Course content © Copyright 2024 by William W. Deardorff, Ph.D, ABPP. All rights reserved.

Implicit biases in healthcare have been found to contribute to health disparities, professionals' attitudes toward and interactions with patients, quality of care, diagnoses, and treatment decisions. This course will explore definitions of implicit and explicit bias, the nature of implicit biases, and how they can affect health outcomes. Because implicit biases are unconscious, strategies will be reviewed to assist in raising professionals' awareness of and interventions to reduce them.

OVERVIEW

As a brief overview of implicit bias, review the following video (Concepts Unwrapped) from the McCombs School of Business. This video is a part of Ethics Unwrapped, a free online educational video series about ethics produced by the Center for Leadership and Ethics at The University of Texas at Austin. Ethics Unwrapped offers an innovative approach to introducing complex ethics topics and behavioral ethics ideas in a way that is accessible to both students and instructors. You can find the video here:

Implicit Bias: Concepts Unwrapped

INTRODUCTION

In the 1990s, social psychologists Dr. Mahzarin Banaji and Dr. Tony Greenwald introduced the concept of implicit bias and developed the Implicit Association Test (IAT) as a measure. In 2003, the Institute of Medicine published the report Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care highlighting the role of health professionals' implicit biases in the development of health disparities. The phenomenon of implicit bias assumes that while well-meaning individuals may deny prejudicial beliefs, these implicit biases negatively impact their clinical communications, interactions, and diagnostic and treatment decision-making.

One explanation is that implicit biases are a cognitive, heuristic, or mental shortcut. These “shortcuts” offer individuals general rules to apply to situations in which there is limited, conflicting, or unclear information. Use of a heuristic results in a quick judgment based on fragments of memory and knowledge, and therefore, the decisions made may be erroneous. If the thinking patterns are flawed, negative attitudes can reinforce stereotypes.

In health contexts, this can be a problem because clinical judgments can be biased and adversely affect health outcomes. The Joint Commission provides the following example: A group of physicians congregate to examine a child's x-rays but has not been able to reach a diagnostic consensus. Another physician with no knowledge of the case is passing by, sees the x-rays, and says "Cystic fibrosis." The group of physicians was aware that the child is African American and had dismissed cystic fibrosis because it is less common among Black children than White children.

Review the following video on implicit bias in medicine (Implicit Bias in Medicine “The Elephant in the Waiting Room; The Empathy Project).

Implicit Bias in Medicine The Elephant in the Waiting Room

The purpose of this course is to provide health professionals with an overview of implicit bias. This includes an exploration of definitions of implicit and explicit bias. The nature and dynamics of implicit biases and how they can affect health outcomes will be discussed. Finally, because implicit biases are unconscious, strategies will be reviewed to assist in raising professionals' awareness of and interventions to reduce them.

The Kirwan Institute for the Study of Race and Ethnicity

The Kirwan Institute has developed a short video course that will introduce you to insights about how our minds operate and help you understand the origins of implicit associations.

The Institutes offers a total of four modules with brief lessons in each. For this course, we only require reviewing Module 1 and Module 3; however, if you so desire, there is excellent information in the other Modules.  

Module 1: Understanding implicit bias.

Introduction

What is implicit bias

Implicit bias in action

Origins of our bias

Module 3: Learn your biases.

How do we measure our implicit associations?

What is the IAT?

Learn your biases (IAT)

Understanding your results

IMPLICIT VS. EXPLICIT BIAS

In a sociocultural context, biases are generally defined as negative evaluations of a particular social group relative to another group. Explicit biases are conscious, whereby an individual is fully aware of his/her attitudes and there may be intentional behaviors related to these attitudes. For example, an individual may openly endorse a belief that women are weak and men are strong. This bias is fully conscious and made explicitly known to others. The individual's ideas may then be reflected in his/her work as a manager.

The term "implicit bias" refers to the unconscious attitudes and evaluations held by individuals. These individuals do not necessarily endorse the bias, but the embedded beliefs/attitudes can negatively affect their behaviors. Implicit biases can start as early as three years of age. As children age, they may begin to become more egalitarian in what they explicitly endorse, but their implicit biases may not necessarily change in accordance with these outward expressions. Because implicit biases occur on the subconscious or unconscious level, particular social attributes (e.g., skin color) can quietly and insidiously affect perceptions and behaviors. According to Georgetown University's National Center on Cultural Competency, social characteristics that can trigger implicit biases include:

• Age
• Disability
• Education
• English language proficiency and fluency
• Ethnicity
• Health status
• Disease/diagnosis (e.g., HIV/AIDS)
• Insurance
• Obesity
• Race
• Socioeconomic status
• Sexual orientation, gender identity, or gender expression
• Skin tone
• Substance use

An alternative way of conceptualizing implicit bias is that an unconscious evaluation is only negative if it has further adverse consequences on a group that is already disadvantaged or produces inequities. Disadvantaged groups are marginalized in the healthcare system and vulnerable on multiple levels; health professionals' implicit biases can further exacerbate these existing disadvantages.

