Katie Palmer, a Health Tech Correspondent for Stat News, teams up with Usha Lee McFarling, co-author of 'Embedded Bias', to tackle the tough issues surrounding racial disparities in medicine. They explore the drawn-out debates over using race in medical algorithms and diagnostic tools. Insights reveal how these biases lead to alarming underdiagnosis, particularly in Black children. The duo also discusses how the medical community can advocate for change, emphasizing the importance of individualized patient care over broad racial categories.
The incorporation of race in medical diagnostics has historically perpetuated healthcare disparities, often leading to misdiagnoses and inequitable treatment for marginalized groups.
Efforts to remove race from clinical algorithms must be carefully considered to avoid obscuring critical health patterns affecting diverse populations while promoting equitable care.
Deep dives
Racial Bias in Medical Diagnoses
Disparities in health outcomes based on race are often ingrained in diagnostic tools, affecting patient care regardless of healthcare providers' beliefs. For example, algorithms that incorporate race into the diagnosis of urinary tract infections (UTIs) can lead to misdiagnoses among Black children. The use of race-based calculators can result in under-treatment for this demographic, as their lower reported rates of UTIs contribute to a false sense of security. Ultimately, even well-intentioned algorithms can perpetuate health disparities due to the absence of biological reasoning behind these race-based decisions.
Historical Roots of Racial Assumptions
The historical context of systemic racism in medicine has influenced current diagnostic practices, leading to the incorporation of race into clinical algorithms without adequate scientific backing. For instance, the estimation of kidney function traditionally used a formula that suggested Black patients had better kidney function than their White counterparts based solely on creatinine levels. This misrepresentation has historically justified inequities in access to treatment, including kidney transplants, by classifying Black patients as less ill based on flawed assumptions about muscle mass. Such outdated beliefs highlight how early racist ideologies continue to echo through modern medical practices.
Challenges in Removing Race from Algorithms
While there is a growing movement to eliminate race from clinical calculators, there are concerns that doing so without proper oversight could lead to detrimental outcomes for patients of color. Medical professionals are grappling with the challenge of ensuring that new algorithms maintain accuracy and help identify health issues in vulnerable populations. If race is removed too hastily, it might obscure patterns that could be vital in treating certain groups who experience systemic disparities in healthcare access. Therefore, any changes must carefully balance the need to avoid reinforcing racial stereotypes while still addressing the real health challenges faced by different demographic groups.
The Importance of Engaging Patients in Care
Patients are encouraged to take an active role in their healthcare discussions, particularly in understanding how race might influence medical algorithms used in their diagnoses. Engaging in conversations with healthcare providers can illuminate the implications of using race in determining treatment options and risks. Research suggests that many physicians may not always disclose the use of race-based calculators, making it crucial for patients to inquire about how such factors might affect their care. This engagement can empower patients and foster a more informed approach to their health, encouraging equitable treatment.
Race has long been a factor in how doctors approach diagnoses— removing it has proved a challenge. Katie Palmer, Health Tech Correspondent for Stat News, joins host Krys Boyd to discuss the bias baked into medicine for decades, how it contributes to system disparities, and why the work to change it is so difficult. Her series “Embedded Bias” is written with co-author Usha Lee McFarling.
Get the Snipd podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share & Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode