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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Beyond Keywords: A Qualitative Study of Context-Dependent Stigmatizing Language in Clinical Documentation from a
Zhihong Zhang1, Stephanie Potts-Thompson2,3, Laura Prescott2,3
1Columbia University Data Science Institute, New York, New York, United States of America.
Background:
Stigmatizing language in clinical documentation is increasingly recognized as a contributor to healthcare disparities, with evidence that such language disproportionately affects marginalized populations.
Objective:
This study aimed to explore home healthcare (HHC) clinicians' perceptions of stigmatizing language in documentation and to identify strategies for reducing bias.
Methods:
We conducted semi-structured interviews with seven nurses between may and October 2024 from a large nonprofit HHC agency in New York City, which serves patients across diverse payer types, such as Medicare, Medicaid, and private insurance. We used maximum variation sampling to ensure diversity in race, experience, and geography. Interviews were transcribed and analyzed using a thematic analysis approach to identify clinicians' views on stigmatizing terms, contextual influences, and strategies for bias reduction.
Results:
Five themes emerged. First, clinicians demonstrated varied interpretations of potentially stigmatizing language, emphasizing the role of context in how terms are perceived. Second, clinical and system-level factors, such as complex patient needs, time pressures, and regulatory demands, shape documentation practices. Third, stigmatizing language can influence provider attitudes, erode patient trust, and hinder patient-clinician care collaboration. Fourth, clinicians identified both individual and organizational strategies to reduce bias, including reflective documentation practices and formal education and training to increase language awareness. Fifth, participants highlighted the potential role of technology in reducing biased documentation.
Conclusions:
This study highlights the complexity of identifying stigmatizing language in HHC and the importance of contextual understanding. Findings support the development of clinician-informed, context-sensitive NLP tools and training programs to promote equitable, patient-centered documentation practices.
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