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

TL;DR

This study uses text classifier tools to analyze EHRs, revealing that stigmatizing and doubt-marking language features are more prevalent among certain patient groups and provider types, highlighting ongoing biases in healthcare documentation.

Contribution

The paper introduces a method combining lexicon matching and supervised learning classifiers to quantify stigmatizing language in EHRs, revealing disparities across patient demographics and provider roles.

Findings

Higher stigmatizing labels for Black patients and those with government insurance.

Increased doubt markers among male patients.

More stigmatizing language used by nurses and social workers.

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.