title: A probability metric for identifying high-performing facilities: an application for pay-for-performance programs.
abstract: BACKGROUND
  Two approaches are commonly used for identifying high-performing facilities on a performance measure: one, that the facility is in a top quantile (eg, quintile or quartile); and two, that a confidence interval is below (or above) the average of the measure for all facilities. This type of yes/no designation often does not do well in distinguishing high-performing from average-performing facilities.
  
  
  OBJECTIVE
  To illustrate an alternative continuous-valued metric for profiling facilities--the probability a facility is in a top quantile--and show the implications of using this metric for profiling and pay-for-performance.
  
  
  METHODS
  We created a composite measure of quality from fiscal year 2007 data based on 28 quality indicators from 112 Veterans Health Administration nursing homes. A Bayesian hierarchical multivariate normal-binomial model was used to estimate shrunken rates of the 28 quality indicators, which were combined into a composite measure using opportunity-based weights. Rates were estimated using Markov Chain Monte Carlo methods as implemented in WinBUGS. The probability metric was calculated from the simulation replications.
  
  
  RESULTS
  Our probability metric allowed better discrimination of high performers than the point or interval estimate of the composite score. In a pay-for-performance program, a smaller top quantile (eg, a quintile) resulted in more resources being allocated to the highest performers, whereas a larger top quantile (eg, being above the median) distinguished less among high performers and allocated more resources to average performers.
  
  
  CONCLUSIONS
  The probability metric has potential but needs to be evaluated by stakeholders in different types of delivery systems.
authors.affiliation: *Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System (152M) †School of Management ‡School of Public Health §Department of Surgery, School of Medicine, Boston University ∥Center for Healthcare Organization and Implementation Research, Bedford VA Hospital, Bedford, MA.
authors.forename: Michael
authors.initials: M
authors.lastname: Shwartz
authors.forename: Erol A
authors.initials: EA
authors.lastname: Peköz
authors.forename: James F
authors.initials: JF
authors.lastname: Burgess
authors.forename: Cindy L
authors.initials: CL
authors.lastname: Christiansen
authors.forename: Amy K
authors.initials: AK
authors.lastname: Rosen
authors.forename: Dan
authors.initials: D
authors.lastname: Berlowitz
mesh_terms: D001499:Bayes Theorem; D019985:Benchmarking / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D011336:Probability; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data*; D011787:Quality of Health Care / Q000592:standards*; D012052:Reimbursement, Incentive / Q000706:statistics & numerical data*; D014481:United States; D014493:United States Department of Veterans Affairs