Eliminating Explicit and Implicit Biases in Health Care: Evidence and Research Needs

1Although expressions of explicit bias have declined in the United States over time, implicit bias has remained unrelenting. Health care providers hold negative explicit and implicit biases against many marginalized groups of people, including racial and ethnic minoritized populations, disabled populations, and gender and sexual minorities, among others. Implicit bias permeates the health care system and affects patients via patient–clinician communication, clinical decision making, and institutionalized practices. Higher education systems, including medical schools and academic hospitals, have been affected by the discrimination and bias that have long permeated the health care delivery system. Bias in admissions and promotions processes, in classroom and bedside instruction, and by health care providers contributes to the constant messaging that stereotypes and isolates marginalized groups. These biases hinder improvement in compositional diversity of health care providers, long recognized as an important mechanism in reducing health care disparities. This complex system of discrimination and biases causes devastating health inequities that persist despite a growing understanding of the root causes and the health care system’s professional, ethical, and moral responsibility to address these inequities.

2It has been theorized that implicit bias and structural racism mutually reinforce one another—ambient structural racism and its outcomes reinforce an individual’s psychological associations between racial identity and poorer outcomes (implicit bias). Inequitable structural determinants have diminished housing, education, health care, and income and have increased exposure to environmental pollutants and chronic stressors for marginalized populations. Structural inequities and discrimination have created stereotypes of marginalized populations or communities and implicit and explicit biases toward them. Health care providers hold negative explicit and implicit biases against racialized minorities. A similar reinforcing dynamic may exist for marginalized populations such as those who are overweight/obese, use wheelchairs, have limited English proficiency, have mental health illness, and belong to lower socioeconomic classes. These biases can facilitate the creation and perpetuation of discriminatory systems and practices, creating a complex feedback loop that sustains itself.

3Addressing bias remains a fundamental professional responsibility of health care and public health professionals accountable for population health and wellness. This article (a) provides an overview of existing evidence of bias among health professionals, health practitioners, and public health workers in the practice and training environments (and lay health workers as appropriate) and its impact on health disparities; (b) systematically reviews the extant literature for evidence and limitations of current interventions designed to reduce or manage biases; (c) explores the interaction between bias and structural elements of the health care system (including medical education); and (d) proposes a conceptual model that frames bias not as an independent factor in the generation of disparities but as one element of a reinforcing system of elements that perpetuates such disparities. Ultimately, we provide evidence that interventions designed to reduce or manage existing explicit and implicit biases in clinical settings and public health are insufficient and will continue to fall short in reducing health inequities if we do not concomitantly address the racism and discrimination ingrained in health, medical educational systems, and other societal structures.

4Critical to an understanding of interventions that address explicit and implicit biases in health care is an understanding of key terminology, tools used to measure bias, and the evidence for and impact of these biases in health care.

5Implicit biases are unconscious mental processes that lead to associations and reactions that are automatic and without intention; actors have no awareness of the associations with a stimulus (Table 1). Axt et al. maintain that social status is relational and people unconsciously hold more negative attitudes or feelings about membership of an outgroup (people with whom they do not share identities) than about membership of an ingroup (people with whom they share identities). A stereotype is a fixed set of attributes associated with a social group.

6Implicit bias goes beyond stereotyping to include favorable or unfavorable evaluations toward groups of people (Table 1). Although we are not aware these implicit biases exist, they have a significant impact on decision making.

7A belief is explicit if consciously endorsed. Explicit forms of bias include preferences, beliefs, and attitudes of which people are generally consciously aware, personally endorse, and can identify and communicate. Discrimination, the result of either implicit or explicit biases, is the inequitable treatment and/or impact of general policies, practices, and norms on individuals and communities based on social group membership. Daumeyer et al. argue that implicit biases must be exposed and discussed so that people and institutions can be held accountable for their effects. They argue for nuanced conversations about the ways in which implicit biases shape behavior and the ways to combat it.

