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Operations Research Vol. 71 No. 6 2023

Data-Driven Hospital Admission Control: A Learning Approach

Mohammad Zhalechian1; Esmaeil Keyvanshokooh2; Cong Shi3; Mark P. Van Oyen4

1 Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405; · 2 Information and Operations Management, Mays Business School, Texas A&M University, College Station, Texas 77845; · 3 Management Science, Herbert Business School, University of Miami, Coral Gables, Florida 33146; · 4 Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48105

Abstract

A Data-Driven Approach to Improve Care Unit Placements in Hospitals The choice of care unit upon hospital admission 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. These decisions require carefully weighing the benefits of improved health outcomes against the opportunity cost of reserving higher level care beds for potentially more complex patients arriving in the future. In “Data-Driven Hospital Admission Control: A Learning Approach,” Zhalechian, Keyvanshokooh, Shi, and Van Oyen introduce a data-driven algorithm to address this challenging task. By focusing on reducing the readmission risk of patients, the algorithm is designed to (i) adaptively learn the readmission risk of patients through batch learning with delayed feedback and (ii) determine the best care unit placement for a patient based on the observed information and occupancy levels to minimize total readmission risk. The algorithm is supported by a performance guarantee, and its effectiveness is showcased using real-world hospital system data.

DOI
10.1287/opre.2020.0481
Volume
71
Issue
6
Pages
2111-2129
Language
en
Sources
crossref openalex

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