Complexity and Healthcare Organization

A View from the Street

David Kernick

• Publisher: CRC Press
• Year: 2018
• ISBN:
  9781315344867
• Edition: 1
• Topic: Medicine
• Subtopic: Public Health, Administration & Care

Statistics and evidence-based medicine are assessed in most postgraduate and undergraduate medical examinations and degrees in health sciences. All clinicians have to acquire skills in this area. This book aims to provide a brief overview of basic medical statistics and the numerical aspects of evidence-based medicine to give realistic worked examples to illustrate the interpretation of studies relevant to clinical practice and to allow examination practice. It aims to cover all major topics covered in the undergraduate and postgraduate examinations. Each chapter begins with an overview and summary of the main points followed by worked examples and exercises with full answers. It will be ideal for all postgraduate medical examination candidates. Other clincians and undergraduate students in medicine and health sciences will also find it useful.

Section 1

Getting to grips with the basics

Introduction

We need to make sense of the world and act. Chapter 1 explores how we do this using models and metaphors; and how these are packaged into disciplinary frameworks known as paradigms that in turn can exert a hold on how we view the world.

The classical or Newtonian approach has been the dominant paradigm within Western thought and traces its origins to the time of the enlightenment. It proceeds on the confident assumption that there are physical laws based on simple relationships between cause and effect that govern the behaviour of all natural systems. Individual knowledge of all the parts of a system add up to an understanding of the system as a whole and given its initial conditions, any future state can be predicted. When this approach is not applicable, systems are seen as exhibiting unpredictable random behaviour that can be described by statistical methods. However, neither of these two ideals occurs in the natural world. Most systems have features of both randomness and order and it is here that the study of chaos and complexity sits.

Despite considerable effort, ideas derived from classical science have had limited success when applied to human organizations. Structures like the NHS do not behave predictably and seem reluctant to be engineered towards desired objectives. Neither do they behave in a random manner. In many ways, organizations seem to have a life of their own and theories derived from chaos and complexity may offer us some useful insights into aspects of organizational behaviour.

However, these new theories have been compared to ‘theological concepts’ -lots of people talk about them but no one knows what they really mean. A major difficulty is that complexity science draws upon developments across a diverse number of disciplines, each with their own interpretation. Academics jostle to champion their particular perspectives and, inevitably, definitions and, conceptual themes remain contested.

Of particular contention is the relationship between chaos and complexity theory. Most books on complexity do not include a discussion of chaos theory. It is seen either as unhelpful or a sub-discipline of complexity theory. I find it a useful starting point as it helps to understand the concept of non-linearity, which for me is the essential feature of complex systems. Although many experts would not agree, I find it helpful from an organizational perspective to see chaos theory as the quantitative study of dynamic non-linear systems (systems that change with time, are dependent on what has gone before and have an uncertain relationship between cause and effect). Complexity theory is the qualitative study, drawing upon metaphors derived from chaos theory.

Chapter 2 explores chaos theory and the concept of non-linearity. The fainthearted can move straight to Chapter 3 where we explore the fundamental concepts of complexity theory that are applied throughout this book.

CHAPTER 1

Models, metaphors and paradigms: making sense of the world and the road to complexity

David Kernick

Those who take for their standard anyone but nature weary themselves in vain.

(Leonardo da Vinci)

This chapter explores how we use models and metaphors to make sense of the world and act. It sets complexity in the evolving story of scientific thinking, from modern science, that traces its origins to the time of the enlightenment, through post-modernism to complexity science.

Key points

• Models create reality around bundles of related assumptions that help us make sense of the world and act.
• The use of metaphor offers insights that can be lost when we construct models.
• Models and metaphors are consolidated into disciplinary frameworks known as paradigms. Paradigms can exert a deep hold on how we view the world.
• Despite many successes, the paradigm of modern science has been limited in its ability to predict and control the behaviour of human organizations.
• Complexity science is the study of dynamic, non-linear systems. The model is a network of co-evolving elements where changes in one element can change the context for all other elements. This has profound implications for how we view organizations.
• The metaphor for understanding organizations changes from machine to ecosystem.
• Complexity aims to complement modern science, not overturn it.

