Prediction-Driven Surge Planning with Application to Emergency Department Nurse Staffing
Management Science · 2025 · Yue Hu、Carri W. Chan、Jing Dong
中文摘要
如何在服务质量与人员配置成本之间取得平衡,以确定急诊科 (ED) 的护士人员配置决策,可能极具挑战性,尤其是在患者需求存在高度不确定性时。数据可得性的不断提高与预测分析的持续进步,为利用需求预测来缓解需求不确定性提供了机会。本文研究一个两阶段预测驱动的人员配置框架,其中预测模型与急诊科的基础人员配置 (提前数周作出) 和激增人员配置 (近乎实时作出) 决策相整合。我们量化了具备使用更昂贵的激增人员配置之能力所带来的收益,并识别出平衡需求不确定性与系统随机性的重要性。我们还提出一种易于解释和实施的近乎最优的两阶段人员配置策略。最后,我们构建了一个将参数估计、实时需求预测与急诊科护士人员配置相结合的统一框架。针对急诊科的高保真仿真实验表明,所提出的框架有望在保证患者及时获得医疗服务的同时,将年度人员配置成本降低 10%–16% (200 万至 300 万美元)。
Abstract
Determining emergency department (ED) nurse staffing decisions to balance quality of service and staffing costs can be extremely challenging, especially when there is a high level of uncertainty in patient demand. Increasing data availability and continuing advancements in predictive analytics provide an opportunity to mitigate demand uncertainty by using demand forecasts. In this work, we study a two-stage prediction-driven staffing framework where the prediction models are integrated with the base (made weeks in advance) and surge (made nearly real-time) nurse staffing decisions in the ED. We quantify the benefit of having the ability to use the more expensive surge staffing and identify the importance of balancing demand uncertainty versus system stochasticity. We also propose a near-optimal two-stage staffing policy that is straightforward to interpret and implement. Last, we develop a unified framework that combines parameter estimation, real-time demand forecasts, and nurse staffing in the ED. High-fidelity simulation experiments for the ED demonstrate that the proposed framework has the potential to reduce annual staffing costs by 10%–16% ($2 M–$3 M) while guaranteeing timely access to care.