Speaker: Jean Pauphilet
Date & Time: November 19, 2021, Friday, 13:30
Title: Hospital-wide Inpatient Flow Optimization
Abstract: To improve quality and delivery of care, operations need to be coordinated and optimized across all services in real-time. We propose a multi-stage adaptive robust optimization approach combined with machine learning techniques to achieve this goal. Informed by data and predictions, our framework unifies the bed assignment process across the entire hospital and accounts for present and future inpatient flows, discharges as well as bed requests – from the emergency department, scheduled surgeries and admissions, and outside transfers. We evaluate our approach through simulations calibrated on historical data from a large academic medical center. For a 600-bed institution, our optimization model can be solved in seconds, reduce off-service placement by 22% on average, and boarding delays in the emergency department and post-anesthesia units by 43% and 29% respectively. We also illustrate the benefit from using adaptive linear decision rules instead of static assignment decisions.
Bio: Jean is an Assistant Professor in Management Science and Operations at London Business School. His research focuses on large-scale discrete optimization, robust optimization, and machine learning, with applications in healthcare operations. His work has been published in the likes of Mathematical Programming, SIAM Journal on Optimization, and M&SOM, and recognized by many awards, including the INFORMS Pierskalla, George E. Nicholson, and Computing Society best student paper awards.
Jean received a Ph.D. in Operations Research from MIT and a Diplôme d’ingénieur from Ecole Polytechnique (Paris).