Economics and resourcing of complex healthcare ...

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Australian Health Review http://dx.doi.org/10.1071/AH11041

Review

Economics and resourcing of complex healthcare systems Abdolvahab Baghbanian1,2,3,5 BSc, MSc, PhD, Senior Lecturer Ghazal Torkfar4 BS, MPH, PhD Candidate 1

Health Promotion Research Centre and Faculty of Health, Zahedan University of Medical Sciences, Mashahir Square, Zahedan, 98169-13396, Iran. 2 Faculty of Health Sciences, Cumberland Campus, University of Sydney, PO Box 170, Lidcombe, NSW 2141, Australia. 3 Menzies Centre for Health Policy, Victor Coppleson Building, University of Sydney, Sydney, NSW 2006, Australia. 4 School of Public Health and Menzies Centre for Health Policy, University of Sydney, Sydney, NSW 2006, Australia. Email: [email protected] 5 Corresponding author. Email: [email protected]

Abstract. With rapid increases in healthcare spending over recent years, health economic evaluation might be thought to be increasing in importance to decision-makers. Such evaluations are designed to inform the efficient management of healthcare resources. However, research into health policy decisions often report, at best, moderate use of economic evaluation information, especially at the local level of administration. Little attention seems to have been given to the question of why economic evaluations have been underused and why they may yield different results in different contexts. There are many barriers to applying economic evaluations in situations which combine complexity with uncertainty. These barriers call for innovative and creative responses to economic evaluation of healthcare interventions. One response is to view economic evaluations in the context of complex adaptive systems theory. Such theory offers a conceptual framework that takes into account contextual factors, multiple input and output, multiple perspectives and uncertainty involved in healthcare interventions. This article illustrates how complexity theory can enrich and broaden policy-makers’ understanding of why economic evaluations have not always been as successful as health economists would have hoped. It argues for health economists to emphasise contextual knowledge and relativist understanding of decision contexts rather than seeking more technically sound evidence-based reviews including economic evaluations. What is known about the topic? Although it is widely acknowledged that economic evaluation, as presently constituted, is underused in its influence on allocation decisions in healthcare, previous research often ignores the ways multiple factors influence economic evaluations at several inter-related levels of the healthcare systems. Our topic is novel in its application of complexity theory to economic evaluation and attempts to show how allocation decisions reflect concern for economic efficiency in complex situations. What does this paper add? This paper shows that, although there has been a dense body of literature on the theoretical use of economic evaluations in allocation decisions around the world, evidence of successful uptake is limited. The paper shows that current economic evaluation practices oversimplify complex allocation decisions. They often ignore, marginalise or devalue the context and modifying conceptual factors that underlie explanation, meaning, sense making and values of real world contingencies. They insufficiently take notice of contextual factors and relationships in multi-objective, multistakeholder resource management situations. One approach is to view economic evaluations through the lens of complex adaptive systems theory, which rarely has been informed by current research. This study is innovative in its approach to using complex adaptive systems theory to investigate economic evaluation in a complex environment. The paper describes a vital step for greater acceptance of economic evaluation through understanding the underlying features of complex adaptive systems theory. It supports a shift away from equilibrium and reductionist thinking into the complex behaviour of natural and social systems. It argues for health economists to emphasise contextual knowledge and relativist understanding of the decision contexts, rather than seeking more and more ‘technically sound’ economic evaluations. What are the implications for practitioners? This study should be of interest to a broad readership, including those interested in health economics, public health policy, healthcare delivery, healthcare resource allocation and decision-making. The paper creates a dialogue about how researchers can better respond to the needs of those making resource allocation decisions in healthcare. Received 4 May 2011, accepted 23 February 2012, published online 10 September 2012

