Evidence map›Paper›PMID 40998318›Full record

ArticleJMIR research protocols2025

Identifying and Reducing Stigmatizing Language