Eugenia Calvo Prieto
Implicit Biases and the Healthcare System
In social identity theory, an implicit bias (also known as an unconscious bias), refers to the thoughts, feelings, or attitudes towards a social group or social identity that is not consciously held or recognized (Staats et al., 2016). They are an example of the brain’s fast, automatic, intuitive, emotional, and unconscious thinking system (i.e. System 1), as they function outside of our own awareness. Often, we act on the basis of these biases unintentionally and are unable to recognize them.
Since social identity theory suggests that people’s self-concepts (e.g. sports teams, religions, occupations, sexual orientation, gender) are based on their membership in social groups (Leaper, 2011), this would explain how implicit biases often result in favoritism towards ingroup members as well as negative feelings and strong dislikings of outgroup members (Abrams, 2001).
There are various reasons for the development of implicit biases. One common cause is that we, as humans, take mental shortcuts. Humans tend to avoid assessing an individual’s characteristics since the cognitive effort to do so seems to be greater than directly categorizing a person into a particular group with certain characteristics (i.e. stereotypes and prejudices) (Burgess et al., 2004). This ability to sort experiences is thought to be an evolutionary development that ensured survival for our early ancestors. It allowed them to make sense of a complicated world by quickly deciding whether an encounter was friendly or dangerous. Thousands of years later, our brains still use these tendencies (Marcelin et al., 2019). These shortcuts make it faster and easier for the brain to sort through the overwhelming amounts of information it is met with every second of the day. Implicit biases are formed when these cognitive shortcuts are taken inaccurately or inappropriately, leading us to incorrectly rely on these unconscious prejudices towards certain groups of people to provide guidance in a highly complex world (Rynders, 2019).
Another reason why we develop implicit biases is social and cultural influences. Although most people would reject negative images and ideas associated with disadvantaged groups, many of us have grown up in cultures where these groups of people are often shown disparagingly or stereotypically (Glas & Faloye, 2020). Research has shown that children use group membership to guide their inferences about psychological and behavioral traits. At such a young age, these negative influences from their peers can unconsciously foster prejudicial attitudes (Baron et al., 2014).
Overall, recognizing what sets you apart from others and forming negative opinions and associations about these outgroups contributes to the development and aggravation of implicit biases.
Such biases can have an especially detrimental effect on the healthcare system (Rose & Flores, 2020). These biases can affect how healthcare professionals perceive, interact with and treat patients through patient-provider interactions and treatment decisions, and adherence. An example of this might be doing diagnostic work, recommending different treatment options for patients based on prejudicial assumptions, preventative services, acute treatment, and chronic disease management. As a result, this can lead to poor patient health outcomes, and higher disease prevalence, lower life expectancy, and increased mortality among certain patient groups.
One of the most common forms of implicit bias in the healthcare system is implicit racial bias, which is when people have an unconscious automatic preference for or prejudice against people of a certain race. An example of this is how many healthcare providers often have an implicit bias in terms of positive attitudes towards white people and negative attitudes towards people of color (Hall et al., 2015).
Another common form of implicit bias is gender bias. These are the unintentional and automatic mental associations based on the ways in which we judge men and women based on traditional feminine and masculine assigned traits. These stem from traditions, norms, values, culture, and/or experience (The Bureau for Employer's Activities & International Labour Organization, 2017). For example, women are 3 times less likely than men to receive knee arthroplasty when clinically appropriate. This is based on stereotypes that men are more athletic than women and hence more likely to engage in rigorous activities that would benefit from joint replacement (Chapman et al., 2013). These types of biases can contribute to health disparities by gender.
One other common form of implicit biases is LGBTQ+ community bias. In the healthcare system, LGBTQ+ individuals are known to experience higher levels of health disparities compared to the general population. For example, implicit biases for heterosexual people over people of the LGBTQ+ community are especially prevalent among heterosexual healthcare providers (Sabin et al., 2015). Not only do implicit biases affect the quality of care received by LGBTQ+ patients, but they can cause these patients to delay seeking care due to experiences with discrimination. For example, in a study about the utilization of veterans administration health care, 25% of sexual minority veterans avoided seeking healthcare services due to concerns about stigma (Simpson et al., 2013). Additionally, in another study, it was concluded that many lesbian patients do not screen for cervical cancer at recommended rates due to similar reasons (Tracy et al., 2010).
Therefore, it is important to promote awareness of the methods to reduce these implicit biases in the healthcare system, especially among providers who give care to disadvantaged populations.
Firstly, physicians should be encouraged to identify their own unique implicit biases. A tool that researchers have developed called the Implicit Association Test (IAT) helps reveal such biases. It requires participants to categorize negative and positive words together with either images or words (Greenwald, McGhee, & Schwartz, 1998). The main idea behind this test is that making a response is easier when closely related items share the same response key. Therefore, the more closely associated two concepts are, the easier it is to respond to them as a single unit.