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Introduction Health policy decision-making is becoming increasingly complex and interdependent. There has been increasing calls for research-informed decision-making. Greater effort is being made to use health research findings more generally.1,2 The discipline of economics, for instance, has developed and refined methods to support decision-making, and members of the sub-discipline of health economics have called for increased use of evidence from economic evaluations to inform rational decision-making.3 Yet, despite their espoused value and methodological developments in pharmaceuticals and advanced technologies in Australia, Canada and Wales, existing studies of the role of systematic evidence-based reviews in healthcare decision-making show low or moderate impact of the procedures in practice.3–9 Economic evaluation represents a set of methods for comparative analysis of alternative courses of action, in terms of both their costs and consequences. That is, economic evaluation compares the outcomes or benefits of an action to the inputs required or cost of that action.3,9 Several potential barriers have been identified as deterring decision-makers from putting the results of economic evaluation studies into action. These have been reported in detail elsewhere;4–8 however, the main barriers include: constraints on the capacity to produce, access and interpret evidence from economic evaluations; inflexibility of healthcare budgets; lack of familiarity with economic evaluations; political objectives; lack of time and timeliness of economic evaluations; assumptions in economic evaluation; and poor quality of studies. The key barrier appears to centre around the fact that current economic evaluations are rarely taking into account the real world contingencies or contexts in which allocation decisions are made.6,10 Most economic evaluations are simply applied economics and ‘practise a form of robust and eclectic empiricism’.11,12 They are designed to answer single-issue questions from a single discipline perspective (e.g. comparing a new vaccine with an existing vaccine or no vaccine). They exclude both subjective and nonempirical factors, as well as specific interests of different stakeholders.12 However, the economics of healthcare is more intricate, socially structured and unpredictable than the existing paradigm allows.10 This suggests the need for a new mode of thinking about economic evaluation, which might better reflect the decision context. This is acknowledged, on paper at least, by one of the key protagonists, Drummond,13 who notes that the results of economic evaluations should not be seen as being independent of the decision-making context. One way forward is to apply complex adaptive systems theory, which can allow evidence-based strategies, such as economic evaluations, to be set in a context with other factors that impinge on the decision-making process.10,14,15 Complexity thinking provides new insights into natural systems to complement traditional knowledge. It has the potential to develop our understanding of issues including multiple perspectives, uncertainty, transdisciplinarity and broader societal involvement.7 This can shift evidence-based evaluations to more qualitativeand socially-based discourses through identifying and assessing the needs of individuals to make good decisions.12,16 This paradigm shift may permit economic evaluations to ‘connect’ to policy actions in new ways that are both wise and practical.

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This article illustrates how complexity theory can enrich and broaden our understanding of why evidence-based reviews, including economic evaluations, have not always been as successful as commentators such as health economists would have hoped. It shows health economists and all other evaluators how to recognise situations they are in, and the limitations these present for traditional evidence-based decision-making. Insights from complex adaptive systems theory have the potential to correspond to familiar activities, which are a part of evidencebased strategies, and can help health economists to reduce the limitations of current economic evaluations. Changing metaphors in healthcare For a long time, Newtonian mechanics has been a metaphor underpinning medicine and healthcare. It assumes that activity and performance can be accurately determined via rational, ‘technical fixes’ in a reductionist fashion.17–19 It views the human body as a complicated machine and health problems as a failure of its components. In turn, reducing healthcare problems into smaller component parts and considering each in isolation allows an accurate identification of the most appropriate intervention for each problem.20 Although the reductionist methodologies and machine metaphor have had a considerable influence on the practice of medicine and on the management of healthcare, in general they have limited applicability to the non-linear dynamism of social systems.21,22 Healthcare problems often result from the interactive and multi-dimensional nature of different systems (e.g. a human body or health system), and it is seldom that they can be reduced to one single cause.23,24 It is difficult for healthcare decision-makers to predict and adopt best practices, comply with regulations and achieve wide-scale improvements.7,20 It is the limitations of reductionism that has inspired social scientists in healthcare to outline and develop insights from complexity theory.21,25 Describing complexity Concept Broadly, complexity is an explanatory concept which is used to describe change and operation in social systems. Most writers on complexity recognise that complex systems are generally constrained in their relevance to certain zones, where the degree of both task complexity and causality can be predicted.14,18,19,26,27 These zones include simple, known or rational; complicated or knowable; complex; and zones or times of anarchy and chaos. The premise behind simple and complicated situations is that causal relationships are perceptible, and right choices can be determined based on the facts, assuming an ordered universe. However, complex and chaotic situations are unordered, where causeand-effect relationships are not immediately apparent and the way forward is determined by emerging patterns.14 Complexity thus refers to a dynamic state of behaviour, between order and chaos, that a system can exhibit.19 Complexity theory is not a single theory and deals with certain complex systems which tend to adapt to their context, i.e. they are complex adaptive systems.28 Broadly and inclusively, a complex system contains a large number of elements, as well as many relationships and interactions between those. A complex adaptive system is adaptable in

