The DOI matches its Crossref record; 104 of the 104 references it cites were found in a registry or an index.
Eliminating Explicit and Implicit Biases in Health Care: Evidence and Research Needs
Open accessAbstract
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.
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“By simulating scenarios that mirror real-life clinical decision-making processes, and through reading the multi-agent conversations, clinicians can be made aware of potential cognitive biases and how to correct them. This facilitates learning in a controlled, educational environment [25,26].”
From Mitigating cognitive biases in clinical decision-making through multi-agent conversations using large language models: simulation study (2024) · cites this paper as [26]
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Metadata
- Authors
- Vela, M. B., Erondu, A. I., Smith, N. A., Peek, M. E., Woodruff, J. N., & Chin, M. H.
- Affiliations
- University of Illinois Chicago (US)
- University of California, Los Angeles (US)
- Hospital of the University of Pennsylvania (US)
- University of Chicago (US)
- ORCID
- Woodruff, James N.
- Venue
- Annual Review of Public Health
- Publisher
- Annual Reviews
- Published
- 2022-04-05
- Volume
- 43
- Issue
- 1
- Pages
- 477-501
- ISSN
- 0163-7525 (ISSN-L)
- 1545-2093
- PMC
- PMC9172268
- OpenAlex
- W4206528627
- Cited by
- 426
- References
- 102
- Citations by year
- 2026: 89 · 2025: 176 · 2024: 97 · 2023: 55 · 2022: 9
- FWCI
- 14.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Annual Review of Public Health (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK092949)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Cultural Competency in Health Care
- Social Sciences › Sociology and Political Science › Racial and Ethnic Identity Research
- Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
- Keywords
- implicit bias, explicit bias, healthcare workers, racial and ethnic minorities, health disparities, bias interventions, patient-clinician communication, clinical decision-making, organizational discrimination, structural change, conceptual model
- MeSH terms
- Attitude of Health Personnel, Communication, Health Personnel, Humans, Bias, Healthcare Disparities
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