开发和整合机器学习算法来识别外围动脉疾病:多利益相关者定性研究
Sabrina M Wang1, H D Jeffry Hogg2,3, Devdutta Sangvai4
1Duke University School of Medicine, Durham, NC, United States.
JMIR formative research
|September 21, 2023
概括
整合机器学习 (ML) 驱动的临床决策支持 (CDS) 用于外围动脉疾病 (PAD) 识别需要非技术因素,如领导和用户需求. 解决组织和公平挑战对于现实世界的成功至关重要.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
背景情况:
- 机器学习 (ML) 驱动的临床决策支持 (CDS) 显示出改善医疗保健质量的承诺,但面临实施挑战.
- 基于ML驱动的CDS集成的现实结果是混合的,需要对影响因素进行进一步的调查.
研究的目的:
- 探索影响外围动脉疾病 (PAD) 识别算法的整合到临床工作流程中的因素.
- 了解如何实施及时,基于指南的护理,使用ML驱动的CDS用于PAD.
主要方法:
- 在ML驱动的CDS集成过程中,与技术,管理和临床利益相关者进行了12次半结构面试.
- 应用主题分析到伪名化成绩单,以确定与整合相关的关键主题.
- 专注于基于ML的CDS,以识别高概率PAD病例进行跨学科审查和初级保健提供者的建议.
主要成果:
- 成功将ML算法性能转化为现实世界的有效性取决于非技术因素:强大的临床领导力,可信的工作流程,最终用户关注度和可操作的问题识别.
- 整合的挑战包括缺乏语境整合,缺少反循环和数据孤岛.
- 确定了利益相关者特定的成功标准,突出了在CDS整合中的不同需求和团队动态.
结论:
- 纵向,多学科的利益相关者参与对于有效的ML驱动的CDS转化为现实世界护理至关重要.
- 除了技术方面之外,包括行政/运营领导者以及早期考虑临床医生的需求至关重要.
- 整体观点揭示了由ML驱动的CDS加剧的健康不平等;解决方案通常需要系统的,非ML干预措施来缓解PAD护理中的差异.
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