OBJECTIVES: To develop a model for case-mix adjustment of Consumer Assessment of Healthcare Providers and Systems (CAHPS) Hospital survey responses, and to assess the impact of adjustment on comparisons of hospital quality. DATA SOURCES: Survey of 19,720 patients discharged from 132 hospitals. METHODS: We analyzed CAHPS Hospital survey data to assess the extent to which patient characteristics predict patient ratings ("predictive power") and the heterogeneity of the characteristics across hospitals. We combined the measures to estimate the impact of each predictor ("impact factor") and selected high impact variables for adjusting ratings from the CAHPS Hospital survey. PRINCIPLE FINDINGS: The most important case-mix variables are: hospital service (surgery, obstetric, medical), age, race (non-Hispanic black), education, general health status (GHS), speaking Spanish at home, having a circulatory disorder, and interactions of each of these variables with service. Adjustment for GHS and education affected scores in each of the three services, while age and being non-Hispanic black had important impacts for those receiving surgery or medical services. Circulatory disorder, Spanish language, and Hispanic affected scores for those treated on surgery, obstetrics, and medical services, respectively. Of the 20 medical conditions we tested, only circulatory problems had an important impact within any of the services. Results were consistent for the overall ratings of nurse, doctor, and hospital. Although the overall impact of case-mix adjustment is modest, the rankings of some hospitals may be substantially affected. CONCLUSIONS: Case-mix adjustment has a small impact on hospital ratings, but can lead to important reductions in the bias in comparisons between hospitals.
OBJECTIVES: To develop a model for case-mix adjustment of Consumer Assessment of Healthcare Providers and Systems (CAHPS) Hospital survey responses, and to assess the impact of adjustment on comparisons of hospital quality. DATA SOURCES: Survey of 19,720 patients discharged from 132 hospitals. METHODS: We analyzed CAHPS Hospital survey data to assess the extent to which patient characteristics predict patient ratings ("predictive power") and the heterogeneity of the characteristics across hospitals. We combined the measures to estimate the impact of each predictor ("impact factor") and selected high impact variables for adjusting ratings from the CAHPS Hospital survey. PRINCIPLE FINDINGS: The most important case-mix variables are: hospital service (surgery, obstetric, medical), age, race (non-Hispanic black), education, general health status (GHS), speaking Spanish at home, having a circulatory disorder, and interactions of each of these variables with service. Adjustment for GHS and education affected scores in each of the three services, while age and being non-Hispanic black had important impacts for those receiving surgery or medical services. Circulatory disorder, Spanish language, and Hispanic affected scores for those treated on surgery, obstetrics, and medical services, respectively. Of the 20 medical conditions we tested, only circulatory problems had an important impact within any of the services. Results were consistent for the overall ratings of nurse, doctor, and hospital. Although the overall impact of case-mix adjustment is modest, the rankings of some hospitals may be substantially affected. CONCLUSIONS: Case-mix adjustment has a small impact on hospital ratings, but can lead to important reductions in the bias in comparisons between hospitals.
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