Diversity Matters

Color lines: Disparities in pharmacy treatment, education, and practice

AAIM Recommendations to Promote Equity in the Clerkship Clinical Learning Environment

ContextStudies documenting racial/ethnic disparities in health care frequently implicate physicians’ unconscious biases. No study to date has measured physicians’ unconscious racial bias to test whether this predicts physicians’ clinical decisions.ObjectiveTo test whether physicians show implicit race bias and whether the magnitude of such bias predicts thrombolysis recommendations for black and white patients with acute coronary syndromes.Design, Setting, and ParticipantsAn internet-based tool comprising a clinical vignette of a patient presenting to the emergency department with an acute coronary syndrome, followed by a questionnaire and three Implicit Association Tests (IATs). Study invitations were e-mailed to all internal medicine and emergency medicine residents at four academic medical centers in Atlanta and Boston; 287 completed the study, met inclusion criteria, and were randomized to either a black or white vignette patient.Main Outcome MeasuresIAT scores (normal continuous variable) measuring physicians’ implicit race preference and perceptions of cooperativeness. Physicians’ attribution of symptoms to coronary artery disease for vignette patients with randomly assigned race, and their decisions about thrombolysis. Assessment of physicians’ explicit racial biases by questionnaire.ResultsPhysicians reported no explicit preference for white versus black patients or differences in perceived cooperativeness. In contrast, IATs revealed implicit preference favoring white Americans (mean IAT score = 0.36, P < .001, one-sample t test) and implicit stereotypes of black Americans as less cooperative with medical procedures (mean IAT score 0.22, P < .001), and less cooperative generally (mean IAT score 0.30, P < .001). As physicians’ prowhite implicit bias increased, so did their likelihood of treating white patients and not treating black patients with thrombolysis (P = .009).ConclusionsThis study represents the first evidence of unconscious (implicit) race bias among physicians, its dissociation from conscious (explicit) bias, and its predictive validity. Results suggest that physicians’ unconscious biases may contribute to racial/ethnic disparities in use of medical procedures such as thrombolysis for myocardial infarction.

Implicit racial bias has been well studied in adults, including among orthopaedic surgeons, through the Implicit Association Test (IAT). Recent studies suggest implicit race bias is also present among children. Explicit racial preference has been studied in children through The Clark Doll Test since the 1930s. The purpose of this study was to determine whether implicit and explicit racial biases are present among pediatric orthopaedic patients. A prospective, cross-sectional survey was administered to pediatric orthopaedic patients aged 7 to 18 years at clinics in a tertiary pediatric hospital setting. The survey included a Clark Doll Test to determine whether pediatric patients expressed explicit bias, followed by a race IAT to determine whether pediatric patients expressed implicit bias. Preference and magnitude of implicit bias as demonstrated on the IAT was calculated using standard D-scores. A total of 96 patients were consented and included in this study. Overall, pediatric patients demonstrated a slight pro-White implicit bias (M = 0.22) on IAT testing. Pediatric patients who identified as White or European American and Hispanic or Latinx both had the strongest pro-White implicit bias (M = 0.35). Patients who identified as Black or African American demonstrated no implicit racial bias (M = -0.13) on IAT testing. No notable explicit bias was observed in participants of any racial background. This study contributes evidence that pediatric orthopaedic patients express implicit racial bias on IAT testing, with an overall slight pro-White bias. It also provides insight into the dissociation of implicit and explicit racial bias in childhood and adolescence. We encourage future research on implicit bias among pediatric patients in the orthopaedic community to provide a better understanding and possible solutions to bias-related challenges in health care.

We investigated whether clinicians' explicit and implicit ethnic/racial bias is related to black and Latino patients' perceptions of their care in established clinical relationships. We administered a telephone survey to 2,908 patients, stratified by ethnicity/race, and randomly selected from the patient panels of 134 clinicians who had previously completed tests of explicit and implicit ethnic/racial bias. Patients completed the Primary Care Assessment Survey, which addressed their clinicians' interpersonal treatment, communication, trust, and contextual knowledge. We created a composite measure of patient-centered care from the 4 subscales. Levels of explicit bias were low among clinicians and unrelated to patients' perceptions. Levels of implicit bias varied among clinicians, and those with greater implicit bias were rated lower in patient-centered care by their black patients as compared with a reference group of white patients (P = .04). Latino patients gave the clinicians lower ratings than did other groups (P <.0001), and this did not depend on the clinicians' implicit bias (P = .98). This is among the first studies to investigate clinicians' implicit bias and communication processes in ongoing clinical relationships. Our findings suggest that clinicians' implicit bias may jeopardize their clinical relationships with black patients, which could have negative effects on other care processes. As such, this finding supports the Institute of Medicine's suggestion that clinician bias may contribute to health disparities. Latinos' overall greater concerns about their clinicians appear to be based on aspects of care other than clinician bias.

The medical community recognizes the importance of confronting structural racism and implicit bias to address health inequities. Several curricula aimed at teaching trainees about these issues are described in the literature. However, few curricula exist that engage faculty members as learners rather than teachers of these topics or target interdisciplinary audiences. We developed a longitudinal case conference curriculum called Health Equity Rounds (HER) to discuss and address the impact of structural racism and implicit bias on patient care. The curriculum engaged participants across training levels and disciplines on these topics utilizing case-based discussion, evidence-based exercises, and two relevant conceptual frameworks. It was delivered quarterly as part of a departmental case conference series. We evaluated HER's feasibility and acceptability by tracking conference attendance and administering postconference surveys. We analyzed quantitative survey data using descriptive statistics and qualitatively reviewed free-text comments. We delivered seven 1-hour HER conferences at our institution from June 2016 to June 2018. A mean of 66 participants attended each HER. Most survey respondents (88% or more) indicated that HER promoted personal reflection on implicit bias, and 75% or more indicated that HER would impact their clinical practice. HER provided a unique forum for practitioners across training levels to address structural racism and implicit bias. Our aim in dissemination is to provide meaningful tools for others to adapt at their own institutions, recognizing that HER should serve as a component of larger, multifaceted efforts to decrease structural racism and implicit bias in health care.

A call for grounding implicit bias training in clinical and translational frameworks

Disparities between Black and White Americans persist in medical treatment and health outcomes. One reason is that physicians sometimes hold implicit racial biases that favor White (over Black) patients. Thus, disrupting the effects of physicians' implicit bias is one route to promoting equitable health outcomes. In the present research, we tested a potential mechanism to short-circuit the effects of doctors' implicit bias: patient activation, i.e., having patients ask questions and advocate for themselves. Specifically, we trained Black and White standardized patients (SPs) to be "activated" or "typical" during appointments with unsuspecting oncologists and primary care physicians in which SPs claimed to have stage IV lung cancer. Supporting the idea that patient activation can promote equitable doctor-patient interactions, results showed that physicians' implicit racial bias (as measured by an implicit association test) predicted racially biased interpersonal treatment among typical SPs (but not among activated SPs) across SP ratings of interaction quality and ratings from independent coders who read the interaction transcripts. This research supports prior work showing that implicit attitudes can undermine interpersonal treatment in medical settings and provides a strategy for ensuring equitable doctor-patient interactions.

Ethical implications of implicit bias in nursing education

Although the medical profession strives for equal treatment of all patients, disparities in health care are prevalent. Cultural stereotypes may not be consciously endorsed, but their mere existence influences how information about an individual is processed and leads to unintended biases in decision-making, so called "implicit bias". All of society is susceptible to these biases, including physicians. Research suggests that implicit bias may contribute to health care disparities by shaping physician behavior and producing differences in medical treatment along the lines of race, ethnicity, gender or other characteristics. We review the origins of implicit bias, cite research documenting the existence of implicit bias among physicians, and describe studies that demonstrate implicit bias in clinical decision-making. We then present the bias-reducing strategies of consciously taking patients' perspectives and intentionally focusing on individual patients' information apart from their social group. We conclude that the contribution of implicit bias to health care disparities could decrease if all physicians acknowledged their susceptibility to it, and deliberately practiced perspective-taking and individuation when providing patient care. We further conclude that increasing the number of African American/Black physicians could reduce the impact of implicit bias on health care disparities because they exhibit significantly less implicit race bias.

Large language models (LLMs) such as GPT-4 hold great promise as transformative tools in health care, ranging from automating administrative tasks to augmenting clinical decision making. However, these models also pose a danger of perpetuating biases and delivering incorrect medical diagnoses, which can have a direct, harmful impact on medical care. We aimed to assess whether GPT-4 encodes racial and gender biases that impact its use in health care. Using the Azure OpenAI application interface, this model evaluation study tested whether GPT-4 encodes racial and gender biases and examined the impact of such biases on four potential applications of LLMs in the clinical domain-namely, medical education, diagnostic reasoning, clinical plan generation, and subjective patient assessment. We conducted experiments with prompts designed to resemble typical use of GPT-4 within clinical and medical education applications. We used clinical vignettes from NEJM Healer and from published research on implicit bias in health care. GPT-4 estimates of the demographic distribution of medical conditions were compared with true US prevalence estimates. Differential diagnosis and treatment planning were evaluated across demographic groups using standard statistical tests for significance between groups. We found that GPT-4 did not appropriately model the demographic diversity of medical conditions, consistently producing clinical vignettes that stereotype demographic presentations. The differential diagnoses created by GPT-4 for standardised clinical vignettes were more likely to include diagnoses that stereotype certain races, ethnicities, and genders. Assessment and plans created by the model showed significant association between demographic attributes and recommendations for more expensive procedures as well as differences in patient perception. Our findings highlight the urgent need for comprehensive and transparent bias assessments of LLM tools such as GPT-4 for intended use cases before they are integrated into clinical care. We discuss the potential sources of these biases and potential mitigation strategies before clinical implementation. Priscilla Chan and Mark Zuckerberg.

Despite extensive research demonstrating relationships between hope and well being, little work addresses whether hope is malleable. We test a single-session, 90-min intervention to increase college students’ hopeful goal-directed thinking (as defined by Snyder et al. in, Pers Soc Psychol 60:570–585, 1991). To date, this study represents the only test of hope’s malleability in fewer than five sessions and contributes to the small but growing literature regarding positive-psychology interventions. This intervention is especially relevant to college students, given the increasing psychological distress and lack of perceived control noted among this population (Lewinsohn et al. in, J Abnorm Psychol 102:110–120, 1993; Twenge et al. in, Pers Soc Psychol Rev 8:308–319, 2004). Ninety-six participants were assigned to the hope intervention or one of two comparison/control conditions—progressive muscle relaxation or no intervention. Assessment occurred prior to intervention (pre-test), following intervention (post-test), and at one-month follow-up. Participants in the hope intervention showed increases in measures of hope, life purpose, and vocational calling from pre- to post-test relative to control participants. They also reported greater progress on a self-nominated goal at one-month follow-up. Counterintuitively, although hope predicted goal progress, hope did not mediate the relationship between intervention condition and goal progress. Implications of these findings and future directions are discussed.

Sharpening the global focus on ethnicity and race in the time of COVID-19

Introduction. Unconscious, or implicit, bias may influence physician treatment decisions and contribute to healthcare disparities. Research Questions. Is implicit bias associated with inappropriate care for claudication and 1-year major amputation? Methods: All vascular interventionists of the Society for Vascular Surgery Vascular Quality Initiative (VQI) were invited to take the race Implicit Association Test (IAT). The IAT asks participants to classify sequential images of African American (Black) and European American (White) faces to positive and negative attributes and groups them by racial preference based on reaction time. IAT results were weighted and linked to VQI peripheral revascularization data to test the association between implicit bias with patient race and performance of below-knee procedures for claudication, a measure of inappropriate care. We assessed the association of below-knee procedures and patient race with odds of 1-yr amputation, using physician implicit bias as a moderator. Results: Among the 218 physicians that completed the IAT, physicians with White preference (70%) were more likely to perform a below-knee procedure on Black patients ( Fig A ) and their Black patients had increased odds of 1-yr amputation compared to White patients (OR: 3.0, 95%CI: 1.7-5.4). Physicians with no preference (20%) had no variability in below-knee procedures by patient race and fewer 1-yr amputations. A below knee procedure for claudication (ref: above-knee) was associated with increased odds of 1-year amputation, but the effect was greater among physicians with White bias (OR: 8.2;4.7-14.5) compared to no bias (OR: 3.6;1.4-9.7) ( Fig B ). Conclusions: Implicit bias is associated with inappropriate treatment selection, especially for Black patients. These results suggest the need for system level interventions that transparently identify procedures that are not aligned with best practices to reduce the negative effect of implicit bias.

If you're going to have a heart attack, an organ transplant, or a joint replacement, here's the key to getting the very best medical care: be a white, straight, middle-class male. This book by a pioneering black surgeon takes on one of the few critically important topics that haven't figured in the heated debate over health care reform - the largely hidden yet massive injustice of bias in medical treatment. Growing up in Jim Crow - era Tennessee and training and teaching in overwhelmingly white medical institutions, Gus White witnessed firsthand how prejudice works in the world of medicine. And while race relations have changed dramatically, old ways of thinking die hard. In Seeing Patients White draws upon his experience in startlingly different worlds to make sense of the unconscious bias that riddles medical treatment, and to explore what it means for health care in a diverse twenty-first-century America. White and co-author David Chanoff use extensive research and interviews with leading physicians to show how subconscious stereotyping influences doctor-patient interactions, diagnosis, and treatment. Their book brings together insights from the worlds of social psychology, neuroscience, and clinical practice to define the issues clearly and, most importantly, to outline a concrete approach to fixing this fundamental inequity in the delivery of health care.