8In 1998, Greenwald et al. described a word association test that identified implicit stereotype effects through indirect reaction time measures even when subjects self-reported low measures of prejudice. Since then, the implicit association test (IAT) has consistently demonstrated implicit stereotyping for a range of different social categories, particularly gender and ethnicity (Table 1). Greenwald et al. maintain that statistically small effects of the IAT can have socially large effects. A meta-analysis by Greenwald et al. demonstrated the predictive validity of the IAT regarding implicit stereotype associations to behavioral outcomes across a range of social subject areas. Some critics challenge whether the IAT measures implicit bias and predicts behavior, and question its utility in clinical and other real-world situations. Most researchers agree that the IAT has limitations. It does not have high test-retest reliability in the same individual, and it is not useful as a tool to label individuals as implicitly sexist or racist or to predict behavior. The IAT has been used in health professions education as a metric to demonstrate the efficacy of educational interventions meant to reduce implicit bias and as a tool to raise awareness of existing implicit bias among health care trainees and providers.

9Implicit racial and ethnic bias exists among health care professionals in favor of White patients and against Black, Hispanic, and dark-skinned patients even when all other major factors (e.g., socioeconomic differences, insurance status) have been controlled and accounted for. Hall et al. published a systematic literature review of 15 studies designed to explore the evidence of provider implicit racial bias and health outcomes. In the studies measuring prevalence, rates of anti-Black bias in health care providers ranged from 42% to 100%. These findings were redemonstrated in similar reviews conducted in 2017 and 2018.

10Hoffman et al. demonstrated in 2016 that White medical students and residents were more likely to believe that Black patients had thicker skin and smaller brains, and were more likely to rate Black patients as feeling less pain than and not needing the same levels of pain medications as White patients. Several studies have demonstrated that negative implicit biases held by those in the health professions are similar to those seen in the lay population.

11The Medical Student Cognitive Habits and Growth Evaluation Study (CHANGES) has provided the greatest insight into the implicit and explicit biases held by medical students and trainees in the United States. This longitudinal multimeasure study followed a large sample of students attending a stratified random sample of 49 US allopathic medical schools and measured associations between possible interventions and levels of biases held by students. A web-based survey completed by more than 4,500 first-year medical students demonstrated that most students exhibited implicit (74%) and explicit (67%) weight bias. The study also demonstrated that scores of implicit weight bias were similar to scores of implicit bias against racial minorities (74%) in the same group of students. The size and scope of this study demonstrate undeniable evidence that implicit bias is pervasive among medical students, even in the first year of medical school. The multiple papers and findings generated by this foundational study were excluded from the final selection of studies in the results section because the study was observational and did not introduce interventions.

12Biases affect health care delivery and public health outcomes, the health professions workplace and learning environments, and the diversity of trainees and workforce (Table 2). Hall et al. demonstrated that these implicit biases have negatively affected patient–provider interactions, treatment decisions, and patient adherence to treatment. The most consistent evidence is found in studies of patient–provider interactions in which the bias of health care providers has been repeatedly linked to discriminatory care—patients rate physicians with higher levels of implicit bias as less patient-centered in the primary care setting. Blanchard & Lurie demonstrated that patients who perceived that they would have received better treatment if they were of a different race were significantly less likely to receive optimal chronic disease screening and more likely to not follow the doctor’s advice or to delay care. In a large study of adult primary care, higher implicit bias among health care providers was associated with patients’ lower ratings of interpersonal treatment, contextual knowledge, communication, and trust.

13Other studies have confirmed associations between provider bias (demonstrated via IAT testing) and disparate treatment of their patients. In a systematic literature review, six studies found that higher implicit bias among health care providers was associated with disparities in treatment recommendations, expectations of therapeutic bonds, pain management, and empathy. Seven studies that examined the impact of implicit provider bias on real-world patient–provider interaction found that health care providers with stronger implicit bias demonstrated poorer patient–provider communication and that health care providers with high implicit biases (a) provided lower rates of postoperative narcotic prescriptions for Black children than for White children, (b) had poorer bonding with Black patients than with White patients, and (c) made disparate recommendations for thrombolytic therapy for Black patients and White patients.

