U of U Health Internal Medicine Grand Rounds
This article is an excerpt from the Department of Internal Medicine Grand Rounds, “Applications of Patient Race and Ethnicity in Medical Education, Research, and Healthcare Delivery,” presented on April 18, 2024, at University of Utah Health.
uring the medical education conference for the Western Chapter of the Association of American Medical Colleges (AAMC) last year, I (Julie) was inspired by a session about race and ethnicity and how we can find actionable methods to build fair and unbiased structures, processes, and practices in health care. This led to the creation of a grand rounds session to provide a basic understanding of the impact of race and ethnicity on the different spheres of academic medicine: education, research, and clinical care.
While this topic is extremely dynamic and nuanced, we as educators, researchers, and healthcare providers can agree that to improve the health of all our patients, we must address unfair assumptions and treatment as they pertain to race and ethnicity by strategizing ways to reduce these disparities.
Education
As educators, we need to critically evaluate our approach to teaching and medical education, particularly regarding the use of race and ethnicity.
Historically, medicine has relied on race-based medicine, which lacks scientific validity. Centuries ago, scientists categorized humans based solely on anthropometric traits such as, skin color and skull size, leading to erroneous assumptions that were used to justify racism and slavery. Despite progress, race-based medicine remains both harmful and unfounded today.
"Race is a social construct, not a scientific one. However, beliefs about race can perpetuate health disparities."
As educators, we must emphasize that while race may be relevant to a patient's social history, it should not influence medical decision-making. More important and directly related to a patient’s health are the social factors such as income, access to care, availability of healthy food, and a safe environment.
Health disparities based on race are not inherently related to race, but rather the social and health consequences of racism at all levels. This includes internalized, interpersonal, institutional, and structural racism. Therefore, we must reframe explanations of racial disparities in health outcomes within the framework of structural determinants of health and social health needs as we teach learners and students.
This practice can begin in the preclinical years and throughout our careers. For instance, next time you hear a patient presentation in which the student or learner says, "This is a 35-year-old Asian man," think about the consequence of including the race and/or ethnicity in the leading sentence — is it serving simply as a shorthand or proxy? Instead of using the shorthand, which can perpetuate stereotypes and bias care, consider relevant indicators of structural vulnerability that might actually exist for any individual patient, especially those the team will need to address in order to achieve a good health outcome: “This is a 35-year-old unhoused man.”
It's also important to address the idea of “colorblindness” in medical education. Admonitions to remove race and ethnicity descriptors from patient presentations and notes should not lead us down a road that refuses to acknowledge differences in the experience of patients based on race or ethnicity. For instance, patients of color have increased risk of experiencing medical racism, stigma, and biased care that impacts their health outcomes and potentially their ability to seek care. These are real social consequences of interpersonal and structural racism that contribute to health disparities.
Research
Key Terms
Race is a social construct based on physical attributes. Race differences often reflect social inequalities faced by racialized groups.
Ethnicity is a multi-dimension construct based on common attributes (language, religion, nationality), e.g., Hispanic or Latino regardless of race.
Ancestry reflects a common line of geographic, genealogic, or genetic decent.
As researchers, we must consider how we categorize race, ethnicity, and ancestry for population, clinical, and translational health research.
Historically, race and ethnicity data and methods for collection and analysis have been under- and inconsistently reported in research. Currently, most guidance on the use of race and ethnicity in research recommends self-identified race and ethnicity, with as much granularity as reasonably possible. Importantly, race and ethnicity cannot provide insight into genetic or biological differences; as a corollary, genetic and biologic differences cannot explain inequities based on race or ethnicity. Differences in outcomes by race or ethnicity represent differences in lived experiences, such as those created by internalized, interpersonal, or structural racism, which have an impact on health.
Genetic ancestry, or the tracing of regional genetic lineage, can provide some insight into biological risk factors for disease, as some genes are more likely to occur within certain regional lineages. However, it is important to note that the grouping of certain genes (e.g., sickle cell disorder) within certain regional ancestries is related to evolutionary pressure in certain parts of the world. As the world population becomes increasingly genetically admixed due to in- and out-migration, these relationships between genes and genetic ancestry become increasingly loose.
"We should not use race as a proxy for genetic ancestry."
The Journal of the American Medical Association has released guidance on how we, as researchers, can approach race and ethnicity. They encourage:
- A balanced, evidence-based discussion of race, structural racism, and ethnicity as social constructs
- A methods section for clinical trials that explains how patients are categorized
- Separating race and ethnicity when creating categories
- Listing races and ethnicities in alphabetical order
- Defining categories as specifically as possible, avoiding the use of "other," and attempting to capture information about regionality as much as possible
When studying race, we can use census data but must recognize its shortcomings. These terms are stand-ins for biological ancestry and don’t have as much scientific value. While there is still value in researching associations of race and ethnicity with health, we must recognize that race and ethnicity on their own are not informative. We must contextualize race and ethnicity within the framework of other social and political factors that influence health and be as specific and intentional as possible to determine the truth behind race in healthcare.
