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Tuesday, October 21, 2008: 12:45 PM
Grand Ballroom D (Hyatt Regency Penns Landing)
Background. Decision making about resource allocation for guideline implementation to change clinical practice is inevitably undertaken in a context of uncertainty surrounding the cost-effectiveness of both clinical guidelines and implementation strategies. Recently, a model has been developed in which monetary values are assigned to health outcomes and economic evidence on guidelines and strategies is combined with information on clinical practice to determine the scope of cost-effective guideline implementation. Adopting a net benefit approach, the model overcomes problems with the use of combined ratio statistics when analyzing decision uncertainty concerning clinical practice change.
Purpose: This paper demonstrates the stochastic application of the model for informing decision-making about the adoption of an audit and feedback strategy for implementing a guideline recommending intensive blood glucose control in type 2 diabetes in primary care in The Netherlands.
Methods: An integrated Bayesian approach to decision modelling and evidence synthesis is adopted using Markov Chain Monte Carlo simulation in WinBUGs. Data on model parameters is gathered from various sources, with the effectiveness of audit and feedback being estimated using a pooled, random effects meta-analysis model. Decision uncertainty is illustrated using cost-effectiveness acceptability curves (CEACs) and frontier (CEAF).
Results: Decisions about whether to adopt the guidance on blood glucose control and whether to adopt audit and feedback for its implementation alter over the range of maximum values that decision-makers are willing to pay for health gain. Through simultaneously incorporating uncertain economic evidence on both guidance and implementation strategy, the CEACs and CEAF show an increase in decision uncertainty concerning guideline implementation.
Conclusions: The stochastic application in diabetes care demonstrates that the model provides a simple and useful tool for quantifying and exploring the (combined) uncertainty associated with decision-making about adopting guidelines and implementation strategies and, therefore, for informing decisions about efficient resource allocation to change clinical practice.
Purpose: This paper demonstrates the stochastic application of the model for informing decision-making about the adoption of an audit and feedback strategy for implementing a guideline recommending intensive blood glucose control in type 2 diabetes in primary care in The Netherlands.
Methods: An integrated Bayesian approach to decision modelling and evidence synthesis is adopted using Markov Chain Monte Carlo simulation in WinBUGs. Data on model parameters is gathered from various sources, with the effectiveness of audit and feedback being estimated using a pooled, random effects meta-analysis model. Decision uncertainty is illustrated using cost-effectiveness acceptability curves (CEACs) and frontier (CEAF).
Results: Decisions about whether to adopt the guidance on blood glucose control and whether to adopt audit and feedback for its implementation alter over the range of maximum values that decision-makers are willing to pay for health gain. Through simultaneously incorporating uncertain economic evidence on both guidance and implementation strategy, the CEACs and CEAF show an increase in decision uncertainty concerning guideline implementation.
Conclusions: The stochastic application in diabetes care demonstrates that the model provides a simple and useful tool for quantifying and exploring the (combined) uncertainty associated with decision-making about adopting guidelines and implementation strategies and, therefore, for informing decisions about efficient resource allocation to change clinical practice.