数据在公共卫生和健康创新的作用:关于健康的社会决定因素,基于社区的数据方法和人工智能的观点
Kaakpema Yelpaala1, Michael Christopher Gibbons2, Ines Maria Vigil3
1Department of Health Policy and Management, Yale School of Public Health, 60 College Street, New Haven, CT, 06510, United States, 1 2037852867.
Journal of medical Internet research
|October 8, 2025
概括
公共卫生转型需要数据公平镜头来解决差距和基础设施侵蚀. 建议侧重于大数据,人工智能包容性,研究资金,HIPAA现代化,以及理解数据公平与健康公平的联系.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 由于全球数据的进步,公共卫生正在发生变化.
- 系统性健康差异需要数据公平的方法.
- 美国公共卫生数据基础设施的侵蚀加剧了紧迫性.
研究的目的:
- 总结来自耶鲁大学公共卫生学院召开的关于数据在公共卫生公平和创新的作用的见解.
- 为包容性数据实践确定可行的建议.
- 促进学术界,政府,医疗保健和工业界之间的跨部门合作.
主要方法:
- 召集跨部门利益相关者 (学术界,政府,医疗保健,工业).
- 主要演讲和圆桌会议讨论了健康的社会决定因素,人工智能对数据公平的影响以及基于社区的数据模型.
- 系统地收集信息的叙事综合.
主要成果:
- 确定了有关数据表示,人工智能的影响和社区数据模型的关键主题.
- 制定了一套可行的,跨领域的建议.
- 强调需要通过大数据和互操作性将健康的社会决定因素与健康结果联系起来.
结论:
- 实现大数据和互操作性对于连接健康和健康结果的社会决定因素至关重要.
- 让各种各样的,非技术性的声音参与到人工智能和健康讨论中是必不可少的.
- 现代化医疗保险可移植性和问责法案 (HIPAA) 对于人工智能和大数据是必要的,同时还需要资助数据公平和健康公平互联的研究.
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