Black–white implicit bias among healthcare workers in UK emergency departments: findings from the multicentred BiasED Study

Background Addressing racial bias in healthcare is crucial for ensuring equitable treatment and outcomes. We aimed to explore implicit (unconscious) racial bias among healthcare professionals in UK emergency departments (EDs). Methods BiasED was an online survey disseminated to healthcare professionals working at 16 UK EDs. Participants completed the race (black/white) Implicit Association Test (IAT), which explores potential implicit bias by examining response times for positive and negative associations for white and black people. Differences were measured using the D score (range −2 to 2, where 0 indicates neutral bias and positive scores indicate pro-white bias). Explicit attitudes and stereotypes were also explored by participants' self-report. Results 969 healthcare workers participated, including doctors (42%), nurses (43%) and other healthcare professionals (median age 36 years; 66% female; 67% white). Self-report measures indicated little explicit bias, with 87% reporting equal preference for black and white people. Black participants expressed more positive attitudes about black people than white or Asian participants. The IAT indicated a pro-white bias ( D =0.32, SD=0.42, p<0.001), considered a small to medium effect. Men ( D =0.36) showed more implicit bias than women (D=0.29, p=0.03). White (D=0.34) and Asian (D=0.37) participants showed more bias than black (D=0.09) participants. No significant differences were found between medical professions. Except for black participants, all groups showed a pro-white bias on the IAT. Conclusion The study identified evidence of implicit racial bias among healthcare workers in UK EDs. These findings highlight the complex nature of racial attitudes in healthcare and emphasise the need for comprehensive bias awareness and mitigation strategies in emergency care settings. Future research should explore the potential impact of these biases on clinical decision-making and patient outcomes.