Objective: To assess the value of a novel composite measure for identifying the best hospitals for major procedures.
Data source: We used national Medicare data for patients undergoing five high-risk surgical procedures between 2005 and 2008.
Study design: For each procedure, we used empirical Bayes techniques to create a composite measure combining hospital volume, risk-adjusted mortality with the procedure of interest, risk-adjusted mortality with other related procedures, and other variables. Hospitals were ranked based on 2005-2006 data and placed in one of three groups: 1-star (bottom 20 percent), 2-star (middle 60 percent), and 3-star (top 20 percent). We assessed how well these ratings forecasted risk-adjusted mortality rates in the next 2 years (2007-2008), compared to other measures.
Principal findings: For all five procedures, the composite measures based on 2005-2006 data performed well in predicting future hospital performance. Compared to 1-star hospitals, risk-adjusted mortality was much lower at 3-star hospitals for esophagectomy (6.7 versus 14.4 percent), pancreatectomy (4.7 versus 9.2 percent), coronary artery bypass surgery (2.6 versus 5.0 percent), aortic valve replacement (4.5 versus 8.5 percent), and percutaneous coronary interventions (2.4 versus 4.1 percent). Compared to individual surgical quality measures, the composite measures were better at forecasting future risk-adjusted mortality. These measures also outperformed the Center for Medicare and Medicaid Services (CMS) Hospital Compare ratings.
Conclusion: Composite measures of surgical quality are very effective at predicting hospital mortality rates with major procedures. Such measures would be more informative than existing quality indicators in helping patients and payers identify high-quality hospitals with specific procedures.
© Health Research and Educational Trust.