This study was published in 2017.

The Implicit Association Test has its flaws. Although its authors maintain that it measures external influences, it’s not clear how well it predicts individual behavior. Another, bigger systematic review of implicit bias in health care professionals was published in BMC Ethics, also in 2017. The researchers gathered 42 studies, only 15 of which used the Implicit Association Test, and concluded that physicians are just like everyone else. Their biases are consistent with those of the general population.

The researchers also cautioned that these biases are likely to affect diagnosis and care.

A study published three years earlier in the Journal of the American Board of Family Medicine surveyed 543 internal medicine and family physicians who had been presented with vignettes of patients with severe osteoarthritis. The survey asked the doctors about the medical cooperativeness of the patients, and whether they would recommend a total knee replacement.

Even though the descriptions of the cases were identical except for the race of the patients (African-Americans and whites), participants reported that they believed the white patients were being more medically cooperative than the African-American ones. These beliefs did not translate into different treatment recommendations in this study, but they were clearly there.

In 2003, the Institute of Medicine released a landmark report on disparities in health care. The evidence for their existence was enormous. The research available at that time showed that even after controlling for socioeconomic factors, disparities remained.

There’s significant literature documenting that African-American patients are treated differently than white patients when it comes to cardiovascular procedures. There were differences in whether they received optimal care with respect to a cancer diagnosis and treatment. African-Americans were less likely to receive appropriate care when they were infected with H.I.V. They were also more likely to die from these illnesses even after adjusting for age, sex, insurance, education and the severity of the disease.