Once physicians are aware of their own biases, they need to reduce their negative thoughts, beliefs, and attitudes towards those certain groups of people. One way to do this is practicing mindfulness which reduces the stress and cognitive load that leads to us relying on such biases. A recent 2015 study found that brief meditation decreased unconscious bias against black and elderly people (Lueke & Gibson, 2014) which helps provide valuable insights into the usefulness of this approach.
Physicians should also be encouraged to consciously challenge and reshape their stereotypes and prejudices. This can be done through perspective-taking, which involves taking the first-person perspective of an individual in a stereotyped group; this technique helps increase psychological proximity towards this group and improve automatic or impulsive irrational group evaluations (Devine et al., 2012). Studies have already shown how perspective-taking can increase patient satisfaction meaning it could be a useful technique in clinical practice and taking (Blatt et al., 2010). This technique would teach physicians to pause, think and reassess situations in which they have interacted with individuals from a stereotyped group or when societal stereotyping has been observed. They would be encouraged to look beyond and evaluate other points of view and consider how a patient may think or feel about diagnostic tests or treatment options, hence reducing health disparities. Therefore, this may be a possible approach that could be used to reduce the implicit biases of healthcare professionals.
Another technique is stereotype replacement which involves replacing stereotypical responses for non-stereotypical responses by firstly recognizing that a response is stereotypical and labeling it like that, reflecting on why this happened, and lastly considering how a biased response could be avoided and replaced by an unbiased response in the future (Brown et al., 2021). This would allow the physician to engage in positive interactions with stereotyped group members and change cognitive opinions and representations of the group (Morris et al., 2019).
There has already been real-world progress in the healthcare system to reduce implicit bias. An example of this is the longitudinal studies that have been conducted to achieve skill development and practice in implicit bias recognition and management (Gonzalez et al., 2020). Research into the approaches to this is also being thoroughly investigated (Sukhera et al., 2020).
Another example is the continuing medical education (CME) activities to provide education on unconscious biases in the healthcare workforce (Stanford CME | Stanford Medicine, 2017, The EveryONE Project, n.d.).
In summary, in order to fully achieve health equity, healthcare professionals have a responsibility to identify and reshape their own cognitive biases to treat all patient groups equally.
 Abrams, D. (2001). Social Identity. International Encyclopedia of the Social & Behavioral Sciences, 14306-14309. https://www.sciencedirect.com/science/article/pii/B0080430767017289
 Baron, A. S., Dunham, Y., Banaji, M., & Carey, S. (2014). Constraints on the acquisition of social category concepts. Journal of Cognition and Development, 15(2), 238-268. https://psycnet.apa.org/record/2014-16275-004
 Blatt, B., LeLaucheur, S. F., Galinsky, A. D., Simmens, S. J., & Greenberg, L. (2010). Does Perspective-Taking Increase Patient Satisfaction in Medical Encounters? Academic Medicine, 85(9), 1445-1452. https://journals.lww.com/academicmedicine/Fulltext/2010/09000/Does_Perspective_Taking_Increase_Patient.15.aspx
 Brown, T., Xu, K., & Glowinski, A. L. (2021). Cognitive Behavioral Therapy and the Implementation of Antiracism. JAMA Psychiatry, 78(12). https://www.researchgate.net/publication/351349421_Cognitive_Behavioral_Therapy_and_the_Implementation_of_Antiracism
 Burgess, D. J., Fu, S. S., & Ryn, M. v. (2004). Why Do Providers Contribute to Disparities and What Can Be Done About It? Journal of General Internal Medicine, 19(11), 1154-1159. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1494785/
 Chapman, E. N., Kaatz, A., & Carnes, M. (2013). Physicians and Implicit Bias: How Doctors May Unwittingly Perpetuate Health Care Disparities. Journal of General Internal Medicine, 28(11), 1504-1510. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797360/
 Devine, P. G., Forscher, P., Austin, A. J., & Cox, W. T. L. (2012). Long-term reduction in implicit race bias: A prejudice habit-breaking intervention. Journal of Experimental Social Psychology, 48(6), 1267-1278. https://www.researchgate.net/publication/236076408_Long-term_reduction_in_implicit_race_bias_A_prejudice_habit-breaking_intervention
