Dan Adelman is a leading expert in business analytics, helping firms and institutions deploy data and decision analysis to build world-class strategic and tactical management capabilities. He conducts research on foundations of the operations research field, as well as studies the link between operational performance metrics and financial performance. His current work focuses on improving healthcare delivery through data analytics, performance measurement, and hospital benchmarking.
He co-directs the Booth Healthcare Initiative and also leads the Healthcare Analytics Laboratory at Chicago Booth, in which teams of students work on real-world projects with providers to improve healthcare delivery through the analysis of large datasets. The lab has conducted projects in population health, hospital readmissions, bundled payment reimbursement, case mix optimization, hospital scheduling, nurse benchmarking, and others. He has served on the faculty advisory board of the Harry L. Davis Center for Leadership. He publishes regularly and has served as Area Editor for Operations Research, the flagship journal of the field. He teaches regularly in Chicago Booth's Executive MBA Program.
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. He is a recipient of the George B. Dantzig Prize for the best dissertation in any area of operations research and the management sciences that is innovative and relevant to practice.
Adelman joined the Chicago Booth faculty in 1997.
D. Adelman. "An Efficient Frontier Approach to Scoring and Ranking Hospital Performance.” Operations Research, Vol. 68, No.3, May-June 2020, pp. 762-792.
Abstract. The Centers for Medicare and Medicaid Services (CMS) star rating methodology for publicly evaluating hospitals uses a latent variable model that is based on the pre- sumption 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 because of weight shifting onto different measures than before. In contrast, we provide an approach to scoring and ranking hospitals that, under reasonable conditions, ensures that 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 for simultaneous efficiency analysis and performance evaluation. We develop theory that explains the behaviour of our approach, in- cluding 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.
D. Adelman. "Thousands Of Lives Could Be Saved In The US During The COVID-19 Pandemic If States Exchanged Ventilators.” Health Affairs, April 30, 2020.
It is thought that there are not enough mechanical ventilators in the U.S. for every patient who may need one during the coronavirus disease (COVID-19) pandemic. However, there is no analysis that measures the potential magnitude of the problem or proposes a solution. In this paper, I combine the pandemic forecasting model used by the federal government with estimates of ventilator availability from the literature to assess the expected shortage under various scenarios. I then propose that the federal government organize a national effort for ventilators to be exchanged between states to take advantage of the inter-temporal differences in demand peaks. I evaluate versions of this proposal, including use of the national stockpile, to estimate the number of lives that could be saved, and observe that it is potentially substantial. In the absence of other viable solutions, the government should begin this effort in earnest, and if not, preparations should be made for such coordination should the country face another pandemic in the future.
D. Adelman and C. Uçkun. “Dynamic Electricity Pricing to Smart Homes.” Operations Research 67(6): 1520-1542.
C.E. Tabit, M.J. Coplan, K.T. Spencer, C.F. Alcain, T. Spiegel, A.S. Vohra, D. Adelman, J. Liao, and R.M. Sanghani. “Cardiology Consultation in the Emergency Department Reduces Re-hospitalizations for Low-Socioeconomic Patients with Acute Decompensated Heart Failure,” The American Journal of Medicine, 2017 Sep; 130(9):1112.e17-1112.e31.
Adelman, D. and A. Mersereau. "Dynamic Capacity Allocation to Customers Who Remember Past Service." Management Science, Volume 59, Number 3, March 2013, pp. 592-612.
Adelman D. "Dynamic Bid-Prices in Revenue Management," Operations Research, 55 (2007).
For a listing of research publications, please visit the university library listing page.
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