人工智能和机器学习增强电子健康记录系统对养老院质量衡量的影响:差异分析:差异分析
Meredith A Barrett1, Angier Allen1, Vy T Vuong1
1ResMed Science Center, San Diego, CA, USA.
概括
通过人工智能 (AI) 增强的电子健康记录 (EHR) 系统显著改善了美国养老院18项质量指标中的16项. 这种人工智能增强的EHR系统导致了更好的功能状态,减少了跌倒和日常援助的需求.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 老年人护理 医疗老年人护理
背景情况:
- 养老院在维持高质量的护理和监测居民福祉方面面临着挑战.
- 电子健康记录 (EHR) 已被广泛使用,但可以改进以提供更积极的患者管理.
- 人工智能和机器学习 (AI/ML) 的整合有可能改善临床决策和患者的治疗结果.
研究的目的:
- 评估EHR系统增强AI/ML (EHR+AI) 对美国养老院质量指标的影响.
- 评估EHR+AI是否与传统的EHR系统相比改善了居民的功能状态,减少了跌倒,并影响了药物使用.
主要方法:
- 使用差异差异 (DiD) 研究设计.
- 该研究包括218个养老院 (94个EHR+AI,124个只有EHR) 在11个季度 (6个干预前,5个干预后).
- 用线性混合效应模型分析了18个医疗保险和医疗补助服务中心 (CMS) 的质量指标,并根据基线差异进行了调整.
主要成果:
- 在18项质量指标中的16项 (89%) 中,EHR+AI站点在统计学上显著改善.
- 显著的改善包括更大减少重大跌倒 (-9%) 和居民需要帮助日常活动 (-22%).
- 此外,EHR+AI还导致了居民改善功能 (5%) 的更高增长,抑郁症状和使用某些药物的减少.
结论:
- 用AI/ML增强的EHR系统可以大大提高养老院护理的质量和效率.
- 实时监测和人工智能驱动的警报有助于及时的临床修改和更好的住院结果.
- 需要进一步的研究来探索AI在长期护理环境中的全部潜力和实施策略.
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