Adaptive decision-making: how Australian healthcare managers decide

2012 ◽  
Vol 36 (1) ◽  
pp. 49 ◽  
Author(s):  
Abdolvahab Baghbanian ◽  
Ian Hughes ◽  
Ali Kebriaei ◽  
Freidoon A. Khavarpour
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Health Policy ◽  
Research Evidence ◽  
Decision Makers ◽  
Economic Evaluations ◽  
Evidence Based ◽  
Full Potential ◽  
Healthcare Decision ◽  
Adaptive Decision Making

Despite many calls for the utilisation of research evidence in health policy-making, it is not widely practised, and little is known about how decision-makers in healthcare organisations actually make decisions. We recruited a purposive sample of Australian healthcare decision-makers to complete a web-based survey. We then took a sub-sample from willing respondents for individual interviews. All interviews were audio-recorded, transcribed verbatim and coded thematically. We found that resource allocation decision-making varied greatly across the Australian healthcare system. Decision-making was highly dependent on the operational context in time, place and purpose, and that research evidence was rarely exploited to its full potential. Decision-making involved a multifaceted interplay of elements in situation of inquiry. All decisions were made by networks or collectives of people; and no instance of individual decision-making was reported. This varied, social and contextual nature of decision-making points to a complexity that is not reflected in systematic evidence-based reviews or evidence-based models for decision-making, and we did not discover an appropriate model to reflect this complexity in the health- related literature. We developed a model of ‘adaptive decision-making’ that has potential to guide robust decision-making in complex situations, and could have some value as an explanatory or theoretical model for teaching and practice. What is known about the topic? The topic is certainly novel and original, relevant and timely for academics and healthcare decision-makers. Despite increasing calls for the use of systematic evidence-based reviews including economic evaluations, the way in which decision-makers arrive at their allocation decisions and how such decisions reflect concern for economic efficiency is often blurred. This topic is an important one for its relevance to the current difficulties in the complex situation of healthcare. What does this paper add? This paper shows that decision-makers acknowledged the integration of economic principles as contextual realities into their decision-making activities, rather than utilising the results of ever-more seemingly ‘technically sound’ economic evaluations, which cannot address the inherent uncertainty attached to complex decision-making activities. We developed a novel adaptive model of decision-making generated by the interplay of multiple behaviours and factors in the situation of inquiry. The model is new and takes into account the complexity of the context in time, place, purpose and administrative location. What are the implications for practitioners? This paper 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 adaptive decision-making model designed in this study has the potential as a guide or heuristic device for teaching and practice. Healthcare decision-makers need to be prepared for complexity and ambiguity and cannot expect the data to tell them everything they need to know. We expect to see a shift in the literature on healthcare decision-making, not away from evidence-based practice and economic evaluation, but towards contextualising these methods in broader, adaptive models of decision-making.

Resource allocation and economic evaluation in Australia's healthcare system

2011 ◽  
Vol 35 (3) ◽  
pp. 278 ◽  
Author(s):  
Abdolvahab Baghbanian ◽  
Ian Hughes ◽  
Freidoon A. Khavarpour
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Economic Evaluation ◽  
Cost Effectiveness ◽  
Decision Makers ◽  
Economic Evaluations ◽  
Healthcare Decision ◽  
Effectiveness Analysis ◽  
Allocation Decisions ◽  
Resource Allocation Decisions

Objective. To explore dimensions and varieties of economic evaluations that healthcare decision-makers do or do not use. Design. Web-based survey. Setting and participants. A purposive sample of Australian healthcare decision-makers was recruited by direct invitation through email. All were invited to complete an online questionnaire derived from the EUROMET 2004 survey. Results. A total of 91 questionnaires were analysed. Almost all participants were involved in financial resource allocations. Most commonly, participants based their decisions on patient needs, effectiveness of interventions, cost of interventions or overall budgetary effect, and policy directives. Evidence from cost-effectiveness analysis was used by half of the participants. Timing, ethical issues and lack of knowledge about economic evaluation were the most significant barriers to the use of economic evaluations in resource allocation decisions. Most participants reported being moderately to very familiar with the cost-effectiveness analysis. There was a general impression that evidence from economic evaluations should play a larger role in the future. Conclusions. Evidence from health economic evaluations may provide valuable information in some decisions; however, at present, it is not central to many decisions. The study suggests that, for economic evaluation to be helpful in real-life policy decisions, it has to be placed into context – a context which is complex, political and often resistant to voluntary change. What is known about the topic? There are increasing calls for the use of evidence from formal economic evaluations to improve the quality of healthcare decision making; however, it is widely acknowledged that such evidence, as presently constituted, is underused in its influence on allocation decisions. What does this paper add? This study highlights that resource allocation decisions cannot be purely based on the use of technical, economic data or systematic evidence-based reviews. In order to exploit the full potential value of economic evaluations, researchers need to make better sense of decision contexts at specific times and places. What are the implications for practitioners? The study has the potential to expand researchers and policy-makers’ understanding of the limited use of economic evaluation in decision-making. It produces evidence that decision-making in Australia’s healthcare system is not or cannot be a fully rational bounded process. Economic evaluation is used in some contexts, where information is accurate, complete and available.

