Prediction-driven Surge Planning with Application in the Emergency Department
Lunch will be served at 11:45 AM.
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. 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.
Jing Dong is the Regina Pitaro Associate Professor of Business in the Decision, Risk, and Operations Division at Columbia Business School. Her research is at the interface of applied probability and service operations management, with a special focus on patient flow management in healthcare delivery systems. She received her Ph.D. in Operations Research from Columbia University. Before joining Columbia Business School, she was on the faculty of Northwestern University.