title: Statistical methodology for classifying units on the basis of multiple-related measures.
abstract: Both the private and public sectors have begun giving financial incentives to healthcare providers, such as hospitals, delivering superior 'quality of care'. Quality of care is assessed through a set of disease-specific measures that characterize the performance of healthcare providers. These measures are then combined into a unidimensional composite score. Most of the programs that reward superior performance use raw averages of the measures as the composite score. The scores based on raw averages fail to take into account typical characteristics of data used for performance evaluation, such as within-patient and within-hospital correlations, variable number of measures available in different hospitals, and missing data. In this paper, we contrast two different versions of composites based on raw average scores with a model-based score constructed using a latent variable model. We also present two methods to identify hospitals with superior performance. The methods are illustrated using national data collected to evaluate quality of care delivered by the U.S. acute care hospitals.
authors.affiliation: Department of Biostatistics and Medical Informatics, CINTESIS, Faculty of Medicine, University of Porto, Porto, Portugal. tpinto@med.up.pt
authors.forename: Armando
authors.initials: A
authors.lastname: Teixeira-Pinto
authors.forename: Sharon-Lise T
authors.initials: SL
authors.lastname: Normand
mesh_terms: D001499:Bayes Theorem; D006333:Heart Failure / Q000191:economics / Q000628:therapy; D006761:Hospitals / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D009042:Motivation; D009203:Myocardial Infarction / Q000191:economics / Q000628:therapy; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D011787:Quality of Health Care / Q000191:economics / Q000706:statistics & numerical data; D014481:United States