Data-Driven Hospital Admission Control: A Learning Approach

Mohammad Zhalechian (), Esmaeil Keyvanshokooh (), Cong Shi () and Mark P. Van Oyen ()
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Mohammad Zhalechian: Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405
Esmaeil Keyvanshokooh: Information and Operations Management, Mays Business School, Texas A&M University, College Station, Texas 77845
Cong Shi: Management Science, Herbert Business School, University of Miami, Coral Gables, Florida 33146
Mark P. Van Oyen: Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48105

Operations Research, 2023, vol. 71, issue 6, 2111-2129

Abstract: The choice of care unit upon admission to the hospital is a challenging task because of the wide variety of patient characteristics, uncertain needs of patients, and limited number of beds in intensive and intermediate care units. The care unit placement decisions involve capturing the trade-off between the benefit of better health outcomes versus the opportunity cost of reserving higher level of care beds for potentially more complex patients arriving in the future. By focusing on reducing the readmission risk of patients, we develop an online algorithm for care unit placement under the presence of limited reusable hospital beds. The algorithm is designed to (i) adaptively learn the readmission risk of patients through batch learning with delayed feedback and (ii) choose the best care unit placement for a patient based on the observed information and the occupancy level of the care units. We prove that our online algorithm admits a Bayesian regret bound. We also investigate and assess the effectiveness of our methodology using hospital system data. Our numerical experiments demonstrate that our methodology outperforms different benchmark policies.

Keywords: Operations and Supply Chains; online learning; bandit; regret analysis; data-driven admission control; readmission (search for similar items in EconPapers)
Date: 2023
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