Paper Info
Title
Data-Driven Patient Scheduling in Emergency Departments: A Hybrid Robust-Stochastic Approach
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 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.
Year
DOI
Venue
2019
10.1287/mnsc.2018.3145
MANAGEMENT SCIENCE
Keywords
Field
DocType
healthcare operations,patient scheduling,robust optimization,stochastic programming,mixed integer programming,queueing network
Economics,Mathematical optimization,Data-driven,Scheduling (computing),Robust optimization,Crowding,Integer programming,Stochastic programming
Journal
Volume
Issue
ISSN
65
9
0025-1909
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Shuangchi He11389.31
Melvyn Sim21909117.68
Meilin Zhang330.73