Han et al. on Race-Based Medical Algorithms and Compassionate Release

Juyoun Han, Jennifer Tsai, and Rohan Khazanchi (Eisenberg & Baum LLP, Yale University - Yale New Haven Health and Harvard University - Brigham and Women's Hospital) have posted Medical Algorithms Lack Compassion: How Race-Based Medicine Impacted the Rights of Incarcerated Individuals Seeking Compassionate Release During COVID-19 (26 Stan. Tech. L. Rev. 43 (2023)) on SSRN. Here is the abstract:

 

In 2020, the U.S. Centers for Disease Control and Department of Justice introduced guidance that a number of underlying medical conditions—including kidney disease—increased one’s risk of severe illness or death from COVID-19 enough to merit compassionate release from jail or prison. Courts reviewing compassionate release applications used a standard metric of kidney function— the estimated glomerular filtration rate (“eGFR”)—to determine the severity of an individual’s chronic kidney disease. Because the equations used to calculate eGFR incorporate a race-based multiplier that specifically and systematically underestimates kidney disease severity for Black patients, compassionate release decisions were influenced and, in several cases, determined on the basis of race. In this article, we articulate the pseudo-scientific origins of race-based medical algorithms and the inequitable impact they pose, particularly for minoritized patients. We address key civil rights implications that arise from the use of race-based medical algorithms that systematically disadvantage Black individuals. We explore legal precedents by drawing parallels with scrutiny of the use of race in other medical algorithms, including the direct impact of GFR estimation on kidney transplant eligibility, race-normed concussion protocols in the evaluation of National Football League players, and race-based pulmonary function testing in asbestos workers’ compensation cases. We conclude by recommending the creation of interdisciplinary task forces and regulatory oversight to reexamine the ways in which medical algorithms produce inequitable outcomes for individuals on the basis of protected classifications like race, often without a sound scientific justification.