Health Care Algorithms in Racial and Ethnic Disparities

March 27, 2024

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The Impact of Healthcare Algorithms on Racial and Ethnic Disparities: A Systematic Review

Author: University of Pennsylvania School of Medicine
Published: 2024/03/26
Post type: Observational study – Peer Reviewed: Yeah
Content: Summary – Major – Related Posts

Synopsis: Study points to ways to reduce potential for racial bias and inequity when using algorithms to inform clinical care. Health systems, insurance companies, and electronic health records companies have begun developing their own algorithms, while artificial intelligence tools have quickly taken on a major role in driving new algorithms, a shift that researchers say researchers, increases the need for scrutiny. Algorithms are often embedded in electronic medical records and patients and doctors themselves are often not always aware that they are being used.

Main summary

For years, it was harder for Black patients to get a coveted spot on the national kidney transplant waiting list because a clinical algorithm made Black patients appear healthier than they were. After a Penn Medicine researcher exposed the problem in 2019 and showed how it exacerbated racial disparities in kidney disease, a national task force recommended removing race from the algorithm’s scoring, a move that was quickly adopted nationwide in an effort to reduce racial inequality. .

But that wasn’t the only impact, according to a comprehensive new study by Penn researchers that delves into the complicated issue of race and ethnicity in health care algorithms. Removing race from the kidney function algorithm also appeared to reduce access to chemotherapy, reduce the eligibility of black patients in clinical trials, and affect drug dosing.

The new article, published this month in the Annals of internal medicine, presents a nuanced picture of algorithms in healthcare—a pervasive, but often invisible, force in clinical decision making—and how their use can impact racial and ethnic disparities. The research team, led by Shazia Mehmood Siddique, MD, assistant professor of Gastroenterology at Penn’s Perelman School of Medicine, found that algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of whether they explicitly use race or ethnic origin as a contribution.

“Intentionality matters,” said Siddique, who also serves as research director of Penn Medicine’s Center for Evidence-Based Practice (CEP) and also the Penn Center for Healthcare Improvement and Patient Safety (CHIPS). . “Racial and ethnic disparities cannot be an afterthought.”

The researchers defined the algorithms as mathematical equations that combined multiple data points and inputs, such as sex and age, to inform clinical care. Algorithms are integrated into all health care, Siddique said, to help providers make complex clinical decisions, such as whether a patient should be diagnosed with a disease or whether they are eligible for a particular treatment, and to help health systems determine how to allocate resources, such as care management and critical care services. Importantly, algorithms are often embedded in electronic medical records and patients and physicians themselves are often not always aware that they are being used.

To patients, it may appear that medical criteria, such as risk scores or treatment thresholds, are based entirely on objective factors, said study co-author Brian Leas, a senior research analyst at CEP. But algorithms introduce a social component into clinical decision-making.

“The way algorithms are built is a choice of the developers,” he said. “It’s a decision to put certain factors together in a formula, and those decisions can be made differently.”

Traditionally, healthcare algorithms were developed by researchers in academic settings. However, more recently, health systems, insurance companies, and electronic health records companies have begun to develop their own algorithms, while artificial intelligence tools have quickly taken on a major role in driving new algorithms, a change that researchers say increases the need for scrutiny.

In 2020, healthcare algorithms caught the attention of four US senators, including Cory Booker of New Jersey. Citing the harmful kidney disease algorithm, as well as another that unfairly reduced concussion-related injury settlement benefits for black players in the National Football League, lawmakers asked the Agency for Research and Quality U.S. Department of Health and Human Services Health Care will conduct a review of race-based clinical algorithms in medical practice. The agency later commissioned the Penn team to conduct the study, which was conducted in collaboration with ECRI, a nonprofit healthcare research organization focused on patient safety and reducing avoidable harm.

In a systematic review of 63 studies, researchers found that there is no magic solution to the problems associated with algorithms. Instead, they identified several strategies to mitigate disparities in health care algorithms, including adding a non-racial variable, using data that reflects diverse racial and ethnic groups when developing algorithms, and swapping race with another more precise variable. , such as genetic or social data. factors that can affect care.

Health care algorithms are most successful in reducing disparities when they intentionally reduce documented inequalities, Siddique said. In some cases, this meant including race as a component of the algorithm. For example, a prostate cancer screening algorithm was found to overscreen black men, leading to unnecessary biopsies and complications. Adding black race as an input to the algorithm mitigated the disparity.

But sometimes, as with the kidney disease algorithm, reducing disparities means compromising other outcomes. When race was removed as a variable from an algorithm to determine eligibility for lung cancer screening, disparities in eligibility improved for Hispanic and Asian Americans, while disparities deepened for black patients.

“A value judgment is needed when we make a decision about these trade-offs in outcomes,” Siddique said. “Is this disadvantage that we’re going to see worth this potential benefit? How can we further refine the algorithm to minimize the disparities between the groups?”

The researchers found that, rather than removing race from the lung cancer eligibility algorithm, they refined the model by allowing race and ethnicity to remain if a patient’s only reason for ineligibility was low life expectancy due to to their race or ethnicity, ultimately improved disparities in lung cancer eligibility across the board. .

Race is often used in algorithms as a proxy for another variable, such as ancestry, a specific gene, social determinants of health or even the effects of systemic racism, Siddique said. The problem, he added, is that it’s often unclear why race is used in an algorithm.

“We need algorithm developers to be clear about what race is being used as an indicator, because doctors may have no idea,” Siddique said. “If there is no transparency about this, it can perpetuate the false assumption that race is biological.”

A better option: replace the stroke with a more precise variable. Siddique is now studying, for example, whether replacing race with country of origin in liver cancer screening guidelines would reduce disparities. (Although similar to algorithms, guidelines are non-mathematical, evidence-based recommendations typically developed by medical associations to help guide best clinical practices.)

The conversation about algorithms in healthcare (and their impact on racial and ethnic disparities) is just beginning. In response to the Penn-ECRI research, a diverse panel of healthcare experts met late last year to offer guidance on how to mitigate algorithm bias. The group recommended guiding principles to support equity in healthcare in the algorithm development and review process.

In a commentary in Health Affairs published in October 2023, Siddique and others promoted a “race conscious”Instead of a racial approach, algorithms call for greater diversity in clinical trials, a focus on precision medicine, and better education about the factors that shape health outcomes.

The study was funded by the Agency for Healthcare Research and Quality.

Penn Medicine

Penn Medicine is one of the world’s leading academic medical centers, dedicated to the related missions of medical education, biomedical research, excellence in patient care, and community service. The organization is made up of the University of Pennsylvania Health System and the Raymond and Ruth Perelman School of Medicine at Penn, founded in 1765 as the nation’s first medical school.