Eliminating Medical Bias Starts with Studying Patterns

It’s a hard truth to swallow, but everyone has implicit biases that influence our thoughts and actions. And as hard as we may try, doctors are not exempt from this phenomenon. We’re trained to be non-prejudiced when treating the patients who have entrusted us with their care, but unfortunately, that’s not always the reality of the situation. 

The human brain is constantly detecting patterns about our environment—it’s a key function in how humans learn and make decisions. But humans are imperfect, and that means we sometimes make associations and internalize patterns that aren’t actually true…or helpful. Some of the information we absorb perpetuates negative stereotypes based on attributes like race, gender, and sexual orientation. And that can have detrimental effects on patient care.

Studies show that racial minorities and women are less likely to receive accurate diagnoses and appropriate pain management leading to worse clinical outcomes. In a study of black cancer patients and their doctors, researchers found that physicians with high levels of implicit bias were less supportive of their patients. The patients in the study viewed these physicians as less patient-centered and had more difficulty remembering what the physician told them, had less confidence in their treatment plan and thought it would be more difficult to follow the recommended plan. 

At the same time, medically treating patients in a completely demographic-blind vacuum ignores important factors in an individual’s health. We need to find the right balance of being cognizant of our own biases, while still acknowledging the unique health challenges our patients face.

Capturing the right data

Our inability to pinpoint inequality by maximizing the use of data and analytic intelligence is a massive failing within the health system. 

If I wanted to find out how many MRI centers are available I could. But when I try to access information on a patient’s ethnicity, race, or social determinants of health, I hit a wall. Information that would help me understand a patient’s holistic well-being, any inequality they might be facing and how best to treat them is often absent or very difficult to access from our health system records.

The COVID-19 pandemic truly illuminated this problem across the healthcare system. When we study groups that have been affected the most by the disease, it’s clear that , Latinx and Native American populations have been greatly impacted. We had information on how age and preexisting conditions affected susceptibility and severity of COVID, but we often  have little to no visibility into this racial/ethnic disparity. To truly understand our patients and their unique care needs, we need to start capturing and utilizing demographic data in a standard and comprehensive way.

It’s just math

The key is to remove the human element (and inevitable bias) in pattern processing. How do we do this? The answer lies in leveraging technology and data analytics, so we can ultimately protect the human element when practicing medicine. 

The wide-spread implementation and adoption of an “inequality disparity calculator” would have transformative effects on the way we view and manage vulnerable subpopulations across our system. And equally as important, this agnostic tool could alert us to subpopulations we didn’t know existed.

This calculator could take a wealth of rich demographic and clinical data and evaluate a number of variables to alert physicians of individuals/groups that are at higher-risk of certain diseases or conditions—or are more vulnerable to other physical and social health disparities. By relying on analytics, we minimize room for human bias by relying on objective information to show where inequality lies, so we can collectively act in the patient’s best interest. 

If we had this kind of tool before the COVID-19 pandemic, it would have been clear early-on which subpopulations were having the worst morbidity and mortality rates. We could have acted on that information by being mindful of the risk these groups face when rendering treatment and through proactive community outreach, education and other public health initiatives. 

ID populations that live in the gap

The collective healthcare system tends to focus on fixing the problems that we can see. Reducing extremely high utilization of the ED. Lowering readmission rates. Managing opioid prescriptions and substance use disorder. We use data to find patterns and manage individuals, but how often do we consider what’s happening outside our walls? We aren’t considering health disparities that occur before a patient arrives at a hospital.

For example, a study reported residents that live in neighborhoods that are primarily black, Latinx, or poor are more likely to have an out-of-hospital cardiac arrest, and they are less likely to receive bystander CPR and thus less likely to survive. We can use this insight to proactively address the root issue by working with public health to target these communities and offer CPR training courses.

To identify these subpopulations that live in the gap, we need to put more resources into studying demographic information on entire communities. I suspect we would uncover many unrealized social health needs that prevent these patients from getting the care they need. If we know what these needs are, we can work with government agencies and community resources to more effectively understand and support these populations--be it through CPR training, food services, rental assistance, etc. 

There’s a massive opportunity for the healthcare community to be using analytics in a very different way than we ever have before. It comes down to identifying patterns in the data and also looking at those patterns through different disparity lenses. Only then will we be able to truly understand the magnitude and depth of inequality across our system and how we can work together to fix it. 

Related Reading: Another Statistic: Protecting our Sickle Cell Disease Patients from the Pain of Prejudiced Care

Dr. Amit Shah previously served as CareOregon’s Senior Medical Director of Network and Clinical Support. Dr. Shah has served as a board member of CareOregon, Jefferson Health Information Exchange, Northwest Regional Primary Care Association and Comagine board of directors. Dr. Shah received his medical training at the Drexel University School of Medicine, Philadelphia, and undergraduate degree in Molecular Genetics at the University of Rochester. He has a biomedical informatics certification from Oregon Health & Science University and is board certified in family medicine and a member of the American Academy of Family Physicians.

This article is provided through a collaborative effort with Collective Medical

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