An Administrative Claims Model Suitable for Profiling Hospital Performance Based on 30-Day Mortality Rates Among Patients With an Acute Myocardial Infarction - Krumholz, Harlan M.; Wang, Yun; Mattera, Jennifer A.; Wang, Yongfei; Han, Lein Fang; Ingber, Melvin J.; Roman, Sheila; Normand, Sharon-Lise T. (Circulation (New York, N.Y.), 2006)
Background— A model using administrative claims data that is suitable for profiling hospital performance for acute myocardial infarction would be useful in quality assessment and improvement efforts. We sought to develop a hierarchical regression model using Medicare claims data that produces hospital risk-standardized 30-day mortality rates and to validate the hospital estimates against those derived from a medical record model. Methods and Results— For hospital estimates derived from claims data, we developed a derivation model using 140 120 cases discharged from 4664 hospitals in 1998. For the comparison of models from claims data and medical record data, we used the Cooperative Cardiovascular Project database. To determine the stability of the model over time, we used annual Medicare cohorts discharged in 1995, 1997, and 1999–2001. The final model included 27 variables and had an area under the receiver operating characteristic curve of 0.71. In a comparison of the risk-standardized hospital mortality rates from the claims model with those of the medical record model, the correlation coefficient was 0.90 (SE=0.003). The slope of the weighted regression line was 0.95 (SE=0.007), and the intercept was 0.008 (SE=0.001), both indicating strong agreement of the hospital estimates between the 2 data sources. The median difference between the claims-based hospital risk-standardized mortality rates and the chart-based rates was <0.001 (25th and 75th percentiles, −0.003 and 0.003). The performance of the model was stable over time. Conclusions— This administrative claims-based model for profiling hospitals performs consistently over several years and produces estimates of risk-standardized mortality that are good surrogates for estimates from a medical record model.