Article Highlights:

  • A prominent example of this kind of discrimination is an algorithm used to refer chronically ill patients to programs that care for high-risk patients. A study in 2019 found that the algorithm favored whites over sicker African Americans in selecting patients for these beneficial services. This is because it used past medical expenditures as a proxy for medical needs. Poverty and difficulty accessing health care often prevent African Americans from spending as much money on health care as others. The algorithm misinterpreted their low spending as indicating they were healthy and deprived them of critically needed support.
  • In another instance, electronic health records software company Epic built an AI-based tool to help medical offices identify patients who are likely to miss appointments. It enabled clinicians to double-book potential no-show visits to avoid losing income. Because a primary variable for assessing the probability of a no-show was previous missed appointments, the AI disproportionately identified economically disadvantaged people.
  • Some algorithms explicitly adjust for race. Their developers reviewed clinical data and concluded that generally, African Americans have different health risks and outcomes from others, so they built adjustments into the algorithms with the aim of making the algorithms more accurate. But the data these adjustments are based on is often outdated, suspect or biased. These algorithms can cause doctors to misdiagnose Black patients and divert resources away from them.

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