@inproceedings{harrigian-etal-2023-characterization,
    title = "Characterization of Stigmatizing Language in Medical Records",
    author = "Harrigian, Keith  and
      Zirikly, Ayah  and
      Chee, Brant  and
      Ahmad, Alya  and
      Links, Anne  and
      Saha, Somnath  and
      Beach, Mary Catherine  and
      Dredze, Mark",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.28",
    doi = "10.18653/v1/2023.acl-short.28",
    pages = "312--329",
    abstract = "Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.",
}

Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.
Harrigian, Keith Zirikly, Ayah Chee, Brant Ahmad, Alya Links, Anne Saha, Somnath Beach, Mary Catherine Dredze, Mark
Characterization of Stigmatizing Language in Medical Records