 Glas, K. E., & Faloye, A. (2020). Unconscious (Implicit) Bias. Journal of Cardiothoracic and Vascular Anesthesia, 35(4), 991-992. https://www.jcvaonline.com/article/S1053-0770(20)31186-1/fulltext
 Gonzalez, C., Walker, S., Rodriguez, N., Karp, E., & Marantz, P. (2020). It Can Be Done! A Skills-Based Elective in Implicit Bias Recognition and Management for Preclinical Medical Students. Academic Medicine, 95(12), 150-155. https://journals.lww.com/academicmedicine/Fulltext/2020/12001/It_Can_Be_Done__A_Skills_Based_Elective_in.25.aspx
 Hall, W. J., Chapman, M. V., Lee, K. M., Merino, Y. M., Thomas, T. W., Payne, B. K., Eng, E., Day, S. H., & Coyne-Beasley, T. (2015). Implicit Racial/Ethnic Bias Among Health Care Professionals and Its Influence on Health Care Outcomes: A Systematic Review. American Journal of Public Health, 105(12), 60-76. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4638275/
 Leaper, C. (2011). More Similarities than Differences in contemporary Theories of social development?: A plea for theory bridging. Advances in Child Development and Behaviour, 40, 337-378. https://www.sciencedirect.com/science/article/abs/pii/B9780123864918000098
 Lueke, A., & Gibson, B. (2014). Mindfulness Meditation Reduces Implicit Age and Race Bias: The Role of Reduced Automaticity of Responding. Social Psychological and Personality Science, 6(3). https://journals.sagepub.com/doi/abs/10.1177/1948550614559651
 Marcelin, J. R., Siraj, D. S., Victor, R., Kotadia, S., & Maldonado, Y. A. (2019). The Impact of Unconscious Bias in Healthcare: How to Recognize and Mitigate It. The Journal of Infectious Diseases, 220(2), 62-73. https://academic.oup.com/jid/article/220/Supplement_2/S62/5552356?login=true
 Morris, M., Cooper, R. L., Ramesh, A., Tabatabai, M., Arcury, T. A., Shinn, M., Im, W., Juarez, P., & Matthews-Juarez, P. (2019). Training to reduce LGBTQ-related bias among medical, nursing, and dental students and providers: a systematic review. BMC Medical Education, 19(1), 325. https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-019-1727-3#ref-CR70
 Rose, J. A., & Flores, M. (2020, May 8). Heightening Awareness of Implicit Bias and Its Impact on Patient Care During COVID-19. HSS. https://www.hss.edu/conditions_heightening-awareness-implicit-bias-impact-patient-care-during-COVID-19.asp
 Rynders, D. (2019). Battling Implicit Bias in the IDEA to Advocate for African American Students with Disabilities. Touro Law Review, 35. https://digitalcommons.tourolaw.edu/cgi/viewcontent.cgi?article=2953&context=lawreview
 Sabin, J. A., Riskind, R. G., & Nosek, B. A. (2015). Health Care Providers’ Implicit and Explicit Attitudes Toward Lesbian Women and Gay Men. American Journal of Public Health, 105(9), 1831-1841. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4539817/
 Simpson, T. L., Balsam, K. F., Cochran, B. N., Lehavot, K., & Gold, S. D. (2013). Veterans Administration Health Care Utilization Among Sexual Minority Veterans. Psychological Services, 10(2), 223-232. https://www.researchgate.net/publication/237015242_Veterans_Administration_Health_Care_Utilization_Among_Sexual_Minority_Veterans
 Staats, C., Capatosto, K., Wright, R. A., & Jackson, V. W. (2016). State of the Science: Implicit Bias Review. Kirwan Institute. http://kirwaninstitute.osu.edu/implicit-bias-training/resources/2016-implicit-bias-review.pdf
 Stanford CME | Stanford Medicine. (2017, May 23). Unconscious Bias in Medicine. https://stanford.cloud-cme.com/course/courseoverview?P=3000&EID=20775
 Sukhera, J., Watling, C., & Gonzalez, C. (2020). Implicit Bias in Health Professions: From Recognition to Transformation. Academic Medicine, 95(5), 717-723. https://journals.lww.com/academicmedicine/Fulltext/2020/05000/Implicit_Bias_in_Health_Professions__From.21.aspx
 The Bureau for Employer's Activities & International Labour Organization. (2017). Breaking barriers: Unconscious gender bias in the workplace. ILO. https://www.ilo.org/wcmsp5/groups/public/---ed_dialogue/---act_emp/documents/publication/wcms_601276.pdf
 The EveryONE Project. (n.d.). Implicit Bias Resources. AAFP. https://www.aafp.org/family-physician/patient-care/the-everyone-project/toolkit/implicit-bias.html
 Tracy, J. K., Lydecker, A. D., & Ireland, L. (2010). Barriers to Cervical Cancer Screening Among Lesbians. Journal of Women's Health, 19(2), 229-237. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2834453/