临床数据共享中的组织因素,用于医疗保健中的人工智能
Alaa Youssef1,2, Madelena Y Ng2, Jin Long3
1Department of Radiology, Stanford University School of Medicine, Stanford, California.
JAMA network open
|December 19, 2023
概括
有限的健康数据共享阻碍了AI的发展. 这项研究发现,受定制激励和跨部门合作影响的组织动机和能力是改善人工智能数据共享的关键.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数据科学数据科学数据科学
背景情况:
- 分享多样化的健康数据集对于医疗保健中人工智能 (AI) 算法的通用性至关重要.
- 由于缺乏对组织动机和障碍的理解,有限的数据共享阻碍了AI的发展.
研究的目的:
- 探索影响组织动机的因素,以共享健康数据用于AI开发.
- 确定与人工智能共享健康数据相关的特定行业驱动因素和障碍.
主要方法:
- 采用多个案例研究方法的定性研究.
- 27个半结构面试与领导者在学术,政府,非营利性和私营部门的数据共享角色进行.
- 代定性分析,绘制组织准备数据共享的概念.
主要成果:
- 组织对数据共享的准备是由动机 (价值调整,外部激励) 和能力 (基础设施,人员,专业知识,数据访问) 所定义的.
- 跨部门合作成为克服获取健康数据障碍的关键策略.
- 特定部门的因素显著影响卫生组织的数据共享行为.
结论:
- 量身定制的激励措施可以增强组织的动力,并促进人工智能开发的健康数据的可持续流动.
- 外部激励对于加强跨部门合作和克服数据访问障碍至关重要.
- 了解和解决特定部门因素对于促进人工智能中的健康数据共享至关重要.
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