Academic journal article Australian Health Review

Adaptive Decision-Making: How Australian Healthcare Managers Decide

Academic journal article Australian Health Review

Adaptive Decision-Making: How Australian Healthcare Managers Decide

Article excerpt


At a time of rapid change in science, technology, demography and epidemiology, along with economic and political movements, healthcare policy-making is becoming increasingly complex and inter-dependent.1 Political, economic, institutional, ethical and other decisions shape healthcare systems through many choices among available alternatives.2 This is because each profession brings a distinctive expertise to bear on issues, problems and questions.

There have been increasing calls for, and practice of, researchinformed decisions and policy-making.3-6 Each profession employs more or less different approaches, methods and sources of knowledge in making decisions, and hierarchy of evidence for evidence-based decision-making is now widely known. The discipline of economics, for instance, has developed and refined methods to support decision-making, and members of the subdiscipline of health economics have called for increased use of evidence from economic evaluations to inform rational decisionmaking generally. Economic evaluation is 'the comparative analysis of alternative courses of action in terms of both their costs and consequences'.7

Attempts to improve financial resource allocation decisionmaking have now concentrated on economic principles of technical (i.e. productive) and allocative efficiency, and other ways of incorporating competing demands into existing resource constraints. 8,9 However, research-informed policy-making has proved challenging. It is well understood and widely acknowledged that many policy issues, including financial resource allocation decisions, do not reflect research evidence to the extent that in theory they can,10 and that research evidence rarely feeds directly and instrumentally into policy decisions.5,11 This is where a major gap emerges between theory and practice, or action and research.

In view of evidence that scientific research (including evidence from economic evaluation12,13) is not utilised as often as social scientists (including health economists) recommend, including a lack of evidence about what foundations decisionmakers use, we were interested to discover how Australian healthcare administrators decided to allocate scarce resources.


A mixed-methods approach, using web-based survey and constructivist grounded theory permitted exploration of resource allocation decision-making. 'A constructivist approach places priority on the phenomena of study and sees both data and analysis as created from shared experiences and relationships with participants'.14 The potential Australian healthcare system decision-makers, whose contact details were made available to the general public, were identified and invited to participate in this research study. A purposive (non-probability) sample of 91 participants was then recruited to complete an online questionnaire. The participants were healthcare administrators at multiple levels of organisational structure drawn from the Commonwealth and New South Wales Departments of Health and Ageing, with responsibility for financial resource allocation decision-making. From willing respondents, we took a subsample of 25 participants to participate in face-to-face interviews.

Participants were invited to illustrate how they made financial resource allocation decisions, including questions about the formal methods, skills, heuristics (rules of thumb), experience and natural talent they used in decision-making. Practise interviews with participants were audio-recorded and transcribed verbatim to maintain the ideas and concepts reported. Full text transcripts of interviews and field notes were imported intoNVivo software for thematic analysis and coding.14,15

The data were initially coded line-by-line and closely examined for any similarities or differences. As we became familiar with the data, they were coded by larger segments of data; that is, closely related (initial) codes were condensed into more directed, selective and enduring categories with stronger analytic directions (focussed coding). …

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