Implicit bias refers to the ways our behavior might subconsciously "act on the basis of prejudice and stereotypes without intending to do so" (Stanford Encyclopedia of Philosophy). Bias is in our biology: our brains are inundated with information as we interact with the world and to handle all this data, our brains create shortcuts of associations. These shortcuts that were meant to protect us from threats now encourage us to make assumptions about the people we interact with daily. People may consciously hold beliefs that align with equality, but their backgrounds, the media they have consumed, and their learned behaviors can influence them to favor individuals that look, believe, and behave just like them. Conversely, these biases can influence us to exclude individuals that fit outside of this box.
Implicit bias can negatively affect every aspect of life, but in the context of medicine, biases result in inferior care and poorer health outcomes. A lot of work is yet to be done in this area, but we do know many instances where implicit bias impacts health disparities. For example:
Black patients with diabetes are 4 times more likely to have a limb amputated compared to white patients (Mizelle, 2021).
Hispanic and Black patients are less likely to be prescribed opioids because of widespread assumption of misuse (Chapman, 2013).
Women are 3 times less likely than men to undergo knee arthroplasty (Chapman, 2013).
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Although not a perfect measure of implicit bias, Harvard's Project Implicit developed online self-assessments called Implicit Association Tests (IAT). These assessments use words and images to highlight the ease of association between positive and negative concepts with groups of people. The assessments are available in many topics, including race, gender, religion, and more. You may notice many studies utilize IAT to measure bias.
Research on mitigating implicit bias is now gaining footing in peer-reviewed literature, but much work and appraisal is yet to be done. Most studies examining the effectiveness of implicit bias interventions contain limited sample sizes and effect sizes and contain no examination of long-term efficacy. In a systematic review conducted by FitzGerald, Martin, Berner and Hurst, the most effective interventions featured exposure to individuals that do not fit commonly held stereotypes, identifying commonalities between oneself and groups experiencing bias, evaluative conditioning, and inducing emotion through music or moral elevation (2019).
Jennifer Eberhardt, author and professor of psychology at Stanford University, suggests that combating implicit bias may come down to interrupting the shortcuts our brain utilizes that lead to assumption.