Decreasing Missingness in Race and Ethnicity Data by Inclusion of Preferred Language for Mapping to Aggregate Categories

Background Accurate and complete patient race and ethnicity data are essential for informing healthcare quality and patient safety initiatives. However, missing data remains a persistent issue. We aimed to explore the utility of preferred language to impute patient race and ethnicity. Methods This was a retrospective analysis from three emergency departments in NYC, from 6/1/2023-5/31/2024. We leveraged a mapping schema for imputation of missing race and ethnicity data using preferred language for categorization into the Office of Management and Budget (OMB)’s seven categories. We examined concordance between preferred language and predicted categories. Results The proportion of patients with missing race and ethnicity data decreased from 9.7% to 8.6%, reducing missingness by 11.1%. The greatest proportion of change with use of preferred language was for Middle Eastern and North African patients (14.7%). Conclusions Our findings support that language-based imputation has the potential to reduce missing race and ethnicity data and may be a helpful tool in quality improvement and research efforts. For health systems where race and ethnicity fields may not be fully detailed or have a high rate of missing data, the use of language may serve as a valuable adjunct in improving the comprehensive picture of a population.