BACKGROUND: Individual quality measures have significant limitations for assessing surgical performance. Despite growing interest in composite measures, empirically-based methods for combining multiple domains of surgical quality are not well established. OBJECTIVE: To develop and validate a composite measure of surgical performance that best describes variation in hospital mortality rates and forecasts future performance. RESEARCH DESIGN: Using the national Medicare claims database, we identified all patients undergoing aortic valve replacement in 2000 to 2001 (n = 53,120). To serve as input variables, we identified hospital-level predictors of mortality with aortic valve replacement, including hospital volume, complication rates, and mortality with other procedures. Hospital-specific predicted mortality rates were then determined using Bayesian-derived modeling techniques and assessed against subsequent hospital mortality (2002-2003). RESULTS: Our composite measure explained 78% of the variation in aortic valve replacement mortality rates (2000-2001). The most important input variables were hospital volume, mortality with aortic valve replacement, and mortality for other high-risk cardiac procedures. The composite measure forecasted 70% of future hospital-level variation in mortality rates (2002-2003), and was substantially better in this regard than individual measures. Hospitals scoring in the bottom quintile on the composite measure in 2000 to 2001 had 2-fold higher mortality rates in 2002 to 2003 than hospitals in the top quintile (adjusted odds ratio, 1.97; 95% CI, 1.73-2.23). CONCLUSIONS: Compared with individual surgical quality indicators, empirically derived composite measures are superior in explaining variation in hospital mortality rates and in forecasting future performance. Such measures could be useful for public reporting, value-based purchasing, or benchmarking for quality improvement purposes.
BACKGROUND: Individual quality measures have significant limitations for assessing surgical performance. Despite growing interest in composite measures, empirically-based methods for combining multiple domains of surgical quality are not well established. OBJECTIVE: To develop and validate a composite measure of surgical performance that best describes variation in hospital mortality rates and forecasts future performance. RESEARCH DESIGN: Using the national Medicare claims database, we identified all patients undergoing aortic valve replacement in 2000 to 2001 (n = 53,120). To serve as input variables, we identified hospital-level predictors of mortality with aortic valve replacement, including hospital volume, complication rates, and mortality with other procedures. Hospital-specific predicted mortality rates were then determined using Bayesian-derived modeling techniques and assessed against subsequent hospital mortality (2002-2003). RESULTS: Our composite measure explained 78% of the variation in aortic valve replacement mortality rates (2000-2001). The most important input variables were hospital volume, mortality with aortic valve replacement, and mortality for other high-risk cardiac procedures. The composite measure forecasted 70% of future hospital-level variation in mortality rates (2002-2003), and was substantially better in this regard than individual measures. Hospitals scoring in the bottom quintile on the composite measure in 2000 to 2001 had 2-fold higher mortality rates in 2002 to 2003 than hospitals in the top quintile (adjusted odds ratio, 1.97; 95% CI, 1.73-2.23). CONCLUSIONS: Compared with individual surgical quality indicators, empirically derived composite measures are superior in explaining variation in hospital mortality rates and in forecasting future performance. Such measures could be useful for public reporting, value-based purchasing, or benchmarking for quality improvement purposes.
Authors: Michael Shwartz; Erol A Peköz; Cindy L Christiansen; James F Burgess; Dan Berlowitz Journal: Health Serv Res Date: 2012-06-20 Impact factor: 3.402
Authors: Ian J Barbash; Hongwei Zhang; Derek C Angus; Steven E Reis; Chung-Chou H Chang; Francis R Pike; Jeremy M Kahn Journal: Med Care Date: 2017-05 Impact factor: 2.983
Authors: Rohan Khera; Ambarish Pandey; Thomas Koshy; Colby Ayers; Brahmajee K Nallamothu; Sandeep R Das; Mark H Drazner; Michael E Jessen; Ajay J Kirtane; Timothy J Gardner; James A de Lemos; Deepak L Bhatt; Dharam J Kumbhani Journal: JAMA Cardiol Date: 2017-12-01 Impact factor: 14.676
Authors: Randal S Weber; Carol M Lewis; Scott D Eastman; Ehab Y Hanna; Olubumi Akiwumi; Amy C Hessel; Stephen Y Lai; Leslie Kian; Michael E Kupferman; Dianna B Roberts Journal: Arch Otolaryngol Head Neck Surg Date: 2010-12