Abstract: Optimizing emergency department (ED) nurse staffing decisions to balance the quality of service and staffing cost 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-rate uncertainty by utilizing demand forecasts. In this work, we study a two-stage prediction framework that is synchronized with the base (made months in advance) and surge (made nearly real-time) staffing decisions in the ED. We quantify the benefit of the more expensive surge staffing. We also propose a near-optimal two-stage staffing policy that is straightforward to interpret and implement. Lastly, we develop a unified framework that combines parameter estimation, real-time demand forecasts, and staffing in the ED. High fidelity ED simulation experiments demonstrate that the proposed framework can reduce staffing costs by 8% – 17% while guaranteeing timely access to care. This is joint work with Yue Hu and Carri Chan.

Bio: Jing Dong is the Regina Pitaro Associate Professor of Business in the Decision, Risk, and Operations Division at Columbia Business School. Her research interests are at the interface of applied probability and service operations management. Her current research focuses on developing data-driven stochastic modeling to improve patient flow in healthcare delivery systems. She received an NSF CAREER Award in 2020. She currently serves as an associate editor for M&SOM and Mathematics of Operations Research.