New Findings Reveal the Impact of Health Care Algorithms on Racial Disparities, Patient Outcomes

As algorithms wield increasing influence in health care and even over patient outcomes, new research led by Shazia Mehmood Siddique, MD, MSHP, revealed these algorithms can both exacerbate and mitigate racial and ethnic disparities in health care.¹ Investigating more than a decade of literature, researchers uncovered complex findings, ranging from algorithms that reduce disparities to those that perpetuate them.

"In 2020, the lethal force inflicted on George Floyd, Breonna Taylor, and Ahmaud Arbery, and that of COVID-19 in racial and ethnic communities, woke the United States, unearthing racism deeply engraved into the foundational bedrock of American society," an editorial published alongside the study stated.²

Activists argued that algorithms, including those used in diagnosing kidney disease, may exacerbate health disparities.

"Now, 4 years later, in a review petitioned by members of the US Congress, Siddique and colleagues attempt to settle an impassioned debate on whether use of race in clinical algorithms has affected racial and ethnic disparities and been harmful or beneficial to the health and medical care of people of color,” authors wrote.

A systematic review was conducted, searching several databases for relevant studies published between January 2011 and September 2023.¹ They utilized predefined criteria for study selection and employed dual review for screening. The selected studies were assessed for their impact on racial and ethnic disparities in health and health care outcomes, as well as the effectiveness of strategies to mitigate bias in algorithm development, validation, dissemination, and implementation.

The review included 63 studies, comprising various study designs such as modeling, retrospective, prospective, prepost studies, and randomized controlled trials. The evidence presented a heterogeneous landscape regarding the impact of algorithms on disparities. Some algorithms were found to reduce disparities, such as the revised kidney allocation system, while others perpetuated or exacerbated them, as seen with severity-of-illness scores applied to critical care resource allocation. Additionally, some algorithms showed no statistically significant effect on select outcomes, such as the HEART Pathway.

"If inferences discovered in this body of literature hold true... then clinical algorithms using race are tools that can have benefits as well as side effects, even within the same algorithm,” the editorial authors wrote.²

Charting a Course for Equity

In their quest to mitigate disparities, researchers identified 7 strategies using the novel findings to guide toward equitable algorithm practices:¹

1. Removing an input variable
2. Replacing a variable
3. Adding race as a variable
4. Introducing non–race-based variables
5. Changing the racial and ethnic composition of the population used in model development
6. Creating separate thresholds for subpopulations
7. Modifying algorithmic analytic techniques

The study acknowledged that the results primarily stemmed from modeling studies, which may limit generalizability and applicability across different contexts. Furthermore, the effectiveness of the identified strategies could be highly context-specific, requiring careful consideration of local factors.

This comprehensive review underscores the complex and multifaceted nature of the relationship between health care algorithms and racial and ethnic disparities. While algorithms have the potential to mitigate, perpetuate, or exacerbate these disparities, the evidence is heterogeneous.

"Two societies among the first to address race, the National Kidney Foundation and the American Society of Nephrology, used an evidence-based approach coupled with equity values to rescue misinformed jettisoning or removal of race from kidney function algorithms and recommended a new, equitable equation,” authors noted.²

The intentionality and implementation of algorithms significantly impact their effect on disparities, suggesting that thoughtful design and deployment are critical.¹ Moreover, there may be tradeoffs in outcomes when implementing mitigation strategies, emphasizing the need for careful consideration of potential unintended consequences.

As health care algorithms continue to shape the landscape of patient care, reckoning with racial and ethnic disparities is non-negotiable. The study serves as a call, reminding stakeholders of the inherent tradeoffs in pursuit of equity.

"The aftershocks of 2020 continue with the use of race still being debated, reconsidered, and transformed in every corner of medicine,” authors concluded.²

References

1. Siddique SM, Tipton K, Leas B, Jepson C, Aysola J, Cohen J, et al. The impact of health care algorithms on racial and ethnic disparities: a systematic review. Annals of Internal Medicine. Published online March 12, 2024. doi: https://doi.org/10.7326/M23-2960

2. Powe NR. Race, health care algorithms, and precision health equity. Annals of Internal Medicine. Published online March 12, 2024. doi:10.7326/M24-0551