An Algorithm Blocked Black Patients Needing a Kidney Transplant

By Natasha

The unending bias for black patients in America’s healthcare sector has been a topic of discussion for decades. Black people have been at the bottom of the health food chain, with little or no access to proper health care, even though the world has evolved, and things have “changed.” Yes, the world has grown so much that doctors no longer have to do any thinking but rely on an algorithm to determine life-changing decisions like healthy enough to get specialized treatments and who isn’t.

“The purpose of medical algorithms is to improve and standardize decisions made in the delivery of medical care.” Still, black people have heard the saying “black don’t crack” all their lives, has that saying become a factor in the formulation of medical algorithms? Algorithms are designed to take information like medical test results and risk factors facing individuals. Some algorithms factor in a person’s race, which means a black man may not access needed health care because of his skin color.

A new study of patients carried out in Boston has presented proof that the formula of a commonly used algorithm for estimating kidney function assigns healthier scores (overestimates) to black people. This not only deprives them of the opportunity to receive more specialized treatments but prevents them from getting on a waitlist for kidney transplants. The study analyzed the health records for 57,000 people of which 2,225 were black with kidney disease; it found that one-third of black patients; that is around 700 plus people, would have been placed into more severe categories of kidney disease if the algorithm used to determine their kidney function was the same as that of white people. This could have affected when these patients would be referred to a kidney specialist or for a kidney transplant. After recalculating these 700 plus black patients’ scores without biases, they found that 64 would have qualified for a kidney transplant waitlist. Yet, none of them had been referred to see a kidney specialist or even evaluated for transplant, which suggests that doctors do not bother to raise an eyebrow over recommendations based on race. 

This algorithm is designed to calculate CKD-EPI, the measurement of kidney function based on blood testing. Lower scores mean worse kidney function. The score is now used to determine the severity of a patient’s kidney disease and the level of treatment that should be given to them; the study found that the scores of black patients were inflated artificially by a staggering 15.9 percent. So, coupled with the other discriminations black people have to suffer, biased medical algorithms that even extend beyond just kidney care have been added to the list. 

The kidney algorithm is one of the many algorithms that blatantly takes race into account, but racially-biased medical algorithms have started to gain more attention thanks to this study. This brings to mind the lawsuit filed by a group of retired black footballers against the NFL, claiming it utilized an algorithm that assumes white individuals have higher cognitive function than black people to decide compensation levels for brain injuries. Petitions are being signed, and more investigations will be carried out, but it is evident that this is a deep-rooted problem that needs to be tackled. Many medical traditions need to be upturned for balance to be created. Although gentle strides are being made as more people are demanding a more in-depth investigation and the total cancellation of racially biased algorithms, there is still a long, long way to go. 

Work Cited

How an algorithm blocked kidney transplants to Black …. https://penntoday.upenn.edu/penn-in-the-news/how-algorithm-blocked-kidney-transplants-black-patients

Michael Taylor: Striving for Community Engagement in …. https://yubanet.com/regional/michael-taylor-striving-for-community-engagement-in-trying-times/

Photo Credit: “Kidney transplant surgery” by Tareq Salahuddin is licensed with CC BY 2.0.
To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/

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