在医院实施人工智能以实现学习型医疗系统:对当前的启用和障碍进行系统审查
Amir Kamel Rahimi1,2, Oliver Pienaar3, Moji Ghadimi3
1Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
Journal of medical Internet research
|August 2, 2024
概括
在医疗保健中实施人工智能 (AI) 面临挑战,特别是实时数据. 本系统性审查确定了医院人工智能集成的关键推动因素和障碍,为成功采用学习健康系统 (LHS) 提供了一个框架.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数字化健康转型
背景情况:
- 电子医疗记录 (EMR) 为学习健康系统 (LHS) 提供计算能力.
- 医疗保健中的人工智能 (AI) 承诺改善结果,但实时EMR集成很少见.
- 了解推动者和障碍者对于将人工智能使用从数据集转移到实时临床工作流至关重要.
研究的目的:
- 进行系统的文献审查,以确定在医院中实现真实世界的AI实施的推动因素和障碍.
- 地图识别了3个地平线框架的因素,以指导数字健康转型并实现LHS.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 搜索了PubMed,Scopus,科学网和IEEE Xplore (2010年1月至2022年1月). 在这些网站上搜索.
- 包括使用医院EMR数据的AI实施案例研究/指南;不包括初级/社区护理.
主要成果:
- 26项研究符合纳入标准,81%被评为质量差.
- 确定了28个推动因素 (8个新因素) 和18个障碍因素 (5个新因素),主要与信息和技术有关.
- 结果映射到一个为可操作的人工智能实施建议的3地平线框架.
结论:
- 显著的挑战阻碍了人工智能在医疗保健中的实施,特别是向实时数据的过渡.
- 一个带有已识别的支持者/障碍者的三地平线框架为人工智能分析集成提供了可行的建议.
- 研究结果可以指导医院的战略规划,以成功采用人工智能和实现LHS.
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