Prediction-Driven Surge Planning with Application to Emergency Department Nurse Staffing
Yue Hu, Carri W. Chan, Jing Dong
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. This paper was accepted by David Simchi-Levi, healthcare management. Funding: J. Dong was partially supported by the Division of Civil, Mechanical and Manufacturing Innovation [Grant CMMI-1944209]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.02781 .