使用可穿戴和人工智能 (BROWNIE) 研究的燃烧预测:一个分散的数字健康协议来预测注册护士的燃烧
Angelina R Wilton1, Katharine Sheffield2, Quantia Wilkes2
1Dept. of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
BMC nursing
|February 12, 2024
概括
这项研究使用可穿戴设备和人工智能来预测和预防护士倦怠,旨在改善医疗保健工作者的福祉和患者的结果. 早期发现倦怠症可以导致及时干预和降低成本.
科学领域:
- 职业健康 职业健康 职业健康
- 数字健康数字健康
- 人工智能的人工智能
背景情况:
- 医疗保健工作者的倦怠会对个人的健康和患者的结果产生负面影响.
- 数据驱动技术为预测职业倦怠提供了潜力.
- 早期的倦怠预测使得预防性干预能够减轻后果.
研究的目的:
- 使用可穿戴技术和人工智能开发职业倦怠的预测模型.
- 估计与医疗保健专业人员的倦怠相关的成本.
- 为了实现可访问性,实施分散和异步的研究设计.
主要方法:
- 在培训,测试和验证队伍中招募360名注册护士 (RN).
- 收集每日智能手表数据 (心率,步骤,睡眠) 一年.
- 利用在线调查和行政系统数据进行全面分析.
主要成果:
- 该研究协议是为分散和异步数据收集而设计的.
- 将采用新的参与策略,以确保参与者遵守规则并减少退出.
- 布朗尼研究旨在解决可穿戴设备研究中的挑战.
结论:
- 布朗尼研究协议是为了预测和预防护士倦怠而建立的.
- 预计这些发现将为支持医疗保健工作者的机构战略提供信息.
- 这项研究有助于了解倦怠对医疗保健系统的影响.
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