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Virtual reality simulations reveal potential biases in patient care

Study shows genomic information can unknowingly influence medical decisions depending on race.

Researchers at the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health, recently published a study using virtual reality simulations to investigate how clinicians might use a type of genomic risk information called polygenic risk scores in medical care.

Polygenic risk scores report an estimate of the measure of disease risk based on the predicted combined effects of small variations in our DNA that are known to be associated with a specific health condition such diabetes and cancer.

The study was conducted by the research group headed by Susan Persky, Ph.D., an associate investigator in the Social and Behavioral Branch of the NHGRI Intramural Research Program. The study was led by Brittany Hollister, Ph.D., in collaboration with Vence Bonham Jr., J.D., NHGRI’s acting deputy director and intramural associate investigator.

The researchers used virtual reality to simulate a patient consultation, studying how medical students used polygenic risk scores in making clinical recommendations to virtual patients. The researchers specifically examined how the patient’s race factored into the recommendations.

When the medical student participants donned their virtual reality headset, they encountered a virtual patient who appeared as either Black or white, in all cases talking about their fatigue and knee pain. Some of these patients provided a report with their polygenic risk scores for several common health conditions.

With at-home genomic testing becoming increasingly accessible, individuals willing to submit a DNA sample and pay a fee can find out more about their possible genomic risks for a variety of health conditions, such as breast cancer, obesity and heart diseases. Although such risk predictions have not yet been proven fully useful or accurate, such testing may improve in the future, and individuals may start to bring polygenic risk reports to visits with their healthcare providers.

But little is known about how clinicians will use this type of genomic risk information and whether an individual’s risk profiles will influence what clinicians recommend to their patients.

In a conversation with science writer Sonja Soo, Ph.D., Dr. Persky explains the findings from the study and what it means to use genomic information in a more equitable way.

The interview was edited and condensed for clarity.

Sonja Soo: Can you tell me more about the background of the study?

Susan Persky: Researchers are increasingly using genomic data in the clinic for diagnosing rare health conditions. But for common health conditions, information such as polygenic risk scores are not currently as accurate at predicting risk. Various direct-to-consumer companies are making these risk scores available to anyone. But researchers know very little about how these new polygenic risk scores will be used or received in medical clinics.


For this study, we wanted to predict how these polygenic risk reports will be applied in the healthcare system and understand whether introducing this information will influence how a doctor might view a patient and make medical recommendations.


We know from previous research that primary care providers generally don’t have a lot of experience with genetic and genomic information. When there is uncertainty in a situation, people typically lean back on their mental shortcuts, such as biases, heuristics and stereotypes. One area where that is applied often in medicine is in racial categorization of patients. So, with this study, we wanted to know what happens when polygenic risk information is brought to a medical clinic and how that is affected by the patient’s apparent race.

Soo: Why is it important to study how race affects genomic medicine?

Persky: We must make genomic medicine broadly accessible for it to be successful, especially for those that haven’t had access in the past. We must also make genomic medicine equitable across populations. Research like this study can help us figure out potential issues and biases associated with using polygenic risk scores in the clinic, such as using patient race as a cue to make medical decisions in a way we hadn’t anticipated.


Using genomic medicine equitably has been an issue in the past. We are now just beginning to create polygenic risk scores that apply to non-European populations. The next step is to think about how we need to communicate about these new technologies and what kind of problems we might run into down the road so we can prepare.

Soo: What are some of the key findings from this study?

Persky: Medical students who received these polygenic risk reports made different recommendations to the virtual patient based on the additional genomic information. For example, medical students are more likely to recommend observing kidney function and screening for diabetes in response to the patient’s symptoms when they had the genomic risk information.


These medical students also viewed the patients with polygenic risk information as less healthy and in need of more care. We’ve seen that in the past, in which genomic risk information is thought of almost as a pre-diagnosis.


Lastly, we saw differences in clinical recommendations based on patient race. For example, we found that participants were more likely to recommend assessing diabetes when the patient was Black, which is in line with racial disparities in diabetes rates in the United States.


In a couple cases, the polygenic risk scores seemed to be used differently depending on the patient’s race. For example, medical students were more likely to recommend monitoring kidney function when taking over-the-counter medication if the patient was white and had the genomic risk information available.

Soo: Were there any results that surprised you?

Persky: When we asked the medical student participants, they said that they didn’t think the polygenic risk scores were useful in the clinic and that they shouldn’t be integrated into care plans. But when the medical students received the polygenic risk scores, they had a different perception of the patient’s health and made different recommendations. So having these genomic risk scores influenced their medical decision-making, even though they were not aware of it.

Soo: What does it mean to have these unintentional biases with genomic data in healthcare?

Persky: We all need to be aware of our biases in our day-to-day life. For the medical student participants, their daily work involves learning how to make medical decisions. It’s important to consider any assumptions that we make about another person and how that can influence our thoughts and decisions.


But, like most things in healthcare, the responsibility is not solely towards the individual. Educating healthcare providers is part of it, but we are going to need systemic approaches. For example, we can consider how we are communicating this genomic risk information to healthcare providers and how to help them use it equitably. Our knowledge of how polygenic risk information is used in the clinic is not at the stage where we can make specific recommendations, but our study has found potential pitfalls and places where information isn't — but should be — used equitably.

Last updated: November 9, 2022