Use of healthcare algorithms can mitigate, exacerbate, or not impact racial and ethnic disparities, according to a literature review published in Annals of Internal Medicine.
The authors reviewed 63 studies related to healthcare algorithms and their study offers several strategies healthcare systems can implement in order to mitigate these effects.
Healthcare algorithms are frequently used to guide clinical decision making, resource allocation, and healthcare management. Although algorithms are developed to optimize specific processes of care, they may introduce or perpetuate racial and ethnic biases, leading to unequal treatment and contributing to or exacerbating unequal health outcomes.
Researchers from the University of Pennsylvania conducted a systematic review of 51 modeling, 4 retrospective, 2 prospective, 5 pre-post studies, and 1 randomized controlled trial.
The authors found varying results, with some research indicating that healthcare algorithms mitigate racial and ethnic disparities, and other research indicating that these algorithms exacerbate these disparities or have no effect at all.
After review, the authors identified seven strategies for potentially mitigating disparities in healthcare algorithms:
- removing an input variable,
- replacing a variable,
- adding race,
- adding a non–race-based variable,
- changing the racial and ethnic composition of the population used in model development,
- creating separate thresholds for subpopulations, and
- modifying algorithmic analytic techniques.
According to the authors, these results highlight the need for more high-quality research, transparency, and monitoring of algorithms to detect and address biases in their application that may develop over time.
