Areas of Focus
Title: An Efficient Frontier Approach to Scoring and Ranking Hospital Performance
The Centers for Medicare and Medicaid Services (CMS) Star Ratings methodology for publicly evaluating hospitals uses a latent variable model that is based on the presumption of a single, but unobservable, hospital-specific quality factor shared across a group of performance measures. Performance measures are given higher weight if they statistically appear to be more strongly correlated with this hidden factor. We show how this approach, when applied to measures that are weakly or not correlated with each other, can effectively ignore measures and can exhibit "knife-edge" instability, so that even if hospitals improve relative to all other hospitals, they may nonetheless score lower overall due to weight shifting onto different measures than before.
In contrast, we provide an approach to scoring and ranking hospitals that, under reasonable conditions, ensures hospitals that improve relative to all other hospitals obtain higher scores, while also having the capability to autonomously adjust weights as measures are added or subtracted over time. Rather than exploit statistical correlation, we propose a conic optimization framework that offers a new integrated approach in Data Envelopment Analysis (DEA) for simultaneous efficiency analysis and performance evaluation. We develop theory that explains the behavior of our approach, including various properties satisfied by hospital scores at optimality. Using data we apply our approach to score and rank nearly every hospital in the United States, and demonstrate the extent to which it agrees or disagrees with the existing approach to the CMS Star Ratings.
Daniel Adelman, PhD., is the Charles I. Clough Jr. Professor of Operations Management at the University of Chicago Booth School of Business. He leads the Healthcare Analytics Laboratory at Chicago Booth, in which teams of graduate students and post-doctoral researchers work on real-world projects with providers to improve healthcare delivery through the analysis of large datasets. Adelman also developed and teaches a course on Healthcare Data Analytics that draws students from around the University of Chicago campus. He has published research based on work with various industries, including the electricity smart grid, gasoline supply chains, software-release planning, intermodal logistics, manufacturing, revenue management/pricing, and healthcare delivery. He publishes regularly in leading academic journals, and he is Area Editor for the Operations and Supply Chains Area of Operations Research. He teaches in Chicago Booth's Executive MBA Program, and in Booth’s Executive Program for Emerging Leaders. He serves on the faculty advisory board of the Harry L. Davis Center for Leadership. Adelman received a PhD in Industrial Engineering and Operations Research in 1997 from the Georgia Institute of Technology, where he also received a bachelor's degree in industrial engineering and a Master of Science in Operations Research. Adelman joined the Chicago Booth faculty in 1997.
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