Racing the Machine: Data Analytic Technologies and Institutional Inscription of Racialized Health Injustice

Abstract

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.

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“…Furthermore, race is associated with socioeconomic status, which impacts one’s health due to the environments in which patients live and work, their medical care, and their access to nutritious food [ 22 ]. Many people of the same race or ethnicity may eat similar foods, have the same beliefs and views, or access to healthcare services [ 16 ]. Thus, excluding race from medical records is problematic since this ignores disease-specific health disparities amongst different races and limits the ability to perform retrospective research or choose patients to participate in prospective trials.…”