Hospital-Wide Inpatient Flow Optimization

• 成果类型: Article
• 署名作者: Bertsimas, Dimitris; Pauphilet, Jean
• 署名单位: Massachusetts Institute of Technology (MIT); University of London; London Business School
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.4933
• 发表日期: 2024
• 页码: 4893-4911
• 关键词: hospital operations; flow management; Machine Learning; multistage robust optimization

摘要

An ideal that supports quality and delivery of care is to have hospital operations that are coordinated and optimized across all services in real time. As a step toward this goal, we propose a multistage adaptive robust optimization approach combined with machine learning techniques. 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, and 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 the 600 bed institution, our optimization model was solved in seconds and reduced off-service placement by 24% on average and boarding delays in the emergency department and post anesthesia units by 35% and 18%, respectively. We also illustrate the benefit of using adaptive linear decision rules instead of static assignment decisions.