Application of CARE-SD text classifier tools to assess distribution of stigmatizing and doubt-marking language features in EHR

Drew Walker, Jennifer Love, Swati Rajwal, Isabel C Walker, Hannah LF Cooper +2 more
7/11/2025
cs.CL

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Abstract

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.

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Cite this paper

@article{walker2025application,
  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},
  year   = {2025},
  eprint = {2507.08969},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.08969v1}
}

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