Please use this identifier to cite or link to this item: https://doi.org/10.1287/mnsc.2018.3145
Title: Data-Driven Patient Scheduling in Emergency Departments: A Hybrid Robust-Stochastic Approach
Authors: Shuangchi He 
Melvyn Sim 
Meilin Zhang 
Issue Date: 2019
Publisher: INFORMS Inst.for Operations Res.and the Management Sciences
Citation: Shuangchi He, Melvyn Sim, Meilin Zhang (2019). Data-Driven Patient Scheduling in Emergency Departments: A Hybrid Robust-Stochastic Approach. Management Science 65 (9) : 4123-4140. ScholarBank@NUS Repository. https://doi.org/10.1287/mnsc.2018.3145
Abstract: Emergency care necessitates adequate and timely treatment, which has unfortunately been compromised by crowding in many emergency departments (EDs). To address this issue, we study patient scheduling in EDs so that mandatory targets imposed on each patient's door-to-provider time and length of stay can be collectively met with the largest probability. Exploiting patient flow data from the ED, we propose a hybrid robust-stochastic approach to formulating the patient scheduling problem, which allows for practical features, such as a time-varying patient arrival process, general consultation time distributions, and multiple heterogeneous physicians. In contrast to the conventional formulation of maximizing the joint probability of target attainment, which is computationally excruciating, the hybrid approach provides a computationally amiable formulation that yields satisfactory solutions to the patient scheduling problem. This formulation enables us to develop a dynamic scheduling algorithm for making recommendations about the next patient to be seen by each available physician. In numerical experiments, the proposed hybrid approach outperforms both the sample average approximation method and an asymptotically optimal scheduling policy.
Source Title: Management Science
URI: https://scholarbank.nus.edu.sg/handle/10635/184680
ISSN: 00251909
DOI: 10.1287/mnsc.2018.3145
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