Introduction: Electronic health records (EHR) are a critical medium through which patient stigmatization is perpetuated among healthcare teams. Methods: We identified linguistic features of doubt markers and stigmatizing labels in MIMIC-III EHR via expanded lexicon matching and supervised learning classifiers. Predictors of rates of linguistic features were assessed using Poisson regression models. Results: We found higher rates of stigmatizing labels per chart among patients who were Black or African American (RR: 1.16), patients with Medicare/Medicaid or government-run insurance (RR: 2.46), self-pay (RR: 2.12), and patients with a variety of stigmatizing disease and mental health conditions. Patterns among doubt markers were similar, though male patients had higher rates of doubt markers (RR: 1.25). We found increased stigmatizing labels used by nurses (RR: 1.40), and social workers (RR: 2.25), with similar patterns of doubt markers. Discussion: Stigmatizing language occurred at higher rates among historically stigmatized patients, perpetuated by multiple provider types.
@article{arxiv.2507.08969,
title = {Application of CARE-SD text classifier tools to assess distribution of stigmatizing and doubt-marking language features in EHR},
author = {Drew Walker and Jennifer Love and Swati Rajwal and Isabel C Walker and Hannah LF Cooper and Abeed Sarker and Melvin Livingston},
journal= {arXiv preprint arXiv:2507.08969},
year = {2025}
}