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Identifying and Reducing Stigmatizing Language in Home Health Care With a Natural Language Processing-Based System

Zhihong Zhang1,2, Pallavi Gupta2, Stephanie Potts-Thompson2,3

  • 1Columbia University Data Science Institute, New York, NY, United States.

JMIR Research Protocols
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

This study uses natural language processing (NLP) to identify and reduce stigmatizing language in home health care (HHC) clinical notes. The ENGAGE system aims to improve patient care by detecting and replacing harmful terminology.

Keywords:
AIartificial intelligencehome health carenatural language processingracial biasstigmatizing language

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Clinical Communication

Background:

  • Stigmatizing language in clinical notes negatively impacts patient care quality.
  • Natural Language Processing (NLP) offers a solution for identifying such language in electronic health records.

Purpose of the Study:

  • To develop and evaluate an NLP-driven system (ENGAGE) for automatically identifying and replacing stigmatizing language in clinical notes.
  • To refine the understanding of stigmatizing language within home health care (HHC) settings.

Main Methods:

  • A mixed-methods approach using electronic health record data from HHC organizations (2019-2021).
  • Aim 1: Refine the ontology of stigmatizing language through interviews and note analysis.
  • Aim 2: Determine optimal NLP approaches for accuracy.
  • Aim 3: Analyze prevalence by patient race/ethnicity.
  • Aim 4: Develop the ENGAGE system with NLP methods and a user interface.

Main Results:

  • Funding secured from the National Institute on Minority Health and Health Disparities.
  • Recruitment initiated in May 2024; interviews completed for 9 participants as of March 2025.
  • Anticipated completion of all study aims by April 2027.

Conclusions:

  • The study will examine stigmatizing language in HHC settings using extensive data.
  • Aims to develop systems for effectively reducing stigmatizing language among HHC nurses.
  • Contributes to improving the quality of patient care through technological intervention.