model is the product, if you like, of the interaction between the ontological view and the epistemological framework.

Science, in the sense that I have defined it above, accepts an objective singular rational reality that can be measured and verified. This associates science with positivism, first described by Comte (Honderich, 1995), by virtue of its acceptance of empiricism (all knowledge is based on sensory experience) and verificationism (if a statement is to be meaningful it must be empirically testable) (Brown, 2001). It also shares with positivism a sense of inexorability – of an inevitable progress inherent within scientific pursuit. And it shares with positivism a notion of hierarchy of knowledge, with knowledge derived from physics having the highest value. Scientific positivism is central to the ontological view held, usually more tacitly than explicitly, by those who support medicine’s conventional explanatory model.

Thus deined, two characteristics are fundamental to science, namely linearity and reductionism. Linearity assumes a regular, proportionate and stable relationship between effect and antecedent cause. Reductionism refers to an approach to understanding phenomena by reducing the whole to its constituent parts, and assuming that the whole is the sum of its constituent parts. It is really important to hold these attributes of the scientific approach in mind, as I shall challenge medicine’s explanatory model precisely at this level, by exploring another explanatory model (based on complexity) which emphasises the importance of interaction between parts, rather than reduction to parts.

In addition to the strict deinitional issues, which it is important to get clear at the outset, it is also important to grasp the implications of this description of medicine’s current explanatory model. By implication, ‘science’ as understood in this way is regarded as a purer form of understanding, where fact is considered more important than value and where explanation is conined to the expression of measurable and verifiable correlations between phenomena (Ruse, 1995). This is justified, I argue, because of the way in which the conception of science is embodied by the latest expression of the explanatory model in medicine, namely evidence-based medicine. Here evidence amounts to knowledge distilled from observation and experiment. These observations sit in a well-recognised hierarchy, with randomised controlled trials at the top, followed by partially or uncontrolled trials, with expert opinion very much at the lowest position in the league. The whole approach to evidence-based medicine, with its ive steps – from deining the patient’s problem to auditing one’s performance in solving it – represents the puzzle-solving approach of normal science. And it makes clear assumptions, albeit tacitly, about the world and the practitioners who make sense of it, using the epistemological principles laid down by its most revered exponent, David Sackett.

In Clinical Epidemiology: a Basic Science for Clinical Medicine, Sackett and colleagues state ‘the assumption is that medicine is rational and so are you’ (Sackett et al., 1985). Evidence-based medicine depicts a world that is rational and objective, and which can be measured empirically. In addition, it depicts a world in which experiments can be performed in closed systems where it is assumed that the researcher can stand outside the system, apart from it, manipulating one or two key variables in order to measure the outcome precisely. If in the course of this book this model is criticised, it is not to diminish the extent to which developments in science, which lies at the model’s heart, have beneited mankind. Rather it is to place the role of science in the contemporary explanatory model in a wider context – a context in which, I shall argue, several diferent explanatory models are deployed, exchanged and accepted.

Now this use of the term ‘science’ is open to criticism. As it stands, it is strongly associated with the principle of induction, the process of generalising from a series of particulars. That argument is not logically justifiable (Chalmers, 1982; Honderich, 1995; Brown, 2001), nor are the issues of authority of scientific knowledge or Popper’s falsificationism (Popper, 1963) accounted for. Nor, finally, is the issue of all observations being theory driven given proper accommodation (Polanyi, 1958). But the use of the term ‘science’ in this way is justified pragmatically. It accords with Chalmer’s common-sense view of science (Chalmers, 1982) and Brown’s definition of normal science (Brown, 2001), which are in themselves compatible with the way in which science is conceptualised in the current explanatory model in medicine (Sackett et al., 1985).

Facing up to the evidence: chinks in the armour of the gold standard

Although ‘O’-level Latin was still a prerequisite for entry to my medical school in Glasgow in the early 1970s, the training there was resolutely scientific. Biomedical science was riding the crest of an intellectual wave (from which, as we shall see, it was to fall), doctors were held in indisputably high regard, and advances in technology, especially in the field of molecular sciences, held out the promise of dramatic new interventions which would conquer the most common fatal diseases affecting Western societies. It would be nearly two decades before the breathtaking manifesto of the Evidence-Based Medicine Working Group (1992) exhorted us to concentrate on assessing precisely what kind of (scientific) evidence we had available to address our clinical questions. To have questioned the medical model at that point would have appeared heretical (and stupid).

However, some tried. Ivan Illich introduced the notion of iatrogenesis, the process by which medical care itself could cause illness (Illich, 1975). Cartwright and Anderson (1981) published their seminal study of general practice patients not long afterwards, giving us the first hint that perhaps they were not so unthinking and passive as doctors assumed them to be. However, there was no real attempt to disarticulate the medical model, and the hegemony of science remained virtually impregnable up until the turn of the century.

I confess, along (I imagine) with many other healthcare professionals, to holding a fairly simplistic view of evidence during that time. The whole thing was rather a mystery really, but one took comfort from the fact that a randomised controlled trial was the best one could get, and one could pretty well bet one’s life on the truth of their outcomes. The fact that many patients did, only to experience an unsatisfactory outcome, was one of the trends that led some practitioners to reappraise the nature of such trials, and to examine just how they were set up. The practice to which I belonged in the last two decades of the twentieth century did precisely that in relation to what appeared, to the untutored eye, to be a powerful body of evidence supporting the anticoagulation of patients who were suffering from atrial fibrillation. Around the time when this evidence was published, I had responsibility in my general practice partnership for clinical policies in this area. The publication of this evidence, authoritative reviews of it and policy d...

Table of contents

• Cover Page
• Title Page
• Copyright
• Contents
• Foreword
• About the author
• Introduction
• 1 Why bother? The need to understand explanatory models
• 2 The biomedical tradition: why doctors think like doctors
• 3 Evidence-based medicine: the contemporary manifestation of the explanatory model in medicine
• 4 The naturalistic tradition: historical overview of the epistemological origins of qualitative research
• 5 The non-linear tradition: historical development of complexity
• 6 Developing an understanding of chaos and complexity: implications and examples
• 7 Using Complexity Principles in Healthcare Research: Examples of Data Analysis Using Complexity Principles
• 8 Complexity and Medical Practice: Prospects for the Future
• Appendix