Prediction-Driven Surge Planning with Application in the Emergency Department
Yue Hu, joint work with Carri W. Chan and Jing DongGraduate School of Business Columbia Business School
Determining 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 uncertainty by utilizing 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) 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 demand stochasticity. 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 capacity sizing in the ED. High-fidelity simulation experiments for the ED demonstrate that the proposed framework can reduce annual staffing costs by 11%–16% ($2 M–$3 M) while guaranteeing timely access to care.
Yue Hu is a PhD Candidate at the Decision, Risk and Operations Division at the Graduate School of Business, Columbia University. Her research lies at the intersection of healthcare operations management, applied probability, and optimal control theory. With particular focus on patient-flow management and capacity sizing in healthcare delivery systems, she studies how to leverage predictive analytics to guide operational strategies and innovations. In addition to solving practically relevant problems, she conducts research in developing new methodologies for the approximation and control of stochastic systems.
Hu’s research has been recognized in a number of competitions, including as the winner of the INFORMS APS Best Student Paper Award in 2020, finalist of the INFORMS IBM Best Student Paper Award in 2019, and honorable mention in the INFORMS Undergraduate Operations Research Prize in 2017. Prior to pursuing her PhD, she received a BS from the Department of Industrial Engineering and Management Sciences at Northwestern University.