14A study of 3,756 students at 49 US medical schools demonstrated that high scores of racism as measured by the three variables were significantly correlated with low scores of student intentions to work in underserved areas and to provide care to minority populations.

15Implicit bias affects not only patients but also trainees and faculty within health care systems. A 2014 systematic literature review revealed that rates of harassment and discrimination against trainees (24% reported racial discrimination, 33% reported sexual harassment, and 54% reported gender discrimination) have remained unchanged over time. Minority trainees report facing daily bias and microaggressions and having feelings of isolation and substantial stress. Minority medical students reported five-times-higher odds of racial discrimination and isolation than did nonminority peers. Stereotype threat (defined in Table 1) is common, particularly among non-White students, interferes with learning, and adds to the cognitive load of minoritized students. Thus, bias in health professions training can affect the performance of racialized minorities. Early and small differences in assessed clinical performance, which may be affected by implicit biases, lead to larger differences in grades and selection for awards [e.g., Alpha Omega Alpha Honor Medical Society (AOA)], ultimately affecting career trajectories of racial minority candidates. For example, significant differences in negative descriptive words on medical students’ evaluations have been found across different racial and gender groups. Membership in AOA, conferred to only 16% of each graduating medical school class, has effectively barred diversity in many specialties and may represent a longstanding form of structural racism.

16Literature outside of health care has introduced techniques to manage implicit bias, including stereotype replacement (replacing stereotypical responses to bias with nonstereotypical ones), counter-stereotypic imaging (imagining known counter-stereotypical people), individuation (learning personal attributes of persons present rather than identifying group attributes), perspective taking (taking the perspective of persons present), and increasing opportunities for contact. Several studies have explored the efficacy of these interventions. Strikingly, the only study demonstrating reduction of measured implicit bias was conducted on undergraduate students enrolled in a course using a prejudice-habit-breaking intervention involving instruction of all the aforementioned techniques with effects lasting 8 weeks. Unfortunately, these results may not be generalizable and have not been reproduced. Lai et al. tested nine interventions and although all immediately reduced implicit preferences, results were sustained for only several hours to days. FitzGerald et al. conducted in 2019 a systematic review of bias interventions utilizing the IAT or other measures across multiple disciplines. They found that most studies did not provide robust data to support many interventions, although perspective taking was more successful than counter-stereotypic imaging.

17Implicit bias has important interactions with structural elements of the health care system. Evidence suggests that implicit bias can reinforce structural dimensions of the health care system that generate disparities. Other evidence suggests that structural dimensions of the health care system and medical education can reinforce implicit bias. These interactions suggest a complex and mutually reinforcing relationship between implicit bias and structural elements of the health care system.

18Implicit biases influence the decisions of policy makers in government and health care that result in structural racism. Public health responses to the coronavirus disease 2019 (COVID-19) pandemic offer evidence of this dynamic. Despite data demonstrating that non-Hispanic Black populations and Hispanic populations were dying at a younger average age (71.8 years and 67.3 years) than non-Hispanic White patients were (80.9 years), the phase 1b vaccination strategy targeted individuals age 75 and older. Thus, federal public health recommendations ignored or discounted the evidence that an age-based approach would lead to further disparities in COVID-19 infections and mortality, amounting to structural racism against Black and Hispanic populations.

19Studies have consistently shown that decision makers burdened with higher cognitive load are more likely to make biased decisions. A more recent study of physicians in the emergency department has confirmed that cognitive stressors such as patient overcrowding and patient load were associated with increased implicit racial bias as measured by a race IAT preshift compared to postshift.

