发表机构
United Airlines; Department of Statistics and Operations Research, University of North Carolina at Chapel Hill（联合航空; 统计与运筹学系，北卡罗来纳大学教堂山分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对急诊科拥挤问题，提出基于马尔可夫决策过程的框架，利用患者信息和床位可用性主动申请住院床位，得出三种数据驱动策略。模拟评估显示可减少住院患者等待时间和所有患者住院时间，报童启发式权衡最佳，强化学习启发式在特定情况更优，展示了预测工具对改善急诊科运营的价值。
AI 中文摘要
急诊科拥挤是由于患者在等待住院床位时滞留在急诊科。我们提出一个框架，利用当前患者信息和床位可用性在最终入院决定前主动申请住院床位，以减少急诊科拥挤和住院时间。将问题表述为马尔可夫决策过程，据此得出基于近似动态规划、强化学习和报童模型的三种数据驱动策略。通过模拟模型评估，结果显示主动床位申请可减少住院患者平均等待时间30%-70%，所有急诊科患者平均住院时间6%-15%，且仅产生适度床位闲置时间。报童启发式方法在急诊科表现和住院床位闲置时间间提供了最具吸引力的权衡，而强化学习启发式方法在下游医院流程稳定性特别重要时产生更平稳的床位申请模式。我们的工作展示了急诊科如何利用预测工具做出主动床位申请决策，改善急诊科运营，帮助管理者平衡急诊科延误减少和住院床位闲置时间。研究还说明了评估简单近视启发式方法和更复杂的基于强化学习的方法的价值，因为每种方法根据对管理者最重要的绩效指标和实施约束可提供不同优势。
英文摘要
Emergency department (ED) boarding occurs when admitted patients remain in the ED while awaiting inpatient beds. Boarding is a major driver of ED crowding and has been associated with poor patient outcomes. We propose a framework to help EDs reduce boarding time and length of stay by using information about current patients and bed availability to proactively request inpatient beds before admission decisions are finalized. We formulate the problem as a Markov decision process in which predictions of each patient's admission probability and time to disposition are aggregated to guide early inpatient bed requests. This formulation leads to three data-driven policies based on approximate dynamic programming, reinforcement learning, and a newsvendor-type approach. Using a simulation model based on data from a large ED, we evaluate these policies across a wide range of settings. The simulation study shows that proactive aggregate bed requests can reduce average boarding times for admitted patients by 30-70\% and average length of stay for all ED patients by 6-15\%, while creating only modest idle time for prepared inpatient beds. The newsvendor heuristic provides the most attractive tradeoff between ED performance and inpatient bed idle time, whereas the reinforcement learning heuristic produces smoother bed-request patterns when stability in downstream hospital processes is especially important. Our work shows how EDs can use prediction tools to make proactive bed-request decisions that improve ED operations while helping managers balance reductions in ED delays against inpatient bed idle time. Our findings also illustrate the value of evaluating both simple myopic heuristics and more sophisticated reinforcement learning-based approaches, since each can offer distinct advantages depending on the performance measures and implementation constraints most important to managers.
Comments51 pages, 8 figures
