The issue of racial bias in healthcare has become a topic of significant conversation over the last several years. Despite increased public awareness, the problem is still obscured by differing opinions, controversy, and even claims that racial bias in healthcare doesn’t exist.
Unfortunately, the numbers tell a different story. People of color have worse patient outcomes than other segments of the population. While there is no single solution to this problem, data may play an important role in reducing the sting of racial bias in the world of healthcare.
In 2017, a pregnant Serena Williams walked into a hospital to give birth to her daughter, Olympia. She almost never came out. What started as a routine pregnancy, ended with an emergency C-section after the baby’s heart rate dipped during contractions.
A scary but common situation that occurs every day in hospitals across the world. What happened next was less common. A series of clots, torn stitches, and coughing fits resulted in six days where the famous tennis star was battling for her life.
Serena Williams lives today thanks to the help of skilled doctors, remarkable personal resources, and the knowledge and confidence to advocate for herself.
Nevertheless, the situation called attention to an uncomfortable fact of western society. Though people living in the United States have access to historic healthcare technology that has largely shifted the birthing process into the “safe” category, black women experience stillbirths and delivery-related death at a significantly higher rate than anyone else.
Serena Williams was fortunate. Many are not. Pinpointing the exact problem is hard. One can reasonably assume that the vast majority of doctors and nurses intend to give unimpeachable care to anyone who walks into the hospital. And it remains true that thousands of black women experience tragedy on what should be one of the happiest days of their lives each year.
There are several reasons that this may be the case.
A Lack Of Understanding
The African American population is slightly more at risk for certain health conditions—including high blood pressure—that could result in dangerous outcomes during the birthing process, and other otherwise routine health-related procedures. Doctors may be less aware of these advanced risks, and not necessarily well trained in how to manage them.
Lower Rates of High-Quality Pre-natal care
It’s also true that African American women for a variety of reasons statistically experience lower rates of high-quality pre-natal, and other forms of preventative care. Regular visits to the gynecologist during pregnancy are instrumental both in detecting issues with the baby, and the woman who will be delivering them. Similarly, regular visits to the doctor can help prevent conditions from advancing over time.
While this may explain some incidents of bad healthcare outcomes, it’s far from a universal explanation. Serena Williams and many other black mothers with access to high-quality healthcare still experience risks during child delivery that may not be common to the rest of the population.
Unquestionably the thorniest of possibilities is that of unconscious bias. Unconscious bias is not a decision, but an assumption healthcare providers most likely aren’t aware they are making. Statistics indicate that doctors and nurses are more likely to take white people seriously when they self-report health conditions than they are people of color.
This is relevant when it comes not only to discrepancies in the birthing experience but also in a wide variety of healthcare-related situations.
Doctors and nurses may get rid of their unconscious bias first by recognizing it, and then by actively working to correct it. This process is imprecise and takes time.
For truly equitable healthcare it must take place, but until then, data can be used to bridge the gap that exists in current medical standards.
What Data Can Do
Without hard numbers, a well-meaning doctor can very easily read about unconscious bias and think “what a terrible thing that is happening with other doctors.” Most people don’t think of themselves as having bias, and virtually all healthcare professionals enter the field because they want to produce the best possible outcomes for all of their patients.
A doctor may be able to recognize that some physicians are guilty of unconscious bias, without necessarily believing that it affects them. Data may tell a different story, providing a granular look at patient outcomes in local hospitals, or even patient outcomes that are specific to the doctor in question.
A physician who sees that people of color are getting a lower level of care than others may be more motivated to work hard at wrestling with their own biases.
Data can also just pinpoint what conditions doctors should be more aware of. For example, if African Americans are having worse patient care outcomes because doctors historically neglect that they are more likely to suffer from high blood pressure, this can be identified in the data.
Data coupled with AI algorithms can also lend a degree of objectivity to the treatment and diagnosis process. New programs allow doctors to input patient symptoms into a database and receive a shortlist of conditions that could be behind the symptoms. With this information, bias doesn’t get a chance to rear its ugly head.
Similarly, wearable health technology may also lend itself well to improved patient outcomes for people of color. Data-taking wearables add a degree of objectivity to symptoms patients report. Doctors may take complaints from patients of any background more seriously if they can see the data that backs it up.
Data Isn’t a Perfect Answer
Naturally, data isn’t the perfect solution to the problem of bias in healthcare. Healthcare professionals still need to be willing participants in the process of providing the highest level of care for all patients. This may mean training, soul searching, and continued educational efforts that emphasize ways to bridge the care gap amongst different segments of the population.
Still, it’s a good start. For doctors and nurses who mean well but may legitimately not understand how they are undeserving people of color, data can serve as a powerful impetus toward change.