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阶层增强学习为公平的手术室安排在整形外科手术
Christoph Wallner1, Flemming Puscz1, Sonja Verena Schmidt1
1From the Department for Plastic and Hand Surgery, BG University Hospital Bergmannsheil Bochum, BG University Hospital Bergmannsheil, Ruhr University Bochum, Bochum, Germany.
Plastic and reconstructive surgery. Global open
|September 22, 2025
概括
一个新的分层强化学习 (HRL) 框架优化了手术室 (OR) 调度,减少了偏差,提高了外科手术培训公平性. 这种数据驱动的方法平衡了效率与外科医生公平的资源分配.
科学领域:
- 手术手术研究研究研究手术研究
- 医疗保健中的机器学习
- 健康 公平 卫生 公平
背景情况:
- 公平的手术室 (OR) 安排对于外科培训和资源优化至关重要.
- 传统的调度方法往往含有隐藏的偏见,导致资源分配不平等.
- 提出一个层次化的强化学习 (HRL) 框架来解决这些偏见.
研究的目的:
- 开发和评估一个HRL框架,以优化OR分配.
- 分析和改善手术安排中的公平性.
- 保持机构约束,同时提高效率和公平.
主要方法:
- 对一个整形外科服务 (24名外科医生) 的1.5年OR分配的回顾性分析.
- 利用随机森林模型与沙普利添加式解释来预测OR需求和量化特征的重要性.
- 实施了一个HRL架构 (近距离政策优化和深度Q学习) 来重新分配OR天,考虑各种约束.
主要成果:
- 基线调度显示出显著的不平等 (Gini = 10.39).
- HRL减少了不平等 (Gini = 0.22) 和平均绝对偏差 (33.6到25.9).
- OR暴露增加了研究员 (+50%) 和初级居民 (-14%),保持了100%规则合规的OR总容量.
结论:
- 该HRL框架提供了数据驱动的,公平的和符合约束的OR时间表.
- 这种方法平衡了手术培训中的运营效率与教育公平.
- 为了更广泛的适用性,建议进行多中心前性验证.
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