To develop a compositing method that demonstrates improved performance compared with commonly used tests for statistical analysis of physician cost of care data.
Commercial preferred provider organization (PPO) claims data for internists from a large metropolitan area.
We created a nonparametric composite performance metric that maintains risk adjustment using the ilcoxon rank‐sum () test. We compared the resulting algorithm to the parametric observed‐to‐expected ratio, with and without a statistical test, for stability of physician cost ratings among different outlier trimming methods and across two partially overlapping time periods.
The algorithm showed significantly greater within‐physician stability among several typical outlier trimming and capping methods. The algorithm also showed significantly greater within‐physician stability when the same physicians were analyzed across time periods.
The nonparametric algorithm described is a more robust and more stable methodology for evaluating physician cost of care than commonly used observed‐to‐expected ratio techniques. Use of such an algorithm can improve physician cost assessment for important current applications such as public reporting, pay for performance, and tiered benefit design.
