Modeling the value for money of changing clinical practice change: a stochastic application in diabetes care.
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. Adopting a total net benefit approach, a model was recently developed to overcome problems with the use of combined ratio statistics when analyzing decision uncertainty.


OBJECTIVE
To demonstrate 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 modeling and evidence synthesis is adopted, using Markov Chain Monte Carlo simulation in WinBUGs. Data on model parameters is gathered from various sources, with effectiveness of implementation being estimated using pooled, random-effects meta-analysis. Decision uncertainty is illustrated using cost-effectiveness acceptability curves and frontier.


RESULTS
Decisions about whether to adopt intensified glycemic control and whether to adopt audit and feedback alter for the 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 cost-effectiveness acceptability curves and cost-effectiveness acceptability frontier 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.
Hoomans
Ties
T
Department of Health Organization, Policy, and Economics, Maastricht University, Maastricht, The Netherlands, United Kingdom. t.hoomans@beoz.unimaas.nl
Abrams
Keith R
KR
Ament
Andre J H A
AJ
Evers
Silvia M A A
SM
Severens
Johan L
JL
D001499:Bayes Theorem; D001786:Blood Glucose / Q000032:analysis*; D003657:Decision Making; D003924:Diabetes Mellitus, Type 2 / Q000097:blood* / Q000191:economics* / Q000453:epidemiology / Q000628:therapy; D005102:Health Expenditures; D006801:Humans; D008390:Markov Chains; D018803:Models, Economic; D009010:Monte Carlo Method; D009426:Netherlands / Q000453:epidemiology; D017410:Practice Guidelines as Topic; D015995:Prevalence; D011320:Primary Health Care / Q000191:economics*; D019057:Quality-Adjusted Life Years; D013269:Stochastic Processes; D035501:Uncertainty
D001786:Blood Glucose