The murder of George Floyd in 2020 ignited a nationwide conversation about racial equity in the United States. This discussion extended into the field of medicine, where questions about the role of race in clinical tools used to determine a patient’s risk of disease were brought to the forefront. James Diao, a former medical student at Harvard Medical School, delved into these questions, focusing on the idea that if race is a social construct, why was it a factor in these tools?
Diao’s research and analysis on the impacts of race and its removal from clinical calculators have played a significant role in shaping policy decisions that affect millions of patients. His work has highlighted the importance of considering the values and assumptions embedded in seemingly objective tools used in healthcare.
As a resident at Brigham and Women’s Hospital and one of STAT’s 2024 Wunderkinds, Diao has demonstrated a commitment to listening to various perspectives, including those of patients, advocates, and policymakers. He believes that there is always more to learn, and his career has been shaped by this ethos.
During the pandemic, Diao collaborated with Arjun Manrai, an assistant professor of biomedical informatics at Harvard Medical School, to investigate how hospitals were adjusting their calculators for kidney function to remove race. The pair worked closely together, engaging in numerous Zoom meetings to delve into this complex issue.
Diao also involved his partner, Gloria Wu, a public health researcher, in their research. Together, they navigated the nuanced debates surrounding the removal of race from clinical calculators, seeking to balance empirical and moral arguments. Diao was mindful of the need to present data objectively while acknowledging the underlying ethical issues at play.
In a study published in the Journal of the American Medical Association, Diao quantified the impact on Black patients if race were to be removed from the existing eGFR calculator. The findings suggested that removing race could increase access to kidney care for some patients but might also limit access to certain treatments that rely on accurate kidney function measurements.
Despite facing criticism for his research, Diao continued to advocate for a more nuanced approach to calculating kidney function, beyond simply removing race from the equation. In a perspective published in the New England Journal of Medicine, Diao and his colleagues highlighted the need to move past the binary debate on race in clinical tools and explore more comprehensive solutions.
Diao’s work exemplifies the importance of critically examining the role of race in healthcare and striving for equitable and ethical solutions. By engaging with diverse perspectives and advocating for evidence-based decision-making, Diao has made significant contributions to the ongoing dialogue on racial equity in medicine. Months after the initial recommendation to explore race-free equations in clinical algorithms, the task force issued its final decision supporting two specific approaches that were highlighted in the paper. This decision directly impacted thousands of Black patients, particularly in the realm of kidney transplant wait times.
James Diao, a young researcher, has been at the forefront of this movement towards race-free algorithms in healthcare. Growing up in the diverse suburbs of Houston, Diao experienced different perspectives on race when visiting his father’s hometown in China. This exposure fueled his passion for studying health policy and understanding the global implications of race in healthcare.
One of Diao’s significant contributions was in developing a race-free kidney function calculator that led to adjustments in kidney transplant wait times for Black patients. Building on this success, he also worked on a similar project with the American Thoracic Society to move away from race-based lung function testing. This shift had potential implications for worker’s compensation eligibility, treatment access, and diagnostic accuracy for Black patients.
Diao’s groundbreaking research has not gone unnoticed, as he recently graduated summa cum laude from Harvard Medical School. He is now diving into residency in Boston, with a particular interest in cardiology. His passion for cardiology lies in its high stakes and data-driven nature, where his background in computer science and statistics can make a significant impact.
In his latest publications, Diao projected the impacts of adopting another race-free tool, PREVENT, developed by the American Heart Association. This tool aims to predict the likelihood of strokes and heart attacks by incorporating BMI, kidney function, and social determinants of health, such as ZIP code data. Diao’s exploration of this tool revealed dramatic differences in predictions based on varying ZIP codes in Boston, highlighting disparities in life expectancy between neighborhoods.
As the healthcare industry considers the adoption of race-free tools like PREVENT into clinical guidelines, Diao emphasizes the importance of accuracy and patient perspectives. By incorporating social determinants of health into predictive models, researchers like Diao are paving the way for more equitable and effective healthcare practices. Patients may feel uncomfortable with the idea of their race being used to determine their risk of disease, but what if their estimated income was factored in instead? This is a question that researcher Diao and his colleagues are currently exploring in a new study.
Diao is taking a unique approach by simply asking people how they feel about certain factors being used in their care. He wants to understand what patients are comfortable with and where they draw the line when it comes to using personal information for medical purposes.
By listening to patients and considering their perspectives, Diao hopes to gain valuable insights into how different demographic factors can impact healthcare decisions. The study aims to shed light on the complex relationship between race, income, and health outcomes, and how these factors intersect in the realm of patient care.
It is important to consider the ethical implications of using income as a factor in healthcare decision-making. While race has long been a controversial issue in medicine, income can also be a sensitive subject for many patients. Factors such as socioeconomic status can influence access to care, treatment options, and overall health outcomes. By examining the role of income in healthcare, researchers can better understand the barriers that patients face and work towards creating more equitable and inclusive healthcare practices.
Ultimately, the goal of this study is to empower patients and ensure that their voices are heard in the healthcare system. By engaging with patients and respecting their preferences, healthcare providers can build trust and foster a more patient-centered approach to care. Diao’s research highlights the importance of considering the diverse needs and perspectives of patients in order to create a more inclusive and equitable healthcare system for all.