20Unfortunately, to date, medical education and educators have not adequately addressed the implicit biases that place marginalized patients at high risk of receiving disparate care and suffering poorer health outcomes. In fact, Phelan et al. concluded that structural racism is at play in medical education through many medical schools’ formal and hidden curricula. In contrast to a formal curriculum, which can be measured by the number of hours students receive training related to racial disparities and bias, structured service-learning, minority health activities, cultural awareness programming, and the completion of an IAT, the hidden curriculum is unofficial and often more powerful, consisting of faculty role modeling, institutional priorities around the interracial climate, and experiences of microaggressions.

21Most medical students continue to believe that both race and gender (as opposed to sex) are genetic and biological constructs. Even when students are taught otherwise, the practice of race-based medicine reinforces these characterizations. When students are taught about health disparities without the appropriate contextualization of structural racism, historic segregation, the pathologization of gender and sexual orientation, and the medical professions’ complicity in scientific racism, students may assume there is something inherently wrong with racialized minorities rather than with the systems that have harmed them. Students are often taught that race, instead of racism, is an independent risk factor for disease. They learn to associate race with any number of diseases. They are taught to incorporate the race of their patient into the opening line of clinical presentations even though there is no evidence that race is relevant to the establishment of diagnoses. They learn to use race-based algorithms to calculate glomerular filtration rates, pulmonary function testing, hypertension guidelines, and even urinary tract infection diagnoses in pediatric populations. Such messaging only serves to undo any structured teaching on the social construct of race and gender.

22As discussed above, there is substantial evidence that implicit bias results in health care disparities through mechanisms including disparate care and trust. But the relationship between implicit bias and outcomes may be bidirectional. Evidence has shown that implicit attitudes are malleable and that such attitudes are learned and strengthened through repeated observation of particular classes of people in valued or devalued circumstances. For example, individuals exposed to less favorable exemplars from a given identity demonstrate increased implicit bias and stereotypes with respect to that entire group. Furthermore, these investigators showed that changing exposure to more favorable exemplars can diminish established implicit bias. This phenomenon has been demonstrated in experiments looking specifically at race- and age-related attitudes. These findings suggest that a practitioner’s implicit bias toward a marginalized group may be augmented or diminished by the clinical outcomes of that group.

23The CHANGES study demonstrated that students’ implicit bias against sexual minorities was reduced at 42 medical schools and increased at only 7 schools. Reduced bias was associated with more frequent interaction with LGBT students, faculty, and patients; the perceived quality of that contact; and increased training involving skills in caring for sexual minorities.

24The CHANGES study found that changes in student implicit racial attitudes were independently associated with formal curricula related to disparities in health and health care, cultural competence, and minority health; informal curricula (or hidden curricula, defined in Table 1), including racial climate and role model behavior; and the amount and favorability of interracial contact during medical school.

25Thus, carefully designed structural elements of the learning environment can favorably affect the implicit biases and wellness of students.

26A systematic literature review was performed with the goal of assessing the efficacy of extant interventions designed to reduce the explicit and implicit biases of health care providers and of learners across the continuum of health professions education.

27We searched three databases (ERIC, PubMed, and MedEdPORTAL) using key terms (Figure 1). The terms “implicit bias,” prejudice,” and “stigma” were often used inter-changeably and the terms “bias” and “biases” yielded more than 100,000 articles, often with little relevance to implicit bias in the health professions. We found, as did FitzGerald et al. in their systematic review, that indexing in databases for these terms was inconsistent and that titles and abstracts were often imprecise. We conducted repeated searches with and without these terms, comparing the number of search results. We developed a set of terms most frequently encountered in the titles and abstracts of irrelevant articles and defined important terminology (Table 1) to narrow the search. We reviewed the references of landmark articles and used the advanced search function to increase the likelihood that no key articles were missed.

28A study had to include health care professionals, assess an intervention (e.g., training, workshop, didactics, contact, program) designed to address explicit or implicit bias held by health care providers, be written in English, and be published between May 2011 and May 2021. We excluded commentaries, theoretical frameworks, editorials, and institutional or societal pledges that address racism, although these were reviewed for context. We did not exclude qualitative studies, studies without comparison groups, or studies outside North America. However, although we did find studies from other countries detailing explicit and implicit biases, we did not find articles with interventions addressing these biases for inclusion in this review. We extracted subjects, intervention format (e.g., lectures, workshops, discussions, panels, interviews), target (e.g., knowledge, skills, attitudes, IAT), and summary of key findings.

29We excluded abstracts that did not include original research or bias reduction as an expected outcome; that did not employ a discrete intervention or, like the CHANGES study, retrospectively identified effective interventions; or that studied populations other than health professions students, trainees, or providers. We excluded articles that focused on self-stigma (e.g., from a diagnosis of obesity, HIV, sexually transmitted infection, mental health) and community-based interventions, as they were not focused specifically on the bias of health professionals. Observational studies without discrete interventions were excluded but were reviewed in Section 1.

30Title, abstract, and full-text review were conducted by three authors (M.B.V., A.I.E., and N.A.S.) and coded to consensus.

31Twenty-five studies met inclusion criteria (Table 3). None of the studies mentioned in Sections 1 and 2 met inclusion criteria but were reviewed because of their significant contributions to the understanding of the interactions of implicit bias in learning and clinical settings. Most studies (68%) engaged medical students and utilized classroom or web-based interventions. Most studies did not have a control group (72%) and none used actual clinical settings. Three studies focused on interventions for implicit bias of faculty serving on admissions or search committees.

32The three studies of faculty serving on admissions or search committees reported increased awareness of biases, but none reported bias reduction or long-lasting impact.

33Three studies followed subjects 3, 4, and 6 months post-intervention, but only one noted a lasting positive impact.

34All studies addressing implicit bias among health care providers raised awareness of implicit bias through didactic instruction, discussions, workshops or other reflection-based techniques (e.g., service-learning, photovoice, contact-based interventions, theater reading; see Table 4), or an IAT or similar measure.

35Despite the limitations noted in Section 2, the IAT continues to be widely utilized. The IAT and other measures of implicit bias, stigma, and attitudes toward groups of persons were used among subjects to (a) demonstrate the existence of participant implicit biases, (b) act as a springboard to create cognitive dissonance for oral and/or written reflection and to practice bias management skills, and (c) evaluate interventions. Gonzalez et al. found that using the IAT without priming on its results and without a follow-up debriefing led some subjects (22%) to question the validity of the measure and the existence of implicit biases, and therefore advised judicious use of the IAT and trained facilitators. Subjects who accepted the results of the IAT were not able to develop management strategies for those biases without dedicated instruction.

36Despite having low explicit bias based on a self-reported survey, admissions committee members at The Ohio State University College of Medicine had high levels of implicit preference for White versus Black students as measured by the Black-White IAT. Results were presented to committee members with strategies to reduce implicit bias. The following admissions cycle resulted in an increase in underrepresented minority matriculation from 17% to 20%, a change that was not statistically significant.

37Seventy-six percent of studies instructed on structural determinants such as structural racism and/or historic oppression of groups so that subjects could explore explicit and implicit biases. All these studies demonstrated an increased awareness of bias, and subjects often voiced a willingness to address their biases. Four studies explored the use of contact with groups with identities such as LGBTQI and persons with mental illness with positive and negative results, respectively.

38In recognition that biases may be immutable in the current health care context but can be managed, educators have used transformative learning theory (TLT) in concert with implicit bias management techniques. TLT transforms the individual’s existing paradigm by disrupting assumptions and then engaging in critical reflection and dialogue to interpret the disruptions. TLT may move learners to an “inclusive, self-reflective and integrative frame of reference” (, p. 718). This paired approach has had early success. Sherman et al. engaged both residents and faculty in transformative learning to address issues of race, racism, and Whiteness and created an environment for critical dialogue incorporating practical recommendations for addressing implicit bias in clinical practice. Focus groups 4 months later revealed that subjects noted increased awareness of their biases and sustained commitment to addressing racial bias, to challenging their own clinical decision making, and to engaging leadership in dialogue regarding bias.

