title: A nonparametric statistical method that improves physician cost of care analysis.
abstract: OBJECTIVE
  To develop a compositing method that demonstrates improved performance compared with commonly used tests for statistical analysis of physician cost of care data.
  
  
  METHODS
  Commercial preferred provider organization (PPO) claims data for internists from a large metropolitan area.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
authors.affiliation: Clinical Analytics, UnitedHealthcare, 5901 Lincoln Drive, Edina, MN 55436, USA. Brent_a_metfessel@uhc..com
authors.forename: Brent A
authors.initials: BA
authors.lastname: Metfessel
authors.forename: Robert A
authors.initials: RA
authors.lastname: Greene
mesh_terms: D000465:Algorithms; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006801:Humans; D010818:Practice Patterns, Physicians' / Q000191:economics* / Q000706:statistics & numerical data*; D018709:Statistics, Nonparametric