When the concept of implicit bias was introduced in the 1990s, it was thought that implicit biases could be directly linked to behavior. Despite the decades of empirical research, many questions, controversies, and debates remain about the dynamics and pathways of implicit biases.

OTHER COMMON DEFINITIONS

In addition to understanding implicit and explicit bias, there is additional terminology related to these concepts that requires specific definition.

Cultural Competence

Cultural competence is broadly defined as practitioners' knowledge of and ability to apply cultural information and appreciation of a different group's cultural and belief systems to their work. It is a dynamic process, meaning that there is no endpoint to the journey to becoming culturally aware, sensitive, and competent. Some have argued that cultural curiosity is a vital aspect of this approach.

Cultural Humility

Cultural humility refers to an attitude of humbleness, acknowledging one's limitations in the cultural knowledge of groups. Practitioners who apply cultural humility readily concede that they are not experts in others' cultures and that there are aspects of culture and social experiences that they do not know. From this perspective, patients are considered teachers of the cultural norms, beliefs, and value systems of their group, while practitioners are the learners. Cultural humility is a lifelong process involving reflexivity, self-evaluation, and self-critique.

Discrimination

Discrimination has traditionally been viewed as the outcome of prejudice. It encompasses overt or hidden actions, behaviors, or practices of members in a dominant group against members of a subordinate group. Discrimination has also been further categorized as lifetime discrimination, which consists of major discreet discriminatory events, or everyday discrimination, which is subtle, continual, and part of day-to-day life and can have a cumulative effect on individuals.

Diversity

Diversity "encompasses differences in and among societal groups based on race, ethnicity, gender, age, physical/mental abilities, religion, sexual orientation, and other distinguishing characteristics". Diversity is often conceptualized into singular dimensions as opposed to multiple and intersecting diversity factors.

Intersectionality

Intersectionality is a term to describe the multiple facets of identity, including race, gender, sexual orientation, religion, sex, and age. These facets are not mutually exclusive, and the meanings that are ascribed to these identities are inter-related and interact to create a whole.

Institutional racism

Institutional racism (structural) “refers to the processes of racism that are embedded in laws (local, state and federal), policies, and practices of society and its institutions that provide advantages to racial groups deemed superior while differentially oppressing, disadvantaging or otherwise neglecting racial groups viewed as inferior.”

Prejudice

Prejudice is a generally negative feeling, attitude, or stereotype against members of a group. It is important not to equate prejudice and racism, although the two concepts are related. All humans have prejudices, but not all individuals are racist. The popular definition is that "prejudice plus power equals racism". Prejudice stems from the process of ascribing every member of a group with the same attribute.

Race

Race is linked to biology. Race is partially defined by physical markers (e.g., skin or hair color) and is generally used as a mechanism for classification. It does not refer to cultural institutions or patterns. In modern history, skin color has been used to classify people and to imply that there are distinct biologic differences within human populations. Historically, the U.S. Census has defined race according to ancestry and blood quantum; today, it is based on self-classification. There are scholars who assert that race is socially constructed without any biological component. For example, racial characteristics are also assigned based on differential power and privilege, lending to different statuses among groups.

Racism

Racism is the "systematic subordination of members of targeted racial groups who have relatively little social power…by members of the agent racial group who have relatively more social power". Racism is perpetuated and reinforced by social values, norms, and institutions.

There is some controversy regarding whether unconscious (implicit) racism exists. Experts assert that images embedded in our unconscious are the result of socialization and personal observations, and negative attributes may be unconsciously applied to racial minority groups. These implicit attributes affect individuals' thoughts and behaviors without conscious awareness. Structural racism refers to the laws, policies, and institutional norms and ideologies that systematically reinforce inequities resulting in differential access to services such as health care, education, employment, and housing for racial and ethnic minorities.

MEASUREMENT OF IMPLICIT BIAS: THE IAT

Project Implicit is a research project sponsored by Harvard University and devoted to the study and monitoring of implicit biases. It houses the Implicit Association Test (IAT), which is one of the most widely utilized standardized instruments to measure implicit biases. The IAT is based on the premise that implicit bias is an objective and discreet phenomenon that can be measured in a quantitative manner. Developed and first introduced in 1998, it is an online test that assesses implicit bias by measuring how quickly people make associations between targeted categories with a list of adjectives. For example, research participants might be assessed for their implicit biases by seeing how rapidly they make evaluations among the two groups/categories career/family and male/female. Participants tend to more easily affiliate terms for which they hold implicit or explicit biases. So, unconscious biases are measured by how quickly research participants respond to stereotypical pairings (e.g., career/male and family/female). The larger the difference between the individual's performance between the two groups, the stronger the degree of bias. Since 2006, more than 4.6 million individuals have taken the IAT, and results indicate that the general population holds implicit biases.

