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A new paper addresses the use of algorithms in healthcare, their impact on racial/ethnic disparities in care, and approaches to identify and mitigate biases.

A paper published today in JAMA Network Open addresses the use of healthcare algorithms and provides the healthcare community with guiding principles to avoid repeating errors that have tainted the use of algorithms in other sectors. This work, conducted by a technical expert panel that included researchers at the ������Ƶ (AHRQ), supports the Biden Administration , Further Advancing Racial Equity and Support for Underserved Communities Through The Federal Government, issued on February 16, 2023. President Biden calls for Federal agencies to consider opportunities to prevent and remedy discrimination, including by protecting the public from algorithmic discrimination.

The use of algorithms is expanding in many realms of healthcare, from diagnostics and treatments to payer systems and business processes. Every sector of the healthcare system is using these technologies to try to improve patient outcomes and reduce costs.

“Promise aside, algorithmic bias has harmed minoritized communities in housing, banking, and education, and healthcare is no different, so AHRQ’s guiding principles are an important start in addressing potential bias,” said ������ƵDirector Dr. Robert Valdez. “Algorithm developers, algorithm users, healthcare executives, and regulators must make conscious decisions to mitigate and prevent racial and ethnic bias in tools that may perpetuate healthcare inequities and reduce care quality.”

The panel developed a conceptual framework to apply the following guiding principles across an algorithm’s life cycle to address the problems of structural racism and discrimination, centering on healthcare equity for patients and communities as the overarching goal:

1. Promote health and healthcare equity during all healthcare algorithm life cycle phases.
2. Ensure healthcare algorithms and their use are transparent and explainable.
3. Authentically engage patients and communities during all healthcare algorithm life cycle phases and earn trustworthiness.
4. Explicitly identify healthcare algorithmic fairness issues and tradeoffs.
5. Establish accountability for equity and fairness in outcomes from healthcare algorithms.

These guiding principles were developed following a two-day meeting in March 2023 of a diverse panel of experts convened by ������Ƶand the National Institute for Minority Health and Health Disparities at the National Institutes of Health (NIH) in partnership with the HHS Office of Minority Health and the Office of the National Coordinator for Health Information Technology, to review evidence, hear from stakeholders, and receive community feedback. The meeting was informed by an from the ������ƵEvidence-based Practice Center (EPC) Program, which examined the evidence on algorithms and racial and ethnic bias in healthcare and approaches to mitigate such bias. A subsequent meeting convened by ������Ƶand NIH in May 2023 allowed stakeholders and the public to provide feedback on a draft of the guiding principles. More information on AHRQ’s work to explore the current use of algorithms in healthcare, their impact on racial/ethnic disparities in care, and approaches to identify and mitigate existing biases may be found on AHRQ’s .

Although algorithms are widely used and can offer value in diagnostics and treatments, not all individuals benefit equally from such algorithms, creating inequities. This is due primarily to biases that result in undue harm to disadvantaged populations, which perpetuates healthcare disparities and may violate civil rights protections.

Recognition of such disparities has motivated a growing call for clinical algorithms to be both trained and validated on diverse patient data, including representation across race, ethnicity, gender, and age, among other factors. To rectify these issues, the healthcare community must understand how using algorithms may lead to unintended biased outcomes, how to identify biases before implementation, and what to do with biases discovered after implementation.

The paper, Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care, may be found in . The journal also links to an accompanying podcast interview of panel co-chairs Marshall Chin, MD, MPH, and Lucila Ohno-Machado, MD, PhD, MBA. The final EPC report, Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare, can be found .

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Email: media@ahrq.hhs.gov