A STAT investigation found that a common method of using analytics software to target medical services to patients who need them most is infusing racial bias into decision-making about who should receive stepped-up care. https://www.statnews.com/2020/10/13/how-software-infuses-racism-into-us-health-care/?utm_source=STAT+Newsletters&utm_campaign=6e46fe1415-Daily_Recap&utm_medium=email&utm_term=0_8cab1d7961-6e46fe1415-152831030

While a study published last year documented bias in the use of an algorithm in one health system, STAT found the problems arise from multiple algorithms used in hospitals across the country.

The bias is not intentional, but it reinforces deeply rooted inequities in the American health care system, effectively walling off low-income Black and Hispanic patients from services that less sick white patients routinely receive.

The bias can produce huge differences in assessing patients’ need for special care to manage conditions such as hypertension, diabetes, or mental illness:

In one case examined by STAT, the algorithm scored a white patient four times higher than a Black patient with very similar health problems, giving the white patient priority for services.

There are at least a half dozen other commonly used analytics products that predict costs in a similar way.

The bias results from the use of this entire generation of cost-prediction software to guide decisions about which patients with chronic illnesses should get extra help to keep them out of the hospital.

Data on medical spending is used as a proxy for health need — ignoring the fact that people of color who have heart failure or diabetes tend to get fewer checkups and tests to manage their conditions, causing their costs to be a poor indicator of their health status.

A 61-year-old Black woman & 58-year-old white woman had a similar list of health problems, including kidney disease, diabetes, obesity, & heart failure. But the white patient was given a risk score that was 4 times higher, making her more likely to receive additional services.

A 32-year-old white man w/ anxiety & hypothyroidism was given the same risk score as a 70-year-old Black man with a longer list of more severe health problems, including dementia, kidney & heart failure, chronic lung disease, high blood pressure, & prior stroke & heart attack.

Of the patients it targeted for stepped-up care, only 18% were Black, c/t 82% white. When they revised the algorithm to predict the risk of illnesses instead of cost, the % of Black patients more than 2x, to 46%, while the % of white patients dropped by a commensurate amount.

“These small technical choices make the difference between an algorithm that reinforces structural biases in our society, and one that fights against them and gives resources to the people who need them.” - Ziad Obermeyer