Using outcomes data to identify best medical practice: the role of policy models.
Increasingly, physicians are attempting to incorporate best evidence into their clinical decision making. However, best evidence takes a variety of forms, including clinical trials, cohort studies, administrative data, and patient preference data. Incorporating multiple data sources in a way that informs complex clinical decisions is a substantial analytical challenge. One approach to this challenge is to develop a simulation/decision model that explicitly represents the natural history of disease and the impact of treatments on that natural history. The model should be requisite--that is, sufficient in form to address the decision problem--but not overly complex. Such a model can be of value because it (1) allows a variety of viewpoints to be considered, (2) incorporates the best scientific evidence, and (3) permits sensitivity analyses to evaluate the impact of alternative clinical scenarios and uncertainty in model inputs. The Stroke Prevention Policy Model (SPPM) illustrates this approach. The SPPM is a simulation model designed to predict the best among various treatment alternatives for preventing strokes. Similar models can be applied to treatment outcomes for liver disease.
Matchar
D B
DB
Center for Clinical Health Policy Research, Duke University Medical Center, Durham, NC 27705, USA. match001@mc.duke.edu
Samsa
G P
GP
D019985:Benchmarking; D003661:Decision Support Techniques; D019317:Evidence-Based Medicine; D006801:Humans; D008107:Liver Diseases; D017063:Outcome Assessment, Health Care / Q000379:methods*