Dynamic Care Unit Placements Under Unknown Demand with Learning

Arlen Dean (), Mohammad Zhalechian () and Mark P. Van Oyen ()
Additional contact information
Arlen Dean: Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130
Mohammad Zhalechian: Department of Operations & Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405
Mark P. Van Oyen: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48105

Manufacturing & Service Operations Management, 2025, vol. 27, issue 5, 1396-1414

Abstract: Problem definition : Care units are the facilities where admitted hospital patients receive treatment and monitoring services. This paper studies the problem of deciding which patients to place into the various available care units at any time. To determine placements in practice, hospitals rely on clinicians to discern a patient’s care needs and appropriately trade-off between future demand and limited bed availability. Making the right decisions remains challenging because patients are heterogeneous, and demand is uncertain. Methodology/results : We develop a dynamic resource allocation algorithm to decide unit placements by learning the care needs of different patient types. We model hospital beds as reusable resources and assume decision feedback is not immediately available, but rather delayed for an unknown and random length of time. Lastly, we consider the demand to be unknown and allow patient arrivals to be arbitrarily sequenced for robustness. The applicability of our algorithm is demonstrated with real-patient data from a hospital collaboration, where we evaluate our proposed approach using unplanned readmission rates as the performance metric. From extensive simulations, our results suggest the proposed algorithm tends to outperform several greedy benchmarks as well as a hospital benchmark model. A theoretical performance guarantee for our algorithm is provided to complement the case study. Managerial implications : This paper contributes new insights into designing dynamic decision-making models for hospital admissions operations. Our work presents a simple but effective data-driven support tool to help clinicians trade-off between available bed capacity and a patient’s care needs when making care unit placements. We also demonstrate how our algorithm can support the reduction of unplanned readmissions through improved placement decisions.

Keywords: care unit placements; reusable resource allocation; online learning; hospital readmissions; bed capacity management (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/msom.2022.0260 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:27:y:2025:i:5:p:1396-1414

Access Statistics for this article

More articles in Manufacturing & Service Operations Management from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().