title: Application of a propensity score approach for risk adjustment in profiling multiple physician groups on asthma care.
abstract: OBJECTIVE
  To develop a propensity score-based risk adjustment method to estimate the performance of 20 physician groups and to compare performance rankings using our method to a standard hierarchical regression-based risk adjustment method.
  
  
  METHODS
  Mailed survey of patients from 20 California physician groups between July 1998 and February 1999.
  
  
  METHODS
  A cross-sectional analysis of physician group performance using patient satisfaction with asthma care. We compared the performance of the 20 physician groups using a novel propensity score-based risk adjustment method. More specifically, by using a multinomial logistic regression model we estimated for each patient the propensity scores, or probabilities, of having been treated by each of the 20 physician groups. To adjust for different distributions of characteristics across groups, patients cared for by a given group were first stratified into five strata based on their propensity of being in that group. Then, strata-specific performance was combined across the five strata. We compared our propensity score method to hierarchical model-based risk adjustment without using propensity scores. The impact of different risk-adjustment methods on performance was measured in terms of percentage changes in absolute and quintile ranking (AR, QR), and weighted kappa of agreement on QR.
  
  
  RESULTS
  The propensity score-based risk adjustment method balanced the distributions of all covariates among the 20 physician groups, providing evidence for validity. The propensity score-based method and the hierarchical model-based method without propensity scores provided substantially different rankings (75 percent of groups differed in AR, 50 percent differed in QR, weighted kappa=0.69).
  
  
  CONCLUSIONS
  We developed and tested a propensity score method for profiling multiple physician groups. We found that our method could balance the distributions of covariates across groups and yielded substantially different profiles compared with conventional methods. Propensity score-based risk adjustment should be considered in studies examining quality comparisons.
authors.affiliation: Department of Health Policy and Management, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD 21205-1901, USA.
authors.forename: I-Chan
authors.initials: IC
authors.lastname: Huang
authors.forename: Constantine
authors.initials: C
authors.lastname: Frangakis
authors.forename: Francesca
authors.initials: F
authors.lastname: Dominici
authors.forename: Gregory B
authors.initials: GB
authors.lastname: Diette
authors.forename: Albert W
authors.initials: AW
authors.lastname: Wu
mesh_terms: D000328:Adult; D001249:Asthma / Q000628:therapy*; D019985:Benchmarking / Q000379:methods*; D002140:California; D003430:Cross-Sectional Studies; D005260:Female; D006123:Group Practice / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D017060:Patient Satisfaction / Q000706:statistics & numerical data*; D011785:Quality Assurance, Health Care / Q000379:methods* / Q000706:statistics & numerical data; D012044:Regression Analysis; D020379:Risk Adjustment / Q000379:methods*