ArticleJournal of racial and ethnic health disparities2026
Development of a Tool for Patient-Reported Incidents of Race and Language Bias in an Academic Medical Center.
Article in Journal of racial and ethnic health disparities, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPatients from racial and ethnic minoritized groups and with non-English language preference (NELP) face higher risks of bias due to structural racism and systemic inequities. Traditional hospital bias incident reporting systems often lack input from these groups.
objectiveTo develop a hospitalized patient incident reporting tool (IRT) to capture events for potential bias, incorporating feedback from racial and ethnic minoritized and NELP patients.
designThe IRT was developed using an iterative, participatory approach involving a hospital advisory committee and hospitalized participants in the United States. It was available in six languages and had 26 questions, including Likert-scale and open-ended questions spanning participant experience to reporting preferences.
participantsThe IRT was administered to 50 participants admitted to adult internal medicine services at a single academic center. Patient's average age was 50 years, with 50% male, 50% Black, and 40% Latinx. Thirty-eight percent of participants reported NELP. MEASURES: A qualitative and quantitative study design was implemented to (1) identify questions with highest response rates and detail, (2) establish questions with highest response among participants who reported bias incidents, and (3) understand participants' reporting preferences.
resultsThe greatest responses came from Likert-scale and open-ended questions about treatment and communication. Eleven participants reported an experience of bias. Participants preferred phone call and paper surveys for reporting.
conclusionsWe developed an IRT that captures racial, ethnic, and language-based bias experienced during a hospitalization that reflects the needs of underrepresented populations in hospital safety data. Validation of the tool is needed across different hospital settings.
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