Data-Driven Patient Scheduling in Emergency Departments: A Hybrid Robust-Stochastic Approach
针对急诊科拥挤问题，提出混合鲁棒-随机方法优化患者调度，在满足门到医生时间和住院时长目标概率最大化的同时，兼顾时变到达、一般咨询时间分布等实际特征，数值实验优于传统方法。
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. This paper was accepted by Yinyu Ye, optimization.