Assessing Geographical Variations in Hospital Processes of Care Using Multilevel Item Response Models

With health care reform passing in the United States, much effort is directed toward developing and disseminating comparative information on standardized processes of care for health care providers. We propose the use of Bayesian multilevel item response theory models to estimate hospital quality from multiple process measures and to assess geographical variation in hospital quality. Our approach fully incorporates the nesting structure of measures, patients, hospitals, and various levels of geographical units to provide a summary of hospital quality. A national dataset of patients treated for a heart attack, heart failure, or pneumonia illustrates our methods. We find considerable geographical differences in hospital quality for these conditions with variations across census regions and states accounting for slightly more than 10% of the total variation. Some states performed well for all three conditions (e.g., the respective posterior probabilities of having better than the national average performance was close to 1 in Iowa, New Jersey, South Dakota, and Wisconsin). In contrast, quality of other states varied across conditions (e.g., the corresponding posterior probability was close to 1 in Massachusetts for heart attack and heart failure quality, but less than 0.5 for pneumonia care). Our framework provides a comprehensive approach to assessment of hospital performance at both regional and national levels, and might be informative for policy development.