赛车机器:数据分析技术和种族化的卫生不公正的制度铭文
1California State University, Fullerton, CA, USA.
Journal of health and social behavior
|August 12, 2023
概括
关于社会因素的临床数据收集旨在减少不平等. 然而,在电子健康记录中标准化种族和种族数据可能会无意中加强现有的差异.
科学领域:
- 卫生公平研究 卫生公平研究
- 医学社会学 医学社会学
- 医疗信息学 医疗信息学
背景情况:
- 临床环境越来越多地被视为解决社会不平等问题的场所.
- 电子健康记录 (EHR) 和数据分析使得健康社会决定因素 (SDOH) 数据的收集成为可能.
- 需要了解医疗保健专业人员和机构如何看待不平等干预的数据标准化.
研究的目的:
- 检查在公共安全网卫生系统中种族,民族和语言数据收集的扩大.
- 探索医疗保健提供者,工作人员,数据科学家和管理人员关于数据标准化和不平等的观点.
主要方法:
- 10个月的民族学实地研究.
- 32次与各种各样的医疗保健系统人员进行定性访谈.
- 分析种族,民族和语言数据收集的实施情况.
主要成果:
- 电子数据捕获导致生物医学中的脱上下文的种族化.
- 卫生从业者和数据工作者在收集种族数据时使用生物学,文化和社会理由.
- 标准化数据收集可能依赖于并延续现有的偏见.
结论:
- 以数据为中心的干预措施,以解决医疗保健中的社会不公正问题,可能会悖论地加强它.
- 扩大SDOH数据收集需要对其实施和影响进行批判性审查.
- 分层化生物医疗强调了数据,技术和健康公平之间的复杂关系.
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