在医疗保健组织中实施机器学习应用:经验研究的系统审查
Luigi M Preti1, Vittoria Ardito1, Amelia Compagni1,2
1Center for Research on Health and Social Care Management (CERGAS), SDA Bocconi School of Management, Milan, Italy.
在医疗保健中实施机器学习 (ML) 需要解决IT基础设施和文化等组织因素,以及ML设计和利益相关者参与. 成功的ML采用取决于这些横向数字健康实施策略.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 实施科学 实施科学
背景情况:
- 机器学习 (ML) 对医疗保健有很大的希望,但其在临床环境中的采用面临着重大实施挑战.
- 关于如何有效应对这些挑战,以广泛整合ML的知识有限.
研究的目的:
- 使用实施研究综合框架 (CFIR) 检查临床实践中的ML应用特征和实施过程.
- 综合医疗机构用于成功实施ML的策略.
主要方法:
- 从2013年到2023年,根据PRISMA指南进行了一项系统的文献审查.
- 在PubMed,Scopus和Web of Science的搜索中使用了与人工智能,实施和医疗保健相关的关键词.
- 用专题分析和编码分析了临床环境中ML实施的实证研究.
主要成果:
- 综合了34项研究,其中大部分是在2019年之后发表的,并在医院中实施,支持预后和诊断.
- 影响实施成功的关键因素包括内部设置元素 (知识获取,IT基础设施,文化),ML创新特征 (设计,相对优势,复杂性) 和过程/外部设置元素 (利益相关者参与,评估,领导).
结论:
- 在医疗保健中成功的ML实施取决于解决ML特定因素 (例如,信任) 和横向数字健康实施因素.
- 需要进一步的研究来澄清组织层面的因素,支持在医疗保健环境中采用ML应用.
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