If you would like to take the Implicit Association Test (IAT), you can do so at the following link (Outsmarting Implicit Bias, Harvard University). This is not required as part of this CE course but is highly recommended.

The Implicit Association Test: Self-Test

Measuring implicit bias is complex, because it requires an instrument that is able to access underlying unconscious processes. While many of the studies on implicit biases have employed the IAT, there are other measures available. They fall into three general categories: the IAT and its variants, priming methods, and miscellaneous measures, such as self-report, role-playing, and computer mouse movements. This course will focus on the IAT, as it is the most commonly employed instrument.

The IAT is not without controversy. One of the debates involves whether IAT scores focus on a cognitive state or if they reflect a personality trait. If it is the latter, the IAT's value as a diagnostic screening tool is diminished. There is also concern with its validity in specific areas, including jury selection and hiring. Some also maintain that the IAT is sensitive to social context and may not accurately predict behavior. Essentially, a high IAT score reflecting implicit biases does not necessarily link to discriminating behaviors, and correlation should not imply causation. A meta-analysis involving 87,418 research participants found no evidence that changes in implicit biases affected explicit behaviors.

HEALTH DISPARITIES

Implicit bias has been linked to a variety of health disparities. Health disparities are differences in health status or disease that systematically and adversely affect less advantaged groups. These inequities are often linked to historical and current unequal distribution of resources due to poverty, structural inequities, insufficient access to health care, and/or environmental barriers and threats. Please review the following video as an overview to health disparities and implicit bias in healthcare from the Outsmarting Implicit Bias (Harvard University):

Outsmarting Implicit Bias: Bias in Healthcare

Health disparity has been defined as: “a particular type of health difference that is closely linked with social, economic, and/or environmental disadvantage”. Health disparities adversely affect groups of people who have systematically experienced greater obstacles to health based on their racial or ethnic group; religion; socioeconomic status; gender; age; mental health; cognitive, sensory, or physical disability; sexual orientation or gender identity; geographic location; or other characteristics historically linked to discrimination or exclusion.

The Institute of Medicine has implicated implicit bias in the development and continued health disparities in the United States. Despite progress made to lessen the gaps among different groups, health disparities continue to exist (See also Disparities in Health and Health Care: Five Key Questions and Answers). One example is racial disparities in life expectancy among Black and White individuals in the United States. Life expectancy for Black men is 4.4 years lower than White men; for Black women, it is 2.9 years lower compared with White women. Hypertension, diabetes, and obesity are more prevalent in non-Hispanic Black populations compared with non-Hispanic White groups (25%, 49%, and 59% higher, respectively). In one study, African American and Latina women were more likely to experience cesarean deliveries than their White counterparts, even after controlling for medically necessary procedures. This places African American and Latina women at greater risk of infection and maternal mortality.

Gender health disparities have also been demonstrated in the research. Self-rated physical health (considered one of the best proxies to health) is poorer among women than men. Depression is also more common among women than men. Lesbian and bisexual women report higher rates of depression and are more likely than non-gay women to engage in risky behaviors such as smoking and binge drinking, perhaps as a result of LGBTQ -related stressors. They are also less likely to access healthcare services.

Socioeconomic status also affects health care engagement and quality. In a study of patients seeking treatment for thoracic trauma, those without insurance were 1.9 times more likely to die compared with those with private insurance.

ADDITIONAL COURSE MATERIALS

For this course, please review the article “Implicit bias in healthcare professionals: a systematic review” (Fitzgerald and Hurst, 2017). The article can also be found here.

Abstract. Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person on the basis of irrelevant characteristics such as race or gender. This review examines the evidence that healthcare professionals display implicit biases towards patients.The evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wider population. The interactions between multiple patient characteristics and between healthcare professional and patient characteristics reveal the complexity of the phenomenon of implicit bias and its influence on clinician-patient interaction. The most convincing studies from our review are those that combine the IAT and a method measuring the quality of treatment in the actual world. Correlational evidence indicates that biases are likely to influence diagnosis and treatment decisions and levels of care in some circumstances and need to be further investigated. Our review also indicates that there may sometimes be a gap between the norm of impartiality and the extent to which it is embraced by healthcare professionals for some of the tested characteristics.

For this course, please review the article Eliminating explicit and implicit biases in health care: evidence and research needs (Vela et al., 2022). The article can also be found here.

