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Racial Differences in Stigmatizing and Positive Language in Emergency Medicine Notes
Sean Boley1, Abbey Sidebottom2, Marc Vacquier2
1Emergency Care Consultants, Minneapolis, MN, USA. Sean.boley@eccemergency.com.
Emergency medicine notes reveal racial inequities in language, with certain racial groups more likely to be described with stigmatizing terms. Natural language processing identified these biases, highlighting the need for interventions to address implicit bias in medical documentation.
Area of Science:
- Medical Documentation Analysis
- Natural Language Processing in Healthcare
- Health Equity Research
Background:
- Provider language in medical notes can reflect implicit biases.
- Race-related disparities in healthcare are a significant concern.
- Understanding linguistic patterns in emergency medicine notes is crucial for identifying bias.
Purpose of the Study:
- To investigate if the prevalence of stigmatizing language in emergency medicine (EM) encounter notes differs across patient race/ethnicity.
- To apply natural language processing (NLP) techniques to identify linguistic themes in EM notes.
- To quantify associations between patient race/ethnicity and the use of stigmatizing or positive language.
Main Methods:
- Retrospective cohort study of 26,363 EM encounters.
- Utilized NLP to identify 5 stigmatizing themes (difficult, non-compliant, skepticism, substance abuse/seeking, financial difficulty) and 2 positive themes (compliment, compliant).
- Logistic regression models analyzed the association between patient race/ethnicity and the presence of identified themes in notes.
Main Results:
- Non-Hispanic (NH) Black patients' notes were less likely to contain 'difficult,' 'skepticism,' and 'substance abuse/seeking' but more likely to contain 'non-compliant' and 'financial difficulty' compared to NH White patients.
- Hispanic patients' notes were less likely to contain 'difficult' and 'substance abuse/seeking' themes.
- Non-Hispanic Native American/Alaska Native (NH NA/AI) patients' notes had double the odds of containing a stigmatizing theme compared to NH White patients.
Conclusions:
- NLP analysis of EM notes revealed significant inequities in the use of stigmatizing and positive language across racial groups.
- Identified disparities suggest the presence of race-related implicit bias in medical documentation.
- Interventions aimed at minimizing race-related implicit bias in clinical language are warranted.
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