When the 2020 killing of George Floyd sparked calls for racial equity in the US, the field of medicine faced its own thorny questions about race. James Diao, then a medical student at Harvard Medical School, was among the many who focused on one particular problem: If race is a social construct, why was it a factor in the clinical tools used to determine a patient’s risk of disease?

“These big questions were fundamentally not just scientific issues, but also human and moral issues about how values ​​are embedded in these seemingly dispassionate tools that we use,” said Diao, whose papers that analyzes the impact of race and its removal from clinical computers have since played a role in the political decisions affecting millions of patients. “I became very, very obsessed with this idea of ​​the kind of assumptions we put into these computers.”

As the healthcare system continues to face the role of race in many other clinical toolsDiao’s work on this topic has shaped how to balance a quantitative approach to the problem with the perspectives of patients, advocates, and policymakers. Now a resident at Brigham and Women’s Hospital and one of Wunderkinds 2024 from STATDiao’s career was shaped by his willingness to listen.

“The more I learned, the more I realized there was more to learn,” he said.

Searching for a better way to calculate kidney function

When the pandemic hit, Diao had already been working with Arjun Manrai, an assistant professor of biomedical informatics at Harvard Medical School, since he was a sophomore studying statistics and biochemistry. Manrai, who recruited Diao once he began his medical training at Harvard, investigates how clinical algorithms work — and fail — when applied to different populations. As the two were confined at home, they began to explore the different ways hospitals were at the time adjusting their calculators for kidney functionknown as eGFR, to eliminate race.

They increased. many. “I think there was a period where we zoomed with each other every day,” Manrai said. Diao involved his partner Gloria Wu, a public health researcher, in the work. (In August, Manrai officiated their wedding, where the pair showed off their ballroom dancing skills.)

Diao didn’t just stay in his bubble. On Twitter, as debate raged over the harm of both keeping and expelling the breed from the kidney computer, “I was a fly on the wall,” Diao said. “I’m thankful I didn’t say anything then. I had so much to learn.”

To catch up, he read. “The Fatal Invention,” Dorothy Roberts’ book about the fallacy of race as a biological category, was at the top of her Covid reading list. He spoke to medical students advocating for changes to the race-based kidney calculator in their hospitals and joined the Institute for Healing and Justice in Medicine’s grassroots meetings with other medical students who began to question the clinical equations. which I teach.

As she continued her research, Diao had to carefully balance the empirical and moral arguments for considering race in clinical decisions. “He had a very difficult job,” said Rohan Khazanchi, then a medical student studying health services and health equity. Diao was young and a relative outsider, working with teams of doctors and researchers who had been studying — and using — clinical algorithms for years.

“You have to keep it focused on the data so that different people can use that data to inform policy decisions without feeling like you have your thumb on the scale,” Diao said. “But on the other hand, if you try to lean too much into it, people think you don’t care about the underlying issues.”

In late 2020, Diao was the first author on a study in the Journal of the American Medical Association that quantified the impact on black patients if the drug removed race from the existing eGFR calculator without otherwise changing it. Eliminating race would increase earlier access to kidney care, including transplants, he wrote with Wu, Manrai and others. But it would also prevent some patients from accessing chemotherapy or drugs that have doses based on kidney function — a finding that some have interpreted as arguing against removing race from the computer.

“It was very stressful trying to defend,” Diao said. But as the National Kidney Foundation and the American Society of Nephrology convened a task force to reevaluate the role of race in eGFR, Diao was able to make her case, testifying about the research along with other medical students advocating for removing race from their hospital equations.

“He cited some of the numbers,” Khazanchi recalled, “but also talked about the challenges of talking to an individual patient and said, ‘By the way, I’m using your race to determine the stage of your kidney disease.’ ”

The following year, Diao and others published a perspective on the search for a better kidney function equation in the New England Journal of Medicine, which went beyond simply removing race from the existing eGFR calculator.

They aimed to move beyond the “false dichotomy” that pits these approaches against each other, said Manrai, who co-authored the paper. “There are many other equations without race, and many other ways to change this equation to remove stratification by race, that are more accurate and have different strengths and weaknesses.” Months later, the task force issued its recommendation supporting two of the non-race approaches highlighted in that work by quoting it directly. In practice, this decision led thousands of black patients to have them waiting time on kidney transplant lists adjusted.

Bringing patient perspectives to clinical algorithms

Race meant one thing where Diao grew up, in the suburbs of diverse Houston, where his parents worked as engineers for an oil company. But when he visited his father’s hometown in China and showed his school yearbook, he recalled, many people assumed that every dark-skinned student was of African descent.

Seeking to expand her global perspective on race, she went to the other Cambridge across the pond in 2022 to study health policy and expand her global perspective on race. “He really listens to a lot of people who have different perspectives and actively seeks them out,” Manrai said. “He doesn’t just come from one community, from one angle. He really listens to a lot of people.”

Meanwhile, Diao was continuing his work on another clinical algorithm based on race. The American Thoracic Society, spurred by the 2020 racial calculus, began reconsidering its approach to lung function testing, which had long assumed that black patients had lower baseline lung volumes than white patients.

Diao’s approach was similar to his work on the kidney function calculator, said Khazanchi, who worked on the project: “For all patients, what are the positive and negative implications of moving from a race-based lung function testing algorithm to one without race. algorithm based?”

Until Diao published yet another first-authored work in New England Journal of Medicine this year, the ATS had a verdict: it would move away from lung function testing based on race. But the research provided critical context as health systems figured out how to implement that recommendation.

Dropping race-based tools would likely make black patients more eligible for workers’ compensation. With more accurate estimates of lung function, they could have access to treatments and therapies. But a better diagnosis could lead to more invasive, potentially risky interventions, or even eliminate certain surgeries. After the study was published, the Department of Veterans Affairs started an investigation of the change’s impact on disability payments, anticipating a smaller effect than the study predicted.

Based on his work, Diao recently became the 22nd student in Harvard Medical School history to graduate summa cum laude. With Khazanchi, he dives right into residency in Boston, where he was drawn to cardiology.

“It just clicked” about the specialty, Daio said. He likes the high stakes of helping a heart attack patient. But cardiology is also a particularly data-driven specialty — a place where he could see informatics and statistics making an impact. “The field is moving very, very quickly and using all this data that they’re collecting for the benefit of the patient,” Daio said. “This is something I could really contribute to and be a part of.”

For now, he’s focused on helping patients avoid cardiovascular disease. In one of his most recent publications in JAMADiao projected the impact on statin eligibility with the likely adoption of another new non-racing tool, this one used to predict the likelihood of strokes and heart attacks. The new calculator, PREVENT, was built by the American Heart Association as a wholesale overhaul of the previous tool, with the goal of making it more accurate by incorporating patients’ BMI and kidney function.

Also, in a first for an American instrument of its sizetries to refine its predictions by incorporating the social determinants of a patient’s health, using zip code to estimate factors such as income, education and housing status. Diao played around with the new tool, trying different zip codes around his hospital. “Boston has some of the largest disparities in life expectancy between its neighborhoods of any city in the US,” he said. By entering different zip codes in the area, “you end up with these really dramatic differences in prediction.”

As the AHA considers whether and how to adopt PREVENT in its clinical guidelines, the question is whether those zip code-based predictions are more accurate — “and if so, how do patients feel about it?” Diao said. If patients are uncomfortable with their race being used to determine their disease risk, how would they feel about their estimated income?

In a study he’s working on now, Diao and his colleagues are “just asking people,” he said, “asking people what they’re comfortable with being used in their care and when they’re comfortable with it being used in their care.” .

He’s still listening.