研究手术住院时长预测精度与调度灵活性的权衡关系
Prediction accuracy versus rescheduling flexibility in elective surgery management
- 通过模拟不同预测误差场景，评估多种调度调整策略
- 预测误差越大，需调整治疗安排的患者比例越高
- 在资源紧张时，灵活调度可有效避免床位超载
下游资源可用性对择期手术患者入院计划至关重要，其中最关键的资源是住院床位。为确保床位可用，医院常使用机器学习（ML）模型预测患者住院时长（LOS）。然而，实际住院时长可能与预测值存在偏差，导致原排程不可行。为此可实施调度调整策略，利用运营灵活性进行补救，如推迟入院时间、转至不同病区或已入院患者跨病区转移。通常认为更精准的预测能减少调度调整需求，但训练高精度模型成本高昂。本文基于先前提出的模拟机器学习方法，系统研究了不同纠正政策下预测精度与调度灵活性的关系。重点分析在预测误差条件下，最有效的患者调度策略，以防止床位超载并优化资源利用率。
原文摘要 · Abstract (English)
The availability of downstream resources plays is critical in planning the admission of elective surgery patients. The most crucial one is inpatient beds. To ensure bed availability, hospitals may use machine learning (ML) models to predict patients' length-of-stay (LOS) in the admission planning stage. However, the real value of the LOS for each patient may differ from the predicted one, potentially making the schedule infeasible. To address such infeasibilities, it is possible to implement rescheduling strategies that take advantage of operational flexibility. For example, planners may postpone admission dates, relocate patients to different wards, or even transfer patients who are already admitted among wards. A straightforward assumption is that better LOS predictions can help reduce the impact of rescheduling. However, the training process of ML models that can make such accurate predictions can be very costly. Building on previous work that proposed simulated ML for evaluating data-driven approaches, this paper explores the relationship between LOS prediction accuracy and rescheduling flexibility across various corrective policies. Specifically, we examine the most effective patient rescheduling strategies under LOS prediction errors to prevent bed overflows while optimizing resource utilization
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