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two senses: sub-systems or elements within the system adapt to systemic and environmental changes; and the system as a whole adapts to changes in its environment.21,26 Clearly, the larger the number of elements, relationships and interactions, the more complex the system. Fundamentals of complex adaptive systems The study of living complex adaptive systems reveals several properties which defy reductionist thinking. Plsek18 identified eight of these as significant for studies of complex adaptive systems in healthcare (Table 1). Drawing on these features, we will describe how complexity thinking can account for values missing from current economic evaluations. Complexity and change in social organisations Numerous authors have proposed that complex adaptive systems theory has the potential to help enrich and broaden our understanding of how change occurs in social organisations.27 For example, in commerce, complexity has been applied successfully to inform large-scale change in a rapidly changing commercial environment.29 Complexity has also proved popular in healthcare and healthcare organisations, though experimentation and adaptability have only recently begun to be implemented.15,19,30–32 For example, Zimmerman et al.21 provide several examples of how insights from complexity can help healthcare organisations to analyse various situations. In the field of health economics and policy decisions, however, the interest in complexity thinking is much more recent.7,15,33,34 Lessard,7 for example, suggested the use of complexity in the field of health economics to provide added value missing from the current economic evaluations. Shiell et al.15 also argue that a theoretical understanding of complex systems in which interventions are implemented, could help to design better economic evaluations. Yet, if complexity theory is

Table 1. Adaptable elements

Simple rules Non-linearity Emergent behaviour Not predictable in detail

Inherent order Context and nested systems

Co-evolution

C

to be useful in economic evaluation, it is necessary to be clear what (else) it entails. Evidence-based evaluations and complex adaptive systems theory The prevailing paradigm in all evidence-based strategies, remains grounded in linear, reductionist approaches which emphasise rationality, problem-solving, prediction and control within a boundary of knowledge and a closed system model.7,35,36 However, complexity theory emphasises non-linear interpretations of the real world.35,36 Although relationships, networks and interconnections are crucial to both complexity theory and mainstream evidence-based evaluations,36 the ways in which economic evaluations are translated into empirical statements are highly linear.37 It is widely acknowledged that social systems, including economic activities, have a high degree of non-linearity,31,37 and that economic evaluation does not occur outside the context of social interactions.10,15,33,37 Interactions are embedded and integrated in social realities, where socially constructed and contextual patterns are constantly shaped and reshaped.7,37,38 Yet, this issue is neither discussed nor incorporated into current evidence-based evaluations. Understanding such preferences and behaviours means attention must be paid to social realities in the social context within which they emerge.7,10 Complexity theory highlights context.18,32 Context can add crucial dimensions to our understanding.15,34 Evidence-based evaluations rely heavily on quantitative evidence summarised into apparently context-free average effects, with the role of observer usually seen as external to the data.4 In contrast, complexity theory suggests that the ultimate way to identify how a change would occur in a complex system is to observe it.25 In fact, ‘complex [adaptive] systems incorporate the observer as part of the system’.19 Detailed empirical observation and qualitative methods provide a form of explanation which is essential for understanding complex behaviour. This

Fundamentals of complex adaptive systems (adapted from Plsek18)

