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Published On Jun 10, 2015

Clinical Research

Health Care Blindsided

Mahzarin Banaji explores the role of personal bias in medical care.

Racial minorities—African Americans, Native Americans, Latinos and others—receive measurably poorer health care. Economic status and insurance coverage tell only part of that story, as poorer care remains constant even when financial factors are accounted for. While many forces may play into this fact, one grim possibility must be faced: Minorities may be victims of conscious and unconscious attitudes about race held by their physicians, nurses and other health care workers. 

How does bias take root in the brain—and how can it be rooted out? That has been the focus of more than three decades of research by Mahzarin R. Banaji, Richard Clarke Cabot Professor of Social Ethics in the department of psychology at Harvard University. In her new book, Blindspot: Hidden Biases of Good People, Banaji and co-author Anthony Greenwald, a professor of psychology at the University of Washington, explore the impact of prejudices and favoritism in health care and beyond. 

 

Q: Your new book explores the influence of bias in many fields, including medicine. Can you share an example?

A: Gender bias among researchers has been shown in a number of studies. A team headed by Jo Handelsman, a professor of molecular biology at Yale University, found that both men and women scientists selected male candidates over equally qualified females for the job of lab manager. Those men were also offered a bigger starting salary and more potential mentoring. Other research has shown that women with symptoms of heart disease aren’t always transported to health facilities as rapidly as men. African Americans and women with heart attack symptoms aren’t given cardiac catheterizations and other appropriate clinical tests at the same rate as whites and men. The list goes on.

 

Q: Some studies in this field use the Implicit Association Tests, something you helped develop. 

A: The IAT asks people to make rapid judgments about words or pictures shown on a computer screen. The Race IAT presents, one at a time, black faces, white faces, good words (“joy,” “love,” “peace,” etc.) and bad words (“evil,” “failure,” “hurt,” etc.), and registers rapid responses by having the subject press one of two computer keys. The results can be surprisingly effective in revealing underlying preferences by showing stronger associations between certain concepts like white or black with good or bad.

 

Q: And you used the IAT to measure bias in medicine?

A: We found that physicians’ IAT-measured race attitudes helped predict the quality of care they provided. Of course, consciously these physicians intend to do the right thing for their patients. But physicians who displayed stronger automatic white preferences on the IAT made cardiac treatment decisions that favored white patients relative to African Americans. Other studies show that black patients of physicians with similar IAT-measured biases perceived their physicians as being less helpful. 

Bias can also manifest itself as selective helping. A friend of mine cut her hand and was rushed to the emergency room. The physician called in the best-known hand specialists in New England to perform surgery—but only after he learned she was a Yale professor. Helping people with whom we have some shared group identity is a likely way that discrimination often happens.  

 

Q: What can be done to correct or avoid unconscious bias? 

A: Becoming aware of one’s bias is a huge step forward. The key is to bring hidden biases into consciousness in order to beat them. Just as cars are increasingly being built to communicate to drivers when another car or person is in their blind spot, medical professionals can put their minds to becoming alert to bias in their own minds. The ultimate goal is improved decision-making. 

 

Q: How have you incorporated your research into your own life?

A: In so many different ways! My computer screensaver is programmed to flash images of people performing counter-stereotypical behavior. My favorite example is an old New Yorker cover with a woman construction worker breastfeeding her infant on a work break. That image pulls apart my assumptions about who women are, and who construction workers are, in so many different directions, ones that I hope will help me have a more open mind.  

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