A probability metric for identifying high-performing facilities: an application for pay-for-performance programs.

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. CONCLUSION The probability metric has potential but needs to be evaluated by stakeholders in different types of delivery systems.