The elements in a system can change themselves. For example, microorganisms develop resistance to antibiotics and people learn. In machines, change must be imposed, whereas in complex adaptive systems, under the right conditions, change happens from within. Complex outcomes can emerge from a few simple rules that are locally applied. Small changes can have large effects. For example, a large health education campaign may produce little change in behaviour, yet a local rumour could prompt a union into industrial action. Continual creativity and novelty is a natural state of the system. For example, surgical techniques are modified in operating theatres; good social workers generate novel solutions to family problems. Forecasting is inherently inexact, yet bounded. For example, the extent and severity of a new influenza epidemic cannot be predicted with any accuracy, but it is bounded in the sense that we can make generally true statements about things like the probability of a new outbreak or the likely patterns of spread. Self-organisation is a key idea in complexity science. Systems can be orderly even without central control. For example, there is no central controller for the internet or for the food supply of a city; economic markets operate without central control. Complex systems exist within systems, and contain sub-systems. For example, a hospital is part of a national health system and contains wards and clinics. Although we can study a complex adaptive system as a defined whole, its context matters in fundamental ways, as do its constituent parts. A complex adaptive system moves forward through constant tension and dynamic balance. Competition and cooperation are both intrinsic to the way health professionals work. Improved nursing or medical techniques are usually developed through multi-disciplinary collaboration. Tension, paradox, uncertainty and anxiety are healthy in a complex adaptive system, whereas in machine thinking, they are to be avoided.

would result in evidence-based reviews being based more firmly in the experience of individuals engaging with healthcare decision-making.7 Unlike economic evaluation, complexity encourages researchers to approach policy issues from a trans-disciplinary view.30,39 Such an approach shifts the focus away from a single discipline perspective to a multiplicity of insights and a politics of inclusion,22 in order to reach consensus about goals and solutions. A trans-disciplinary approach is open a priori to a range of theories of knowledge and acknowledges that academic experts do not have a monopoly on intelligence and wisdom.22 It seeks to address the real world contingencies by unifying knowledge from the perspectives of both academics and nonacademics in a wider context.39 For economic evaluations to work well, it is important to acknowledge differences in the way the various professions approach the evaluation of interventions, and that those differences call for alternative types of evaluations. Nevertheless, there has been low level of agreement on research directions and methods in the application of complex adaptive systems theory in the field of economic evaluations.40,41 For example, although Lessard7 suggests a turn towards complexity theory and reflexivity as a basis for integration of economic evaluations in policy, Kernick17,38 is sceptical of the maturity of complexity theory and its application to health service research, including economic evaluation studies. Below, we describe how complexity may influence the uptake of evidence from economic evaluations. Further insights from complex adaptive systems theory for evidence-based interventions, including economic evaluations Stacey and Snowden provided models of complex adaptive systems that are useful if understood correctly,14,26,27 but which can be easily misinterpreted. The ‘Stacey Diagram’ (Fig. 1) shows three zones: a zone of plan and control, a zone of complexity and a zone of chaos (anarchy). The purpose of the Stacey Diagram is to assist in choosing appropriate management actions in a complex adaptive system, based on the degree of certainty and level of agreement on the issue in question. The vertical axis measures the level of agreement and the horizontal axis indicates the degree of certainty about an issue or decision within a team. Appropriate management style varies with the level of agreement or certainty surrounding the issue. The degree of certainty relates to knowledge of cause and effect. A decision is close to certainty when the cause and effect relationships are known or can be determined. This is often the case when similar decisions have been managed in the past. At the other end of the certainty continuum are decisions that are far from certainty. These are often unique situations or, at least, ones new to the decision-makers. Their cause and effect linkages are not clear and extrapolating from past experience is not possible. Healthcare management theory and practice have focussed largely on the region in the Diagram which is close to both certainty and agreement. In this region, we use techniques which gather data from the past and use these to predict the future. We develop specific plans, procedures and protocols to direct action and achieve outcomes, and we monitor performance by