39Gonzalez et al. describe implicit bias recognition and management (IBRM), a process that promotes conscious awareness of biases and fosters behavioral changes. IBRM supposes that biases are difficult to reduce and should therefore be managed. IBRM has helped medical students interrupt biases in learning and clinical settings. Wu et al. paired IAT administration with training to improve skills in bias literacy, emotional regulation, and allyship (Table 4). Trainees practiced these skills in clinical vignettes and improved their confidence in addressing bias in real-world settings. All three studies created a brave space to explore biases and emphasized continued practice and development of skills.

40These studies have multiple limitations. They often lacked control groups or used pre- and postcomparison designs. They had limited longitudinal follow-up and often were not performed in real-world clinical or learning environments. Many studies did not focus on targeted outcomes, and most did not access the continuum of learners in medical education such as practicing health care providers and leadership. Most interventions had a limited one-time delivery with no opportunity to measure a dose- or time-dependent effect.

41Many of the interventions demonstrated successful promotion of awareness of implicit bias held among subjects as well as an interest in mitigating implicit biases among subjects. No intervention in this review, however, achieved sustained reduction of implicit bias among health care professionals or trainees. In addition, no study demonstrated that an intervention improved clinical outcomes, the learning environment, interprofessional team dynamics, patient care, health disparities, patient satisfaction, or satisfaction of health professionals. Studies were hampered by lack of statistical analysis, lack of control group, limited numbers of participants, findings that are not necessarily generalizable from the classroom or web-based setting to the clinical or real-world setting, and heavy reliance on qualitative assessments or nonvalidated instruments. Future studies should also assess whether regularly timed booster interventions manifest in sustained changes over time and should have longer-term follow-up to assess sustainability of initial gains. Future studies should include educational models that use direct clinical observation or standardized patients. Studies should assess health care trainees’ ability to incorporate skills into patient communication and shared decision making, their improvement of clinical delivery practices, their interactions with colleagues, and their teaching practices.

42Based on Jones’s allegory A Gardener’s Tale, we present a conceptual model of implicit biases of health care providers and the key structural factors affecting these biases (Figure 2). In the vicious cycle of health disparity, students, trainees, and providers receive a constant barrage of messaging that reinforces biases. The soil of their work (practice and learning environments) is laden with structural bias from racialized medicine, a biased learning environment, and poor compositional diversity. Furthermore, these trainees and health care providers are under substantial time pressure and cognitive load. These characteristics of the practice and learning environments may be considered structural determinants of implicit bias.

43Biases are now primed as the clinician moves to provide care to patients (see the left side of Figure 2). When caring for marginalized patients, the provider’s bias influences communication with the patient, potentially resulting in suboptimal decision making. The patient may sense the bias, may distrust the provider and system, and may decide to not follow through on treatment plans or may modify them. The patient lives in underresourced and unhealthy spaces that contribute to poor outcomes. The provider notes the poor outcomes and their implicit bias is confirmed. Health care disparities are exacerbated. Further exacerbation of the vicious cycle occurs when this dynamic is accompanied with biases toward students, trainees, and providers from marginalized groups. Individuals from these marginalized groups are less likely to succeed, confirming biases about them and perpetuating poor diversity in the health care workforce. The benefits of diversity to education and patient care are lost.

44The right side of Figure 2 depicts the virtuous cycle of health equity. A well-resourced provider learning and working within an environment devoid of racialized medicine and bias and characterized by compositional diversity is less likely to display biases against the patient. Compositional diversity also increases the likelihood that the provider shares lived experiences with the patient. The patient notes the absence of provider bias, develops a trusting relationship, adheres to the treatment plan in a well-resourced environment, and returns with improved health outcomes. The patient’s outcome confirms the provider’s more favorable bias. Health care disparities are reduced.

