通过机器学习和优化进行关节整形手术的智能调度,提高了手术室利用率
Johnathan Robert Lex1,2, Aazad Abbas3,2, Jay S Toor4
1University of Toronto Temerty Faculty of Medicine, Toronto, Ontario, Canada johnathanlex@gmail.com.
BMJ health & care informatics
|January 23, 2026
概括
机器学习和优化显著改善了关节和膝关节置换手术安排. 这种数据驱动的方法减少了手术室未充分利用,增加了患者吞吐量,最大限度地提高了资源效率.
科学领域:
- 整形外科手术 整形外科手术
- 医疗信息学 医疗信息学
- 运营研究 运营研究
背景情况:
- 整体关节和膝关节整形术 (THA和TKA) 是有效的,但资源密集的程序.
- 有效的外科手术安排对于医院资源管理和成本效益至关重要.
研究的目的:
- 与传统方法相比,评估基于机器学习 (ML) 和数学优化手术计划的效率.
- 评估 ML 驱动的调度对手术室 (OR) 使用率和患者吞吐量的影响.
主要方法:
- 利用了来自15,267个初级和修订THA和TKA病例的数据 (2012年4月至2022年2月).
- 开发了特定程序的ML模型来预测操作时间.
- 采用整数线性编程来优化OR利用,并将其与历史的调度实践进行比较.
主要成果:
- 与滚动平均相比,ML模型在预测操作时间方面取得了7.1%的改进.
- 优化日程安排使OR未充分利用率降低了56.2% (13.3分钟/天),案件完成率增加了6.1% (31 OR天).
- 超时只增加了17.2% (3.6分钟/天) 的ML优化时间表.
结论:
- ML预测的操作时间和优化策略可以显著减少OR置时间.
- 数据驱动的外科手术安排最大限度地利用现有的医院资源,提高患者的吞吐量.
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