将护士偏好集成到基于人工智能的调度系统中:定性研究
Fabienne Josefine Renggli1, Maisa Gerlach1, Jannic Stefan Bieri1
1School of Health Professions, Bern University of Applied Sciences, Bern, Switzerland.
JMIR formative research
|June 4, 2025
概括
将人工智能 (AI) 整合到护士安排中可以提高公平性和效率,但人类监督至关重要. 这项研究对护士对人工智能方法的偏好进行了映射,建议采取混合方法,以获得医疗人员的最佳结果.
科学领域:
- 医疗保健管理的管理
- 运营研究 运营研究
- 人工智能的人工智能
背景情况:
- 护士安排是一个关键的医疗保健挑战,影响患者护理和护士福祉.
- 传统的方法往往忽视护士的偏好,导致倦怠和高周转率.
- 不适当的安排实践会降低士气,并对患者的治疗结果产生负面影响.
研究的目的:
- 开发一个框架,将护士的偏好整合到人工智能支持的调度中.
- 收集护士和监督员对安排经验的定性见解.
- 将这些见解映射到合适的数学和基于人工智能的调度技术中.
主要方法:
- 焦点小组采访了21名瑞士医疗保健中的护士,监督员和临时工作人员.
- 使用开放和轴向编码进行定性数据分析,以确定关键主题.
- 绘图确定了人工智能方法的主题,如混合整数编程 (MIP),约束编程 (CP),遗传编程 (GP) 和强化学习 (RL).
主要成果:
- 公平与参与 (85%) 和灵活性/自主性 (76%) 是护士的主要优先事项.
- 人工智能集成被认为有利于效率和公平性 (62%),但存在对可靠性和人类监督 (38%) 的担忧.
- 特定的AI方法被绘制出来:MIP用于公平分配,CP用于复杂的规则,GP用于缺席,RL用于动态适应. 证明了初步的MIP实施.
结论:
- 人工智能支持的日程安排可以提高护理的公平性,透明度和效率.
- 解决有关人工智能的可靠性,适应性和人类监督方面的担忧至关重要.
- 将人工智能与人类决策相结合的混合方法可能是护士安排的最佳方法.
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