Clinical Care
As clinicians, we are obligated to understand how our training and the evidence base we draw from informs our clinical practice, potentially resulting in inequity in the availability and delivery of healthcare.
We recognize that the majority of physicians and clinicians practice from a place of altruism, good intentions, and thoughtfulness. While individual clinicians may not intentionally treat patients differently based on race, racial bias in care delivery can stem from institutional and educational biases that are “baked into” our learning from early in our training. For instance, OB/GYNs in training are taught that Black and Latinx persons are more likely to undergo primary Cesarean delivery than their white counterparts. Black, Indigenous, and other pregnant people of color also have a higher risk of hypertensive disorders of pregnancy, diabetes, and placental insufficiency, which are themselves risk factors for cesarean birth. When we are taught this way, we begin to associate disease with race: a Black person equals a higher risk pregnancy due to inherent biology. This sort of bias stays in our heads and may change medical decision making.
"We have to remember that while race is important to healthcare outcomes, it’s not because of biological implications but because of the social and structural implications."
For instance, data show that among low-risk laboring patients, none of whom have risk factors for placental insufficiency or cesarean birth, Black and Latinx low-risk laboring individuals 20-30% more likely to have a cesarean delivery than their White counterparts, and the increase is driven by concerns for fetal heart rate abnormalities. Because fetal heart abnormalities in labor are linked to placental insufficiency, one potential explanation is that educational bias might make ObGyns more likely to recommend a cesarean birth for a laboring Black patient with equivocal fetal heart rate monitoring than for a White person, even if those two patients actually have equal underlying risk for placental insufficiency. Inequitable cesarean rates in turn account for increased rates of maternal morbidity among Black and Latinx patients. This example of institutional racism (biased education in the medical institution) is just one way that structural factors can perpetuate differences in health outcomes based on race.
To avoid these biases, we have to practice race-conscious medicine. In race-conscious medicine, clinicians recognize racism as an underlying factor that may influence their patient’s health and with this knowledge supporting their patients in overcoming structural barriers to health. We have to remember that while race is important to healthcare outcomes, it’s not because of biological implications but because of the social and structural implications. In the example of OB/GYN care, lack of access to prenatal care, inherent biases in the medical system, all play a larger role in maternal-fetal health than race itself.
Learn from your patients when you need to understand their race and ethnicity.
When you need to understand your patient’s race and ethnicity, whether for screening purposes or to provide culturally competent care, you can start with a simple, respectful script: "I want to take the best possible care of you and be as respectful as I can. Can you tell me more about your background and your culture?"
Try to get specific information. For instance, if they say they are Native, ask what tribe. If they say they are Asian, ask what region. Be transparent about why you are asking and be clear about how this can improve their care.
Patients’ autonomy regarding disclosure of race and ethnicity should always be respected.
Above all, whether you are teaching students, conducting research, or caring for patients, it’s crucial to understand race and ethnicity, its meaning, and implications for disease prevention, health care delivery, and health outcomes. We have a professional obligation to consider, examine, and address health related social needs of each person holistically. When each of us makes a conscious effort to undo our own race-based medical biases, we take another step toward equitable health care for all patients.
View the complete Grand Rounds:
Julie Thomas
Paloma Cariello
Kalani Raphael
Gita Suneja
Kevin Shah
Justin Smith
Michelle Debbink
April Mohanty
Candace Chow
Tiffany Ho
Huntsman radiation oncologist and population health scientist Gita Suneja, and maternal-fetal medicine specialist and racial, ethnic, and geographic disparities researcher Micelle Debbink, share five practical steps health sciences educators, researchers, and clinicians can take toward delivering more equitable care today.
Accurate, self-reported race and ethnicity data is necessary to create visibility of health disparities, provide inclusive care, and improve equity of health outcomes. Redwood Health Center’s Patient Relations Specialist Nichole Misner shares how to respectfully discuss this needed health information with patients.
Accurate, self-reported race and ethnicity data is necessary to create visibility of health disparities, provide inclusive care, and improve equity of health outcomes. Community engagement director RyLee Curtis, Chief Quality Officer Sandi Gulbransen, Project Manager Kimberly Killam, Patient Experience Director Mari Ransco, Chief Medical Informatics Officer Michael Strong, and Health Sciences Equity, Diversity, and Inclusion Librarian Donna Baluchi explain how University of Utah is improving data quality.