Recent scientific and policy initiatives frame clinical settings as sites for intervening upon inequality. Electronic health records and data analytic technologies offer opportunity to record standard data on education, employment, social support, and race-ethnicity, and numerous audiences expect biomedicine to redress social determinants based on newly available data. However, little is known on how health practitioners and institutional actors view data standardization in relation to inequity. This article examines a public safety-net health system's expansion of race, ethnicity, and language data collection, drawing on 10 months of ethnographic fieldwork and 32 qualitative interviews with providers, clinic staff, data scientists, and administrators. Findings suggest that electronic data capture institutes a decontextualized racialization within biomedicine as health practitioners and data workers rely on biological, cultural, and social justifications for collecting racial data. This demonstrates a critical paradox of stratified biomedicalization: The same data-centered interventions expected to redress injustice may ultimately reinscribe it.

中文翻译：

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与机器赛跑：数据分析技术和种族化健康不公正的制度铭文。


最近的科学和政策举措将临床环境视为干预不平等的场所。电子健康记录和数据分析技术提供了记录教育、就业、社会支持和种族民族标准数据的机会，许多受众期望生物医学能够根据新获得的数据纠正社会决定因素。然而，人们对卫生从业者和机构参与者如何看待与不平等相关的数据标准化却知之甚少。本文基于 10 个月的人种学实地调查以及对提供者、诊所工作人员、数据科学家和管理人员的 32 次定性访谈，探讨了公共安全网卫生系统对种族、族裔和语言数据收集的扩展。研究结果表明，电子数据采集在生物医学领域建立了一种脱离语境的种族化，因为卫生从业者和数据工作者依靠生物、文化和社会理由来收集种族数据。这证明了分层生物医学化的一个关键悖论：旨在纠正不公正现象的相同的以数据为中心的干预措施最终可能会重新定义不公正现象。