在紧急护理中最大限度地提高效率:三重干预以最大限度地减少没有被看到的离开:一项观察性研究
Jessica J Kirby1, Heidi C Knowles1, Saba Asad2
1Department of Emergency Medicine, JPS Health Network, Fort Worth, TX 76104, USA.
Medicine
|February 19, 2025
概括
人工智能和机器学习确定了导致患者离开急诊室而不见的关键因素 (LWBS). 有针对性的干预措施显著降低了LWBS的发病率,约为60%.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 紧急医疗手术 紧急医疗手术
背景情况:
- 没有被看到 (LWBS) 是急诊室 (ED) 影响患者结果的关键质量指标.
- 识别和减轻导致LWBS的因素是复杂的,需要先进的分析方法.
- 之前的干预措施取得了不同程度的成功,因此需要采用数据驱动的方法.
研究的目的:
- 利用人工智能和机器学习 (AI/ML) 算法来识别LWBS的关键风险因素.
- 实施有针对性的三重干预措施,解决已识别的风险.
- 为了比较这些干预措施实施之前和之后的每日LWBS率.
主要方法:
- 追溯观察性研究分析了从2019年3月到2024年5月的每日ED吞吐量数据.
- 利用极端梯度提升 (XGBoost) 和随机森林AI/ML算法用于LWBS预测和风险因素识别.
- 实施了三重干预措施:快速分拣,直接安置,减少登机,随后对LWBS率进行了比较分析.
主要成果:
- 人工智能/ML模型在预测LWBS方面表现良好.
- 确定影响LWBS的关键因素是:分拣到床等待时间,登机时间和门到分拣时间.
- 平均每日LWBS率从干预前的4.82%下降到干预后的1.93% (P < .001),减少了大约60%.
结论:
- AI/ML方法在识别与EDs中LWBS相关的关键因素方面非常有效.
- 有针对性的三重干预措施解决分拣,床铺和登机时间显著降低每日LWBS率.
- 这项研究强调了AI/ML驱动的运营管理在改善ED患者流动和护理质量的效率.
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