Policy Brief: The Complexities of Race Adjustment in Health Algorithms
Policy Brief: The Complexities of Race Adjustment in Health Algorithms
As policymakers, health-care practitioners, and technologists pursue the application of AI and machine learning (ML) algorithms in health care, this policy brief underscores the need for health equity research and highlights the limitations of employing technical “fixes” to address deep-seated health inequities.
Chronic kidney disease affects more than one in seven adults—or about 37 million people—in the United States. For racial and ethnic minorities, the burden of kidney failure is higher: Black or African American and Hispanic patients are at least 3-fold and 1.5-fold more likely to progress to kidney failure in comparison to non-Hispanic white patients, in part due to delays in referrals and visits to nephrology. Despite recognition of these disparities in the 1980s, there has been little to no improvement since then.
There are debates about how to account for race in algorithms that are widely used to gauge the severity of kidney disease and inform related care decisions. For a long time, race was considered a factor when assessing kidney disease severity. Two of the most widely adopted kidney-disease-related equations incorporated a Black or non-Black race variable. Because the use of race variables in clinical algorithms propagates racial bias in decision-making, two professional organizations helped develop a different clinical algorithm that does not incorporate race in 2021.
Our paper, “Algorithmic Changes Are Not Enough: Evaluating the Removal of Race Adjustment from the eGFR Equation,” is the first to assess the 2021 equation’s effect on care decision-making for chronic kidney disease patients, including its impact on care disparities for racial and ethnic minorities. Our study estimates the effects of implementing the kidney disease equation without race adjustment on nephrology referrals and visits for patients within the Stanford Health Care system.
While our study focuses on a single medical center and a single disease, the findings present important considerations for the healthcare field. As policymakers, healthcare practitioners, and technologists alike pursue the application of AI and machine learning (ML) algorithms in healthcare, our research underscores the need for health equity research and highlights the limitations of employing technical “fixes” to address deep-seated health inequities.
