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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.08969 |
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| _version_ | 1866916840198897664 |
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| author | Walker, Drew Love, Jennifer Rajwal, Swati Walker, Isabel C Cooper, Hannah LF Sarker, Abeed Livingston III, Melvin |
| author_facet | Walker, Drew Love, Jennifer Rajwal, Swati Walker, Isabel C Cooper, Hannah LF Sarker, Abeed Livingston III, Melvin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08969 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Application of CARE-SD text classifier tools to assess distribution of stigmatizing and doubt-marking language features in EHR Walker, Drew Love, Jennifer Rajwal, Swati Walker, Isabel C Cooper, Hannah LF Sarker, Abeed Livingston III, Melvin Computation and Language 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. |
| title | Application of CARE-SD text classifier tools to assess distribution of stigmatizing and doubt-marking language features in EHR |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.08969 |