A Nonparametric Statistical Method That Improves Physician Cost of Care Analysis

Objective

To develop a compositing method that demonstrates improved performance compared with commonly used tests for statistical analysis of physician cost of care data.

Data Source

Commercial preferred provider organization (PPO) claims data for internists from a large metropolitan area.

Study Design

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.

Principal Findings

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.

Conclusions

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.