人工智能辅助的急诊室垂直患者流量优化
Nicole R Hodgson1, Soroush Saghafian2, Wayne A Martini1
1Department of Emergency Medicine, Mayo Clinic Arizona, Phoenix, AZ 85054, USA.
Journal of personalized medicine
|June 25, 2025
概括
人工智能和机器学习优化了急诊室患者流动. 一个新的协议减少了患者的等待时间,而不影响护理质量,提高了整体效率.
科学领域:
- 紧急医疗 紧急医疗
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 紧急诊所 (ED) 在优化患者流动方面面临着挑战.
- 人工智能和机器学习方面的进步为有针对性的操作改进提供了机会.
研究的目的:
- 评估AI/ML驱动的优化垂直处理途径 (VPP) 对ED患者吞吐量的影响.
- 评估ED停留时间 (LOS) 和质量指标的变化.
主要方法:
- 在选数据上训练了一种非线性ML模型,以预测VPP的适用性.
- 该模型为VPP协议提供了信息,并集成到队列理论框架中.
- 一项为期13周的前性试验比较了ED前后的性能数据.
主要成果:
- 优化的VPP协议将平均ED LOS降低了10.75分钟 (4.15%).
- 经过调整后的分析估计LOS减少在7.5到11.9分钟之间.
- 没有观察到对质量指标的不良影响,例如72小时的ED复诊或住院治疗.
结论:
- 由AI/ML驱动的VPP个性化显著提高ED吞吐量,并保持护理质量.
- 这种适应性方法通过响应ED和和患者敏度,超越了标准快速跟踪系统.
- 该方法可适应各种紧急护理设置的个性化患者流量优化.
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