45This conceptual model highlights two important dynamics in the perpetuation of implicit bias and its impact on care. First, structural determinants in the health care system and surrounding community contribute to the development of implicit bias toward marginalized patient populations and then reinforce that implicit bias through generation of poorer patient outcomes. Second, interruption of this cycle is possible only through an overall shift toward favorable structural influences on implicit bias. Discrete, time-limited training as the sole intervention to reduce implicit bias is unlikely to result in sustained change; health care providers return to a practice or learning environment that is often replete with structural determinants and patient outcomes that reinforce implicit bias. To avoid the ongoing creation and perpetuation of racist structures in society, systems, and organizations, it is crucial to recognize that these dynamics may enhance the implicit bias of medical leaders and policy makers as well.

46To enable provider-level bias interventions to succeed in improving health outcomes, multiple other concurrent approaches should address structural factors inside and outside the health care system that influence these biases.

47Structural inequities outside the health care system include poor access to high-quality health care, racialized violence, the carceral state, crowded housing, healthy food scarcity, lack of access to green spaces, environmental toxins, and poorly protected workspaces, among other issues related to geography and place.

48Structural inequities inside the health care system that prime bias include the work and learning environments of students, trainees, and providers. It will be important to address these structural drivers of bias, including time pressures, cognitive load, and the practice of racialized medicine. Racism, sex and gender discrimination, and other forms of discrimination must be rooted out, as they prevent marginalized trainees and faculty from thriving, create stereotype threat for the marginalized, and confirm bias for the nonmarginalized. Bioethical principles of fairness, distributive justice, and reciprocity should be core for public health officials and health care providers, and practitioner and provider trainings in these areas can raise awareness. For example, to address health inequities laid bare by COVID-19, Peek et al. recommend a multifactorial approach that acknowledges the systemic racism of the health care system and other societal structures as well as the biases of providers.

49Addressing compositional diversity in health care is another avenue for treating the structures that influence implicit and explicit biases and eliminate health care disparities. Minority health professionals are underrepresented in the workforce and health professions faculty. Only 6.2% of medical students identify as Hispanic or Latinx, and only 8.4% as Black or African American. Gender parity among medical school students has been achieved. However, women are underrepresented at the faculty instructor level, with substantially less representation at the professor level, and are also underrepresented in hospital leadership, with even starker inequities for female racial and ethnic minorities. Gender inequalities in salaries have been well documented. In academic medicine, Black male faculty are offered lower rates of compensation than their White counterparts and are less likely to be awarded research funding from the National Institutes of Health. Similarly, in 2016, graduate student enrollment in the Association of Schools and Programs of Public Health demonstrated a ≤5% increase over a 20-year period among Asian, Black, Hispanic, and Native American students; only 11.1% of students were Black and 12% were Hispanic. Black, Hispanic, and Native American representation among tenured public health faculty increased <3% during this same 20-year period.

50TLT, IBRM, and a skills-based approach offer promise for future interventions in implicit bias management. It is also encouraging that discussions around disparities and inequities have moved from race to racism and have focused on the professional responsibility of providers to root out inequities and manage biases. The extant literature regarding the use of provider-level implicit bias interventions suggests that these interventions can play an important role in concert with other interventions that more broadly address bias and discrimination inside and outside the health care system. Evidence supports the use of provider-level interventions in immediate-impact activities such as decision making on search committees or admissions committees and raising critical awareness of the bioethical principles of fairness, distributive justice, and reciprocity. However, provider-level implicit bias interventions alone have not improved health outcomes. Thus, provider-level implicit bias interventions should be accompanied by interventions that systemically change structures inside and outside the health care system that influence biases and perpetuate health inequities.