Unmasking Implicit Biases in the Wound Care Clinic

Implicit bias, generally described as an unconscious stereotype that impacts one’s opinions of a certain person or group of people, continues to gain attention within the healthcare literature because of its potentially deleterious effect on clinical outcomes and employee relations. Unintended bias can affect any healthcare setting, but is more likely to occur in outpatient environments where patient populations may be more varied and temporary. This article will provide an overview of what defines implicit and explicit biases, ways in which biases may impact the clinical environment, and strategies that clinicians can implement to reduce the risk of harboring unintended biases.

Definitions  
Within the literature on discrimination and prejudice, implicit bias is thought of as cognitions that are unconscious or uncontrollable that can be in favor of or against a person or a group of people.¹ This term has historically also been used interchangeably with unconscious bias. An individual is typically unaware that these biases exist, even though they are fairly common. These biases develop based on personal history and experiences that shape how we tend to categorize our world. Bias can be difficult to see or acknowledge, leading to difficulties with taking steps towards eliminating it.² Explicit, or conscious, bias is more overt or purposeful based on attitudes or beliefs about a person or a group on a conscious level. Similar to implicit biases, explicit biases also typically develop based on personal history and experiences, but maybe more overt or more easily identifiable or observable. Both explicit and implicit bias play a part in judgments about individuals based on race, ethnicity, sex, gender, gender identity, sexual orientation, disability, religious affiliation, nationality, weight, political affiliation, mental illness, socioeconomic status, comorbidities, demographics, and/or an identity in which categorization occurs. Implicit bias can also occur within one’s own group. The most common way that implicit bias is measured in today’s research and practice is through an Implicit-Association Test,³ which has been extensively developed and normed to measure the strength of association between groups of people and evaluations or stereotypes.⁴ This test requires the participant to sort words into categories and then measures reaction times when pairing evaluative words relating to certain concepts. The assumption with these tests is that stronger associations between negative words and certain categories of people indicate a greater level of implicit bias against that group. 

Effects of Bias
Implicit biases may play a role in the treatment of patients in healthcare settings. In a recent review of articles on implicit biases based on race, gender, age, or weight in healthcare settings, each study found a relationship between the presence of implicit bias and lower quality of care.¹ In another review, implicit bias was found to be related to a host of variables, including patient-provider interactions, treatment decisions and adherence, and patient health outcomes.⁵ When our stereotypes lead us away from being rational or knowing and/or seeking the truth, implicit bias occurs. For example, patients who are diagnosed with obesity may receive different care than other patients without evidence that differing care is warranted. When identity intersections exist within an individual, patients are more vulnerable to providers’ distorted judgment. For instance, in a study of heart disease, disparities in care were observed among older, female patients.⁶ Further, already vulnerable people are more likely to experience prejudice, which compounds their disadvantage.⁷ In addition to patients being affected by the implicit biases of healthcare providers and staff, employees may also be affected by other employees or the patients they serve. This may contribute to poor employee satisfaction and engagement, burnout, and/or a hostile work environment. Implicit bias has been identified in hiring practices across settings, racial groups, and gender. For example, several studies show that individuals with white- or Caucasian-sounding names (note the invitation for bias just in that descriptor) were more likely to be called for follow-up interviews than were African American-sounding names,⁸ and individuals listing names appearing to be men were more likely to receive callbacks than names appearing to be women.⁹ Implicit bias research has also demonstrated differences in starting salaries, competencies, and hireability between groups.^10-12  

What Can Be Done About Bias?
There are many ways that one can attempt to address or overcome implicit bias so that it does not affect patient care and/or the workplace environment. First and foremost, it is important to increase one’s self-awareness. A recommendation for doing so is taking one of the many different implicit-association tests to gain perspective on any automatic associations that may be harbored as it relates to specific groups of people. Another recommendation is to discuss the concept of implicit bias with co-workers. Because it is generally believed that everyone brings bias to the table, developing a comfort with owning such bias and talking about it can be one way to continue to raise self-awareness and notice patterns among others that could be red flags. Fostering a culture of openness in addressing biases may help change related behaviors. Encouraging trainees to examine their awareness and receive exposure to a broad array of patients are other ways this bias can be explored. For example, providers with experience in treating patients who have a preexisting mental health diagnosis are less likely to negatively evaluate these patients when treating a medical condition.¹ Providers can examine comorbidities in scientific literature to learn the real — not just perceived — impact of such intersections. Lastly, clinicians should open a conversation with their clinic administrators (if they are not the clinic administrators themselves) about current hiring practices to minimize biases, or at least mitigate them as much as possible in the hiring process. This may be done by developing standardized means of evaluating applicants, creating a blind review in which names and genders are removed from application materials, or getting someone outside of the department to critique the process for potential evidence of biases. 

Conclusion
Unconscious or implicit bias is an arational state that can lead to decreased judgement.¹ While explicit bias is notable for its conscious availability, controllability, and intention, research indicates that implicit bias can adversely affect treatment outcomes as well as patient-provider interactions. Unequal employment opportunities and compensation indirectly influence the provision of medical care, since medical providers who reduce implicit bias in the workplace may be more satisfied and offer better care. Providers and trainees can attempt to identify negative attitudes, not in conscious awareness and mitigate their influence. Gaining awareness into implicit biases and taking steps to reduce them will decrease disparities.

Lynette J. Adams is on staff with the U.S. Department of Veterans Affairs. Caroline J. Schmidt is on faculty at the Yale School of Medicine.  

1. FitzGerald C, Hurst S. Implicit bias in healthcare professionals: a systematic review. BMC Med Ethics. 2017;18(1):19.  
2. Zestcott CA, Blair IV, Stone J. Examining the presence, consequences, and reduction of implicit bias in health care: a narrative review. Group Process Intergroup Relat. 2016;19(4):528-42.
3. Project implicit. 2011. Accessed online: https://implicit.harvard.edu/implicit/education.html
4. Greenwald AG, Banaji MR. The implicit revolution: reconceiving the relation between conscious and unconscious. Am Psychol. 2017;72(9):861-71.
5. Hall WJ, Chapman MV, Lee KM, et al. Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: a systematic review. Am J Public Health. 2015;105(12):e60-76. 
6. Lutfey KE, Link CL, Grant RW, Marceau LD, McKinlay JB. Is certainty more important than diagnosis for understanding race and gender disparities? an experiment using coronary heart disease and depression case vignettes. Health Policy. 2009;89(3):279-87. 
7. Wolff J, de-Shalit A. Disadvantage. Oxford, UK. Oxford University Press; 2007.
8. Bertrand M, Mullainathan S. Are Emily and Greg more employable than Lakisha and Jamal? a field experiment on labor market discrimination. Am Econ Rev. 2004;94(4):991-1013.
9. Moss-Racusin CA, Dovidio JF, Brescoll VL, Graham MJ, Handelsman J. Science faculty’s subtle gender biases favor male students. Proc Natl Acad Sci USA. 2012;109(41):16474-79.
10. Greene J, El-Banna MM, Briggs LA, Park J. Gender differences in nurse practitioner salaries. J Am Assoc Nurse Pract. 2017;29(11):667-72. 
11. Smith N, Cawley JF, McCall TC. Examining the gap: compensation disparities between male and female physician assistants. Women’s Health Issues. 2017;27(5):607-13.
12. Turnquist A. Earning what you are worth: guide to salary negotiations for healthcare professionals. TWC. 2018;12(9):17-9.