Shi, Y., Mahdian, S., Blanchet, J. et al. Surgical scheduling via optimization and machine learning with long-tailed data. Health Care Manag Sci 26, 692–718 (2023). https://doi.org/10.1007/s10729-023-09649-0
Abstract
Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to reduce recovery unit congestion. We estimate the LOS and its probability distribution using machine learning models, schedule procedures on a rolling basis using a variety of optimization models, and estimate performance with simulation. The machine learning models achieved only modest LOS prediction accuracy, despite access to a very rich set of patient characteristics. Compared to the current paper-based system used in the hospital, most optimization models failed to reduce congestion without increasing wait times for surgery. A conservative stochastic optimization with sufficient sampling to capture the long tail of the LOS distribution outperformed the current manual process and other stochastic and robust optimization approaches. These results highlight …
Authors
Yuan Shi, Saied Mahdian, Jose Blanchet, Peter Glynn, Andrew Y Shin, David Scheinker
Publication date
2023/12
Journal
Health Care Management Science
Volume
26
Issue
4
Pages
692-718
Publisher
Springer US
