确保公共卫生数据公平的十个核心概念
Yiran Wang1, Alicia E Boyd1, Lillian Rountree1
1Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut.
JAMA health forum
|January 9, 2026
概括
解决公共卫生中的数据不平等问题对于确保人工智能和移动健康进步有利于所有人群至关重要. 一个由10个核心概念组成的拟议框架,在整个数据生命周期中运行数据公平性,以减轻偏见和改善健康结果.
科学领域:
- 公共卫生 数据科学 数据科学
- 医疗信息学 医疗信息学
- 计算公共卫生 计算公共卫生
背景情况:
- 公共卫生决策越来越依赖于大规模数据和人工智能 (AI) 和移动健康等技术.
- 在健康数据集中,某些人群 (农村,残疾人,无家可归者,低收入/中等收入人群) 的代表性不足,导致有偏见的发现和低于最佳的结果.
- 解决数据不平等问题对于技术进步带来的公平健康结果至关重要.
研究的目的:
- 提出一个由10个核心概念组成的框架,以提高公共卫生研究和实践中的数据公平性.
- 将计算机科学原则 (公平,透明,隐私) 与公共卫生数据科学最佳实践相结合.
- 为整个数据生命周期中评估数据实践提供结构化的方法.
主要方法:
- 制定了一个框架,将计算机科学原则 (公平,透明,隐私) 与公共卫生数据科学最佳实践相结合.
- 在数据生命周期中应用的概念:研究设计,数据收集,分析,解释和政策翻译.
- 专注于减轻信息和选择偏见,学习因果关系,并确保可概括性.
主要成果:
- 提出了10个核心概念,以使公共卫生中的数据公平成为可行的.
- 该框架提供了一个结构化的方法来评估对所有人口的代表性和服务的数据实践.
- 数据公平性是可靠推断和可操作证据的基础.
结论:
- 数据公平对于确保技术和数字健康进步改善每个人的结果至关重要,而不是扩大现有的差距.
- 这10个核心概念提供了一种实用的方法,以使公共卫生中的数据公平性变得可行.
- 推进数据公平性需要在信息理论和结构变化方面做出平行努力,以便明智地做出决策.
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