人工智能识别的风险患者的积极护理管理减少了可预防的入院
Ann C Raldow1, Naveen Raja, Chad W Villaflores
1Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, 200 Medical Plaza, Ste B-265, Los Angeles, CA 90095.
The American journal of managed care
|November 15, 2024
概括
通过人工智能 (AI) 识别的患者的积极护理管理显著减少了可预防的住院病例. 这种由人工智能驱动的方法对预防急性住院有望.
科学领域:
- 医疗保健管理的管理
- 医学中的人工智能
- 医疗服务研究 医疗服务研究
背景情况:
- 识别患有不良健康事件高风险的患者对于有效的医疗保健服务至关重要.
- 人工智能 (AI) 模型为识别风险患者群体提供了一种新的方法.
- 积极的护理管理旨在防止昂贵和可避免的医疗保健利用.
研究的目的:
- 评估主动护理管理在减少潜在可预防的急诊室 (ED) 访问和住院 (HA) 在AI模型确定为高风险的患者中的有效性.
- 评估人工智能驱动的护理管理干预对医疗保健利用模式的影响.
主要方法:
- 在48个加州大学洛杉矶分校的卫生诊所实施了一种阶梯集群随机设计.
- 由本土人工智能模型确定为有风险的患者被纳入研究.
- 用中断时间序列和细分回归分析来比较干预队伍中干预实施前后的利用趋势.
主要成果:
- 这项干预措施导致高风险和最高风险患者 (n=3007) 潜在可预防的住院病例 (HA) 在统计学上显著减少27%.
- 没有观察到可能可以预防的ED访问的显著减少,尽管注意到了减速的趋势.
- 总体而言,HAs和ED访问显示非显著的减少,表明针对可预防事件的有针对性的影响.
结论:
- 针对人工智能识别的风险患者的积极护理管理导致可预防的住院病例显著减少.
- 需要进一步的研究来探索人工智能和护理管理策略的整合,以防止急性医院遭遇.
- 人工智能驱动的风险识别与积极的护理管理相结合,有可能优化医疗保健资源配置并改善患者的治疗结果.
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