Abstract. Health care providers hold negative explicit and implicit biases against marginalized groups of people such as racial and ethnic minoritized populations. These biases permeate the health care system and affect patients via patient–clinician communication, clinical decision making, and institutionalized practices. Addressing bias remains a fundamental professional responsibility of those accountable for the health and wellness of our populations. Current interventions include instruction on the existence and harmful role of bias in perpetuating health disparities, as well as skills training for the management of bias. These interventions can raise awareness of provider bias and engage health care providers in establishing egalitarian goals for care delivery, but these changes are not sustained, and the interventions have not demonstrated change in behavior in the clinical or learning environment. Unfortunately, the efficacy of these interventions may be hampered by health care providers’ work and learning environments, which are rife with discriminatory practices that sustain the very biases US health care professions are seeking to diminish. We offer a conceptual model demonstrating that provider-level implicit bias interventions should be accompanied by interventions that systemically change structures inside and outside the health care system if the country is to succeed in influencing biases and reducing health inequities.

For this course, please review the video Implicit bias in healthcare. Tulane Medicine Grand Rounds. Quinn Capers, M.D., Vice Dean for Faculty Affairs, The Ohio State University College of Medicine.

References and Resources

Castillo, EG, Isom, J, DeBonis, KL, Jordan, A, Braslow, JT, & Rohrbaugh R. (2020). Reconsidering systems-based practice: advancing structural competency, health equity, and social responsibility in graduate medical education. Acad. Med., 95(12), 1817-1822.

Centers for Disease Control and Prevention. Health Disparities Among Youth. Available at https://www.cdc.gov/healthyyouth/disparities/index.htm. Last accessed March 15, 2024.

De Houwer, J. What is Implicit Bias? Available at https://www.psychologytoday.com/us/blog/spontaneous-thoughts/201910/what-is-implicit-bias. Last accessed March 15, 2024.

Edgoose, J, Quiogue, M, & Sidhar, K. How to identify, understand, and unlearn implicit bias in patient care. (2019). Fam Pract Manag., 26(4), 29-33.

FitzGerald, C, & Hurst, S. (2017). Implicit bias in healthcare professionals: a systematic review. BMC Med Ethics., 18(1), 19.

FitzGerald, C, Martin, A, Berner, D, & Hurst, S. (2019). Interventions designed to reduce implicit prejudices and implicit stereotypes in real world contexts: a systematic review. BMC Psychol., 7(1), 29.

Georgetown University National Center for Cultural Competence. What the Literature Is Telling Us. Available at https://nccc.georgetown.edu/bias/module-2/2.php. Last accessed March 15, 2024.

Hansen, M, Schoonover, A, Skarica, B, Harrod, T, Bahr, N, & Guise J-M. (2019). Implicit gender bias among US resident physicians. BMC Med Educ., 19(1), 396.

Johnson, R, & Richard-Eaglin, A. Combining SOAP notes with guided reflection to address implicit bias in health care. (2020). J Nurs Educ., 59(1),59-59.

Lai, CK, & Wilson, ME. Measuring implicit intergroup biases. (2020). Soc Personal Psychol Compass., 15(1).

Mayo Clinic. Consumer Health: Mindfulness Exercises. Available at https://www.mayoclinic.org/healthy-lifestyle/consumer-health/in-depth/mindfulness-exercises/art-20046356. Last accessed March 15, 2024.

National Center for States Courts. Strategies to Reduce the Influence of Implicit Bias. Available at https://horsley.yale.edu/sites/default/files/files/IB_Strategies_033012.pdf. Last accessed March 15, 2024.

National Center for States Courts. Addressing Implicit Bias in the Courts. Available at https://www.nccourts.gov/assets/inline-files/public-trust-12-15-15-IB_Summary_033012.pdf?q_DMMIVv0v_eDJUa1ADxtw59Zt_svPgl. Last accessed March 15, 2024.

Ogungbe, O, Mitra, AK, & Roberts, JK. (2019). A systematic review of implicit bias in health care: a call for intersectionality. IMC Journal of Medical Science, 13(1), 1-16.

Sagynbekov, K. Gender-Based Health Disparities: A State-Level Study of the American Adult Population. Available at https://milkeninstitute.org/sites/default/files/reports-pdf/103017-Gender-BasedHealthDisparities.pdf. Last accessed March 15, 2024.

The Joint Commission, Division of Health Care Improvement. Quick Safety 23: Implicit Bias in Health Care. Available at https://www.jointcommission.org/-/media/tjc/documents/newsletters/quick-safety-issue-23-apr-2016-final-rev.pdf. Last accessed March 15, 2024.

Vela, M.B., Erondu, A.I., Smith, N.A., Peek, M.E., Woodruff, J.N., & Chin, M.H. (2022). Eliminating Explicit and Implicit Biases in Health Care: Evidence and Research Needs. Annual Review of Public Health, 43(1), 477-501.