美国医院电子健康记录系统中机器学习的采用:回顾性观察性研究
Huang Huang1, Wei Lyu2, Md Mahmud Hasan3
1Department of Health Management, Economics and Policy, School of Public Health, Augusta University, 2500 Walton Way, Science Hall, E-1031, Augusta, GA, 30904, United States, 1 8595519185.
Journal of medical Internet research
|December 9, 2025
概括
美国医院的机器学习 (ML) 采用率很高,75%的人在电子健康记录 (EHR) 中使用ML. 影响采用的关键因素包括医院规模,非营利性地位和EHR供应商合同,引发了对数字公平的担忧.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 医院管理局 医院管理局
背景情况:
- 在美国医疗保健中,机器学习 (ML) 的采用正在增加,特别是在电子健康记录 (EHR) 系统中.
- ML整合的决定因素及其与医院特征的关系需要进一步的实证研究.
研究的目的:
- 评估美国一般急性护理医院EHR系统中ML的当前采用情况.
- 确定与实施ML功能相关的特定医院特征.
主要方法:
- 利用美国医院协会年度调查 (2022-2023) 和其信息技术补充调查 (2023-2024) 的链接数据.
- 包括2562家通用和急症医院,两年来有4055次观察.
- 采用逆概率权重,描述性统计学和多变量逻辑回归来分析ML采用模式和相关医院特征.
主要成果:
- 大约75%的医院在EHR中采用了ML功能,经常将临床和操作应用整合在一起.
- 预测住院风险和门诊随访是最常见的ML功能.
- 与ML采用积极相关的因素包括非营利性地位,更大的医院规模,大都市位置,与领先的EHR供应商签订合同,以及健康系统的隶属.
结论:
- 医院的ML采用是由组织资源和战略优先事项驱动的,这可能会加剧数字不平等.
- 有限的ML模型评估实践需要加强监管监督和对资源不足的设施的支持.
- 解决ML采用和监督方面的差异对于公平,安全和有效地在医疗保健中实施AI至关重要.
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