Recent studies have shown that healthcare providers hold unconscious bias through the use of the Implicit Association Test (IAT). The IAT was developed in 1998 and is the now the most readily available computerized online tool used to measure and bring awareness to unconscious bias in published litertature (Greenwald et al., 1998). The IAT measures the response time of unconscious associations based on various traits including, but not limited to: race, disability, gender, and ethnicity (Greenwald et al., 1998). The IAT requires the participant to categorize stimuli, such as words, labels, and pictures, into opposing classifications as quickly as possible. For example, a participant could express a faster reaction between a negative word, like “violence” and a picture of a Black face vs. a White face.

Utilizing the IAT, a multitude of research has examined clinician’s implicit bias and created discussion around how this can impact patient care. A study by Green, et al. showed clinicians associate black patients with being less cooperative with treatment compared to white patients and that black patients experience less patient centered care from clinicians with greater implicit bias (Green et al., 2007; Cooper et al., 2012; Blair et al., 2013; Oliver et al., 2014). Another study by Penner, et al. aimed to examine the relationship of oncologists who did not self-identify as black with patients who had a confirmed diagnosis of breast, colorectal, or lung cancer and self-identified as black or African American (Penner et al., 2016). Prior to seeing the patient, providers were administered the IAT and their implicit racial bias was scored. Results showed that patients who were seen by providers with higher implicit bias scores felt their appointment was not patient centered and felt it was more difficult to remember what they had discussed during the appointment. Further when the appointment was reviewed, providers with greater implicit bias practiced less supportive communication skills (p < 0.01) and spent a significantly shorter amount of time interacting with patients (p = 0.02) (Penner et al., 2016).

Although the IAT is widely utilized in research its validity and applicability has been questioned. Many psychologists have criticized the IAT for its arbitrary scoring system, its provision of results that are left to be interpreted differently by each individual, and the reality that an individual’s score can change based off of their surroundings (Azar, 2008). Reviews of research utilizing the IAT show that there is a lack of evidence showing that the test is a valid predictor of behaviors and explicit outcomes (Blanton et al., 2009; Schimmack, 2019). Regardless of critiques, the IAT is still a popular, widely used tool in identification of implicit bias, and it may prove to be useful when incorporated into a meaningful diversity and inclusion curriculum (Marcelin et al., 2019). While it can be utilized to measure unintentional preferences that can impact patient care decisions, we need more scholarship of discovery around the degree IAT predicts behavior and the changes of results over a longitudinal period of time.

As awareness of clinician implicit bias and its impact on patient care and healthcare disparities grows, a push to provide education and training against implicit bias are also being employed throughout the US. Recently the state of California has enacted legislation requiring healthcare providers to participate in implicit bias training, as many institutions across the US already require (Hagiwara et al., 2020). Current research aims to address the issue of how to effectively discuss and unlearn personal implicit bias. Using tools such as the IAT and its variations can be useful in helping clinicians recognize their unconscious, implicit bias, but is recognizing bias enough to make a difference in behaviors? Current research shows that use of the IAT in conjunction with facilitated discussions on bias may be a more useful technique in dispelling bias than using the IAT alone (Stone and Moskowitz, 2011; Hagiwara et al., 2020). One study examined how the use of IATs followed by facilitated discussion impacted emergency medicine residents’ views on their own implicit bias (Zeidan et al., 2018). The residents filled out an online survey which focused on their awareness of personal bias and the topic of implicit bias pre and post IAT. The outcome post IAT and discussion showed residents were significantly more aware of their individual implicit bias (p = 0.003) and how this can influence delivery of care (p = 0.03). However, there were participants who were skeptical of the results and expressed that they were upset about the lack of neutrality in the results (Zeidan et al., 2018). Other studies recommend using “mindfulness practice” to help eliminate many of the cognitive stressors associated with implicit bias and the potential to reduce perpetration of one’s bias onto others. Implicit bias scores from the IAT were lower in participants who participated in mindful meditation sessions (Burgess et al., 2017). Data relevant to the concept of implementing the IAT in conjunction with supplemental learning techniques for successful anti-bias training is growing, however there is still a lack of uniform anti-bias trainings implemented among healthcare professionals.

Recognizing one’s own bias is difficult to do and is only one component of bias mitigation. Research shows that even if a conscious effort is made to uncover our own biases, it cannot fully mitigate implicit biases. Self-awareness does require one to start recognizing a flaw in thinking and admit to tendencies that maybe consciously unrealized. This can be especially difficult for healthcare providers as oaths are taken to “do no harm” whether it be unintentional or not. However, this is what is required in order to start making change both within ourselves and within a system that has allowed implicit bias to impact patient care and further health inequity gaps.