Data-Driven Patient Scheduling in Emergency Departments: A Hybrid Robust-Stochastic Approach

• 成果类型: Article
• 署名作者: He, Shuangchi; Sim, Melvyn; Zhang, Meilin
• 署名单位: National University of Singapore; National University of Singapore; Singapore University of Social Sciences (SUSS)
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2018.3145
• 发表日期: 2019
• 页码: 4123-4140
• 关键词: Healthcare Operations; Patient Scheduling; robust optimization; Stochastic Programming; mixed integer programming; queueing network

摘要

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 robustst-ochastic 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.