Racialized algorithms for kidney function: Erasing social experience

Lundy Braun, Anna Wentz, Reuben Baker, Ellen Richardson and Jennifer Tsai

Social Science & Medicine, 2021, vol. 268, issue C

Abstract: The rise of evidence-based medicine, medical informatics, and genomics --- together with growing enthusiasm for machine learning and other types of algorithms to standardize medical decision-making --- has lent increasing credibility to biomedical knowledge as a guide to the practice of medicine. At the same time, concern over the lack of attention to the underlying assumptions and unintended health consequences of such practices, particularly the widespread use of race-based algorithms, from the simple to the complex, has caught the attention of both physicians and social scientists. Epistemological debates over the meaning of “the social” and “the scientific” are consequential in discussions of race and racism in medicine. In this paper, we examine the socio-scientific processes by which one algorithm that “corrects” for kidney function in African Americans became central to knowledge production about chronic kidney disease (CKD). Correction factors are now used extensively and routinely in clinical laboratories and medical practices throughout the US. Drawing on close textual analysis of the biomedical literature, we use the theoretical frameworks of science and technology studies to critically analyze the initial development of the race-based algorithm, its uptake, and its normalization. We argue that race correction of kidney function is a racialized biomedical practice that contributes to the consolidation of a long-established hierarchy of difference in medicine. Consequentially, correcting for race in the assessment of kidney function masks the complexity of the lived experience of societal neglect that damages health.

Keywords: Algorithms; Estimated glomerular filtration rate; Racialization; Chronic kidney disease (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.socscimed.2020.113548

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