Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms

The use and meaning of "race" in medicine remain a point of contention among physicians, with historical debates centered on whether racial categories reflect underlying population genetics (and thus are clinically useful) or if their potential harms, stemming from a history of racism, outweigh any perceived benefits. This article critically examines the practice of "race correction" in clinical algorithms. These algorithms adjust medical test results or risk assessments based on a patient's self-identified race, often assuming inherent physiological or disease prevalence differences between racial groups.

Examples include algorithms for estimating glomerular filtration rate (eGFR) and spirometry readings, as well as the race-specific indication for BiDil in heart failure. The authors argue that these race-based adjustments inappropriately embed socially constructed notions of race into biological measurements. Such practices can perpetuate racial biases, contribute to health inequities by delaying or misdirecting care for certain groups, and obscure the true social and structural determinants of health. The article advocates for a comprehensive re-evaluation and eventual removal of race-based adjustments in clinical algorithms, promoting an approach to medicine that is more equitable, precisely tailored to individual biology, and adequately addresses the broader social factors influencing health outcomes.

The use and meaning of "race" in medicine remain a point of contention among physicians, with historical debates centered on whether racial categories reflect underlying population genetics (and thus are clinically useful) or if their potential harms, stemming from a history of racism, outweigh any perceived benefits. This article critically examines the practice of "race correction" in clinical algorithms. These algorithms adjust medical test results or risk assessments based on a patient's self-identified race, often assuming inherent physiological or disease prevalence differences between racial groups.

Examples include algorithms for estimating glomerular filtration rate (eGFR) and spirometry readings, as well as the race-specific indication for BiDil in heart failure. The authors argue that these race-based adjustments inappropriately embed socially constructed notions of race into biological measurements. Such practices can perpetuate racial biases, contribute to health inequities by delaying or misdirecting care for certain groups, and obscure the true social and structural determinants of health. The article advocates for a comprehensive re-evaluation and eventual removal of race-based adjustments in clinical algorithms, promoting an approach to medicine that is more equitable, precisely tailored to individual biology, and adequately addresses the broader social factors influencing health outcomes.