Collaborative Practice: A Strategy to Improve the Relevance of Health Services Research

2002 ◽  
Vol 15 (3) ◽  
pp. 18-24 ◽  
Author(s):  
Kevin Brazil ◽  
Stuart MacLeod ◽  
Brian Guest
Keyword(s):  
Decision Making ◽  
Health Services Research ◽  
Health Services ◽  
Research Evidence ◽  
Collaborative Practice ◽  
Decision Makers ◽  
Healthcare Decision ◽  
Services Research ◽  
Collaborative Relationships

Health services research has emerged as a tool for decision makers to make services more effective and efficient. While its value as a basis for decision making is well established, the incorporation of such evidence into decision making remains inconsistent. To this end, strengthening collaborative relationships between researchers and healthcare decision makers has been identified as a significant strategy for putting research evidence into practice.

scholarly journals The NCCMT’s dynamic model for evidence-informed public health

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
E Clark ◽  
S Neil-Sztramko ◽  
M Dobbins
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Research Evidence ◽  
Decision Makers ◽  
Local Data ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Political Preferences ◽  
Health Decision ◽  
Structured Approach

Abstract Issue It is well accepted that public health decision makers should use the best available research evidence in their decision-making process. However, research evidence alone is insufficient to inform public health decision making. Description of the problem As new challenges to public health emerge, there can be a paucity of high quality research evidence to inform decisions on new topics. Public health decision makers must combine various sources of evidence with their public health expertise to make evidence-informed decisions. The National Collaborating Centre for Methods and Tools (NCCMT) has developed a model which combines research evidence with other critical sources of evidence that can help guide decision makers in evidence-informed decision making. Results The NCCMT's model for evidence-informed public health combines findings from research evidence with local data and context, community and political preferences and actions and evidence on available resources. The model has been widely used across Canada and worldwide, and has been integrated into many public health organizations' decision-making processes. The model is also used for teaching an evidence-informed public health approach in Masters of Public Health programs around the globe. The model provides a structured approach to integrating evidence from several critical sources into public health decision making. Use of the model helps ensure that important research, contextual and preference information is sought and incorporated. Lessons Next steps for the model include development of a tool to facilitate synthesis of evidence across all four domains. Although Indigenous knowledges are relevant for public health decision making and should be considered as part of a complete assessment the current model does not capture Indigenous knowledges. Key messages Decision making in public health requires integrating the best available evidence, including research findings, local data and context, community and political preferences and available resources. The NCCMT’s model for evidence-informed public health provides a structured approach to integrating evidence from several critical sources into public health decision making.

CAN BAYESIAN METHODS MAKE DATA AND ANALYSES MORE RELEVANT TO DECISION MAKERS?

2001 ◽  
Vol 17 (1) ◽  
pp. 114-122 ◽  
Author(s):  
Steven H. Sheingold
Keyword(s):  
Decision Making ◽  
Bayesian Methods ◽  
Paradigm Shift ◽  
Relevant Information ◽  
Decision Makers ◽  
Evidence Based ◽  
Critical Importance ◽  
Bayesian Approaches ◽  
Bayesian Analyses ◽  
And Performance

Decision making in health care has become increasingly reliant on information technology, evidence-based processes, and performance measurement. It is therefore a time at which it is of critical importance to make data and analyses more relevant to decision makers. Those who support Bayesian approaches contend that their analyses provide more relevant information for decision making than do classical or “frequentist” methods, and that a paradigm shift to the former is long overdue. While formal Bayesian analyses may eventually play an important role in decision making, there are several obstacles to overcome if these methods are to gain acceptance in an environment dominated by frequentist approaches. Supporters of Bayesian statistics must find more accommodating approaches to making their case, especially in finding ways to make these methods more transparent and accessible. Moreover, they must better understand the decision-making environment they hope to influence. This paper discusses these issues and provides some suggestions for overcoming some of these barriers to greater acceptance.

