A Nonparametric Statistical Method That Improves Physician Cost of Care Analysis
Article first published online: 23 APR 2012
© Health Research and Educational Trust
Health Services Research
Volume 47, Issue 6, pages 2398–2417, December 2012
How to Cite
Metfessel, B. A. and Greene, R. A. (2012), A Nonparametric Statistical Method That Improves Physician Cost of Care Analysis. Health Services Research, 47: 2398–2417. doi: 10.1111/j.1475-6773.2012.01415.x
- Issue published online: 12 NOV 2012
- Article first published online: 23 APR 2012
- Statistical methods;
- physician profiling;
- nonparametric statistics;
- efficiency index
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.