BACKGROUND: There is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities.
PURPOSE: To examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities.
DATA SOURCES: Several databases were searched for relevant studies published from 1 January 2011 to 30 September 2023.
STUDY SELECTION: Using predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms.
DATA EXTRACTION: Outcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension.
DATA SYNTHESIS: Sixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: 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.
LIMITATION: Results are mostly based on modeling studies and may be highly context-specific.
CONCLUSION: Algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes.
PRIMARY FUNDING SOURCE: Agency for Healthcare Quality and Research.
| Specialty Area | Score |
|---|---|
| Hospital Doctor/Hospitalists | |
| Internal Medicine | |
| Cardiology | |
| Neurology | |
| Oncology - Genitourinary | |
| Emergency Medicine | |
| General Internal Medicine-Primary Care(US) | |
| Family Medicine (FM)/General Practice (GP) |
The medical community needs to establish criteria about when to include or not include racial categories in algorithms. This article provides some background information about current practices.
I found this article likely useful for my everyday clinical practice.
Important issue but the limited heterogeneous and context-specific primary data mean that this is mainly of interest to researchers. Clinicians need to be aware of the potential impact of algorithms on disparities, but further research is needed to provide useful guidance for practice.
As a family physician, this study seems to be telling me only that different algorithms might increase or decrease racial disparities.
This is a powerful systematic review raising questions about the unintentional equity considerations of some of our data-driven tools for health care decision-making. Particularly as we move toward ever-more complex algorithms devised using opaque artificial learning / machine learning methods, we will always have to be mindful of the potential for these tools to worsen racial or social disparities; however, they can also be a powerful tool to reduce disparities if they live up to their intended purposes of dispassionate identification of higher risk / higher benefit patients. It is too bad the topic area is so difficult to read into. Authors could do a slightly better job explaining things in a more accessible way.
Important systematic review showing mitigation, perpetuation, and exacerbation of racial and ethnic disparities, regardless of the explicit use of race and ethnicity because of healthcare algorithms used. However, there was just 1 RCT in all the 49 studies evaluated. Most studies did not actually use algorithms to treat illness but modeling simulations were done. There were risks of confirmation and publication and recall biases in this study.
This novel analysis provides useful information regarding inclusion of race and ethnicity in care algorithms.
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