scholarly journals 18.H. Workshop: Bringing evidence during public health emergencies: addressing the challenge through HTA

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Emergency Preparedness ◽  
Public Health Emergency ◽  
Decision Makers ◽  
Evidence Based ◽  
Public Health Emergencies ◽  
Response Planning ◽  
Emergency Preparedness And Response ◽  
Public Health Emergency Preparedness

Abstract Evidence-based decision-making is central to public health. Implementing evidence-informed actions is most challenging during a public health emergency as in an epidemic, when time is limited, scientific uncertainties and political pressures tend to be high, and irrefutable evidence may be lacking. The process of including evidence in public health decision-making and for evidence-informed policy, in preparation, and during public health emergencies, is not systematic and is complicated by many barriers as the absences of shared tools and approaches for evidence-based preparedness and response planning. Many of today's public health crises are also cross-border, and countries need to collaborate in a systematic and standardized way in order to enhance interoperability and to implement coordinated evidence-based response plans. To strengthen the impact of scientific evidence on decision-making for public health emergency preparedness and response, it is necessary to better define mechanisms through which interdisciplinary evidence feeds into decision-making processes during public health emergencies and the context in which these mechanisms operate. As a multidisciplinary, standardized and evidence-based decision-making tool, Health Technology Assessment (HTA) represents and approach that can inform public health emergency preparedness and response planning processes; it can also provide meaningful insights on existing preparedness structures, working as bridge between scientists and decision-makers, easing knowledge transition and translation to ensure that evidence is effectively integrated into decision-making contexts. HTA can address the link between scientific evidence and decision-making in public health emergencies, and overcome the key challenges faced by public health experts when advising decision makers, including strengthening and accelerating knowledge transfer through rapid HTA, improving networking between actors and disciplines. It may allow a 360° perspective, providing a comprehensive view to decision-making in preparation and during public health emergencies. The objective of the workshop is to explore and present how HTA can be used as a shared and systematic evidence-based tool for Public Health Emergency Preparedness and Response, in order to enable stakeholders and decision makers taking actions based on the best available evidence through a process which is systematic and transparent. Key messages There are many barriers and no shared mechanisms to bring evidence in decision-making during public health emergencies. HTA can represent the tool to bring evidence-informed actions in public health emergency preparedness and response.

The limits of evidence-based anti-bribery law

2021 ◽  
Author(s):  
Kevin E. Davis
Keyword(s):  
Decision Making ◽  
Best Practice ◽  
Decision Makers ◽  
Systematic Research ◽  
Evidence Based ◽  
Causal Relationships ◽  
Causal Inferences ◽  
Business Regulation

Evidence-based regulation is a term of art that refers to the process of making decisions about regulation based on evidence generated through systematic research. There is increasing pressure to treat evidence-based regulation as a global best practice, including in the area of anti-bribery law. Too little attention has been paid to the fact that under certain conditions evidence-based regulation is likely to be a less appealing method of decision making than the alternative – namely, relying on judgment. Those conditions are: it is difficult to collect data on either interventions or outcomes; accurate causal inferences are difficult to draw; there is little warrant for believing that the same causal relationships will apply in a new context; or the decision makers in question lack the capacity to undertake one of these tasks. These conditions are likely to be present in complex, transnational, decentralized, and dynamic forms of business regulation such as the global anti-bribery regime.

Introduction

2019 ◽  
pp. 1-18
Author(s):  
William B. Rouse
Keyword(s):  
Decision Making ◽  
Competitive Advantage ◽  
Computational Models ◽  
Decision Makers ◽  
Evidence Based ◽  
Leading Indicators ◽  
Tipping Points ◽  
Decision Making Models

Chapter 1 provides the introduction to this book. Predictions can seldom specify what will happen, so, inevitably, one addresses what might happen. There are often many possible futures, with leading indicators and potential tipping points for each scenario. Computational models can be used to explore designs of systems and policies to determine whether these designs will likely be effective and to aid in decision-making. Models are means to ends rather then ends in themselves. Decision-makers seldom crave models. They want their questions answered in an evidence-based manner. Decision-makers want insights that provide them with a competitive advantage. They want to understand possible futures to formulate robust and resilient strategies for addressing these futures.

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