Making sense of the world using models

As humans we have a recollection of the past and an anticipation of the future. Sense making allows us to make the connection and act. To do so we simplify the world by constructing models, creating reality around ‘bundles of related assumptions’. We look for patterns in our experience while classifying them into categories. We also look for patterns in the interactions between categories, searching for relationships and regularities.¹ Philosophers struggle to reconcile what’s out there with our internal representations without agreement - models are always approximations of the real world. Models help us to address two questions:

• the descriptive element - what is happening, what will happen?
• the prescriptive element - how can we make what we want happen?

Our models powerfully influence how evidence is collected, analysed and understood. At an individual level, we create tacit models or knowledge by reflecting on our experience and comparing it with our expectations, continually rethinking our ideas on causation and intervention. We operate according to these internal mental models that are often below the level of consciousness. They represent the rules for how we order and categorize information and how we respond to our environment. They form our knowledge - how we process and act upon information presented to us. For example, practitioners usually know more than they can say. They reveal a capacity for reflection on intuitive knowledge in the midst of action and use this tacit capacity to cope with the unique uncertain and conflicting situations of practice.² However, when models are implicit, their potential to confuse or obscure new insights can go unnoticed.

Models can also be explicit and shared. Knowledge management is currently an important concept in organizational theory and a recurring theme in this book within the context of ‘learning organizations’. The contention is that individual knowledge is largely unknown to others and therefore wasted. The aim is to capture and share this knowledge for the benefit of the whole organization.

Table 1.1 shows some differences between tacit and explicit models or knowledge.

Table 1.1 Some characteristics of tacit and explicit models or knowledge

In organizations, our shared models are constructed and maintained in symbols that we offer each other. These symbols can be artifactual (for example, organization strategies, documents, uniforms) or communicative, mainly through the use of language. We also design spaces for the creation of our ideas. These symbols and the design of spaces for the creation and interchange of our symbols not only allow us to explore our organizational possibilities but also to place boundaries on them. However, we need to be aware that different organizational cultures may have differing symbolic interpretations. Within organizational contexts, we might also anticipate dysfunctional consequences when there is a dissonance between future expectations derived from our own sense-making models and expectations imposed upon us within a bureaucratic structure derived from different models.

Models need to have external consistency (i.e. they have predictive value) and internal consistency (i.e. they are compatible with other models). Studies in stroke victims suggest that model making is reflected in brain architecture. The left hemisphere creates a model that it maintains at all costs whereas the right hemisphere detects anomalies and forces the left hemisphere to revise it, maintaining internal consistency.³

One way we test and share our models is through the use of narrative or storytelling.* We create our realities with the same motives, themes and structure as fiction. Our culture binds us by a set of connecting stories.⁴ Perhaps through storytelling we modulate the creative tension between our right and left brains as our models are formed and reformed.

Making sense of the world using metaphor

Because models simplify reality they often lose intuitive insights and the use of metaphor helps to retain this information. Metaphor brings together two areas of experience, treating one as if it had the features of the other applied to it. Metaphor can organize our patterns of thinking as we both reflect and interact with each other, helping us to grasp reality in clearer terms. It offers a framework to think and act differently. Aristotle wrote: ‘ordinary words convey what we know already; it is from metaphor that we can get hold of something fresh.’

For example, sense making has been described in terms of the metaphor of cartography.⁵ There is no one best map of any particular terrain - the ground is not already mapped. What sort of map is made depends on where cartographers look, how they look and what tools they have for representation. Map making tends to be social - we construct our maps through our relationships with others through the use of symbolic communication.

How we understand and behave in our organizations is reflected in the metaphors we use to describe them. For example, Morgan⁶ offers a range of metaphorical interpretations ranging from machines to organisms, from brains to psychic prisons. However, metaphor has the potential to mislead if applied inappropriately as the following example demonstrates.

In 1998, in response to disappointing waiting-list statistics, the Secretary of State for Health, Frank Dobson, stated: ‘I said...

Table of contents

• Cover
• Title Page
• Copyright Page
• Contents
• Foreword
• About this book
• List of Contributors
• Prologue
• Section 1 Getting to grips with the basics
• Section 2 The spectrum of how we think about organizations
• Section 3 Complexity perspectives on healthcare organization
• Section 4 Facilitating emergence in healthcare organizations
• Section 5 Going on together in organizations
• Section 6 Going on together in organizations
• Section 7 From theory to action
• Epilogue: being vaguely right rather than precisely wrong
• Appendix 1 Some complexity metaphors
• Appendix 2 Cynefin domains
• Glossary of complexity terms
• Useful resources
• Index