A. Baghbanian and G. Torkfar

Far from agreement

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Anarchy

Complex Complicated

Close to agreement

D

Simple

Close to certainty

Complicated Far from certainty

Fig. 1. Stacey Diagram (Adapted from Stacey, 199627)

comparing action against the plans or protocols. This is good practice for issues and activities which fall in the zone (designated simple). The goal is to repeat what works. Stacey suggests two broad approaches may be useful in the sub-zones of the complicated zone. Where there are reasonably high levels of certainty about how outcomes are achieved but low levels of agreement about which outcomes are desirable and which politics or coalition building are used to move an organisation’s agenda forward. Where there is a reasonably high level of agreement but not much certainty about cause and effect, a strong sense of shared mission or vision may be useful. In this region, the goal is to move towards an agreed future state, even though specific strategies cannot be specified in advance. In all parts of the complicated zone, expertise, professionalism, experience and sound judgement are important. In the top right corner of the Diagram are situations with very high levels of uncertainty and disagreement which often result in a breakdown of systems, anarchy or chaos. Here, traditional methods such as scientific management or reductionist decision-making, do not work. One strategy to deal with issues in this zone is avoidance; another is either command and control, or the power of intellect and charisma. Any action, or inaction, in this zone may carry high risks. The large area in Fig. 1 lies between the anarchy region and regions of traditional management. Stacey calls this the zone of complexity. Complex issues or situations may not be resolved using traditional management approaches or established practice, and often call for creativity, innovation, and new modes of operating.21 Snowden modified Stacey’s model for use in knowledge management activities. Whereas the zones in the Stacey Diagram refer to issues or problems, the Cynefin model14,26 introduces a decision-making framework with four knowledge domains, a concealed central space and two visible-invisible sides characterised by the relationship between cause and effect

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(Fig. 2). Decision-makers in each domain require different actions and knowledge sources.14,26 Unlike the Stacey Diagram, which is an agreement certainty matrix, the Cynefin diagram has no numerical axes. Stacey’s simple zone corresponds to Snowden’s domain of the known; complicated corresponds to knowable; the word ‘complex’ is used in both diagrams; and anarchy corresponds to chaos. But whereas Stacey describes issues, problems and perhaps situations, Snowden focusses on the knowledge held by actors in a complex adaptive system. It is important to recognise that in both models, the whole diagram represents aspects of a complex adaptive system. That is, a complex adaptive system includes locations which are simple and known, problems which are complicated and knowable, situations that are complex and zones or times of anarchy and chaos. Simple situations can become complex and order can emerge from chaos. These are zones or domains with dynamic and permeable boundaries within complex adaptive systems, not kinds of systems or subsystems. We believe that the incorporation of the Stacey Diagram and Cynefin framework into economic evaluations and all other evaluations would provide useful insights for their development and contribute to more robust decision-making. Both models may help evaluators decide whether a particular healthcare intervention can be purposefully managed in a linear, deterministic and reductionist fashion (the zone of rationality in Stacey’s model or simple and complicated zones in Cynefin framework), or requires evaluation to explicitly take account of context and system elements to be useful (the zone of complexity in both models), or can be merely described (the chaotic zone). Within complex adaptive systems theory, this article suggests that the Cynefin model encourages the separation of

evidence-based reviews into two ordered domains (simple and complicated) and two unordered domains (complex and chaos). In the former, the traditional science and explicit or fact-based knowledge can be applied, whereas context- or pattern-based knowledge and adaptive practice are appropriate in the unordered complex domain. In the complex zone, health economists and all other evaluators need to know when to share knowledge and when to use it alone, when to look for the wisdom of the group and when to take their own counsel. A deep understanding of the evaluation context, the ability to embrace complexity and paradox, and a willingness to flexibly perform in different situations will be required if evaluators are to make rigorous assessments in a time of increasing uncertainty, and to take relevant contextual factors and knowledge into account. Below we illustrate how a complex adaptive system can move between the domains, and how economic evaluations might be located in various zones of the Stacey Diagram or the Cynefin framework. An economic evaluation can be as simple as deciding to buy a new MRI machine, which is normally managed with linear and mechanistic system thinking as there is a high degree of certainty and agreement in the evaluation team in achieving successful outcomes. Each of the team members relinquishes some autonomy to achieve an undisputed goal. Each member’s actions are known or knowable to the whole team, and action is bounded by known procedures and explicit guidelines. In this state, the outcome is quite predictable and the team exhibits reasonably little emergent behaviour and the job is done efficiently. However, suppose that the evaluation incorporates the dimension of quality of care into the measurement of benefits. Linear thinking does not work anymore and structured procedures do not

Knowable

Complex Domain of possibilities, complex Cause and effect are only coherent in retrospect and do not repeat Pattern management Perspective filters Complex adaptive systems Probe – Sense – Respond

Chaos

Domain of the probable, complicated Cause and effect separated over time and space Analytical/reductionist Scenario planning Systems thinking Sense – Analyse – Respond

Known

Inconceivable domain, chaotic Single or multi point attractor(s) to stabilise situation Crisis management Avoid creating long term dependency on single attractor ACT – SENSE – RESPOND

E

Domain of the actual, simple Cause and effect relations repeatable, perceivable and predictable Legitimate best practice Fact-based management Standard operating procedures Process reengineering Sense – Categorise – Respond

Fig. 2. Cynefin Domains (Adapted from Kurtz & Snowden, 200326)

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apply because any small change over a long period of time can produce disproportionately major and unknown consequences.18 Although there is considerable agreement among stakeholders about quality of care as a health outcome, they have differing and unknown interpretations and values around what the objectives are. Many economic evaluations are largely mathematical models based on multiple assumptions and excluding consideration of social context. There is a big difference between applying economic or financial evaluation to the purchase of an MRI machine which might last 5 years, to the evaluation of a health prevention program where the benefits might not be obvious for another 20 years or more - so predicting the context, or putting values on costs and benefits that accrue in 20 years or more is problematic. In addition, constructing the economic evaluation on conservative assumptions and available evidence (preferably from randomised clinical trials),7 may not be responsive enough to determine the effects of changes and interventions in a broader societal context.4,33 Knowledge in the complex zone, with creative responses, is required to manage complex and unpredictable issues.18 Although past experience along with some general strategies from experts can be applied as an initial guide, using the methods that have brought about success in the past may not lead to success.18,26,41 Clearly, there are domains where conventional evidencebased evaluations or interventions are very useful. There are, however, situations where the system operators (i.e. the health economist) move into one of the complex or uncertain realms and conventional approaches fail. The question of ‘how often does this happen in the field?’ is a direction for future research.

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Competing interests The authors declare there are no competing interests. Acknowledgements The authors would like to thank Professor Stephen Leeder, Professor Gavin Mooney, Dr Ian Hughes and Dr Freidoon A. Khavarpour for their helpful comments in the preparation of this paper. The authors also gratefully acknowledge the assistance of the Iranian Ministry of Health and Medical Education for financial support and the Australian healthcare managers and administrators who participated in this study between 2006 and 2009.

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Conclusion Healthcare decision-makers often know what information they need to manage resources and that, in many cases, more information is needed than just evidence from economic evaluations. Interventions evaluated by health economists have seldom addressed the totality of factors underlying the decision context. Evidence from healthcare evaluations has to be put in context – a context that is complex, political and often resistant to voluntary change. So the challenge here is not whether economic evaluations are the best techniques to analyse the complex situations that we find in healthcare, but to understand that economics needs to be given more breadth. Proponents of evidence-based resource allocation decisionmaking need to shake off their supposed rationalist, linear, and reductionist ways of thinking and evaluating, and – through greater understanding about complex adaptive systems – alter their goals and methods of analysis to reflect better the nature of the complex interventions with which they are dealing. If systematic evidence-based evaluations are conducted in a realm of complexity, their focus would shift from the rational calculus of evaluations to networks of interdependent relationships, multiple perspective, and trans-disciplinary involvement, enabling them to envisage key components and relationships within relevant systems, and to develop innovative solutions to complex situations within broader contexts. With these interfaces, research evidence, including evidence from economic evaluations, has the most potential to be utilised.

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