A nonparametric statistical method that improves physician cost of care analysis

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 Wilcoxon rank-sum (WRS) 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 WRS 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.