Evaluation of race, ethnicity, and language mischaracterizations in the electronic medical record for Hispanic/Latinx patients
Year: 2025
Type: article
Study design: Observational Study
Abstract: Objective Accurate documentation of interpreter requirements and language preference within the electronic medical record (EMR) is essential for ensuring appropriate care and effective communication with patients who have non-English language preference (NELP). Our objective was to examine the accuracy of EMR documentation related to interpreter needs and language preferences for Hispanic patients at a large academic medical center. Methods This was a cross-sectional study conducted at the University of North Carolina Medical Center from May 2023 to July 2023. A total of 156 Hispanic patients were surveyed based on their clinical records and self-reported language needs. The main measure was the accuracy of EMR documentation compared to patients’ self-reported interpreter needs and preferred language. Results Among 156 Hispanic inpatients, 30.13% had mischaracterized interpreter needs in the EMR, with discrepancies including missing documentation (22%) and direct contradictions (9.92%). Mischaracterizations were significantly associated with male gender (OR = 0.310; 95% CI [0.140, 0.685]; p = 0.0038) and self-reported English proficiency (OR = 0.124; 95% CI [0.041, 0.374]; p < 0.001). Preferred language misclassification occurred in 9.68% of cases, and 7% had incorrect ethnicity documentation. Conclusion Significant inaccuracies in race, ethnicity, and language data documentation hinder equitable care. Addressing these gaps requires standardized documentation protocols, staff training, and objective language assessment tools to improve interpreter utilization and mitigate disparities for NELP patients.
Source: Digital Health
Language: English
Subfield: General Health Professions
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,000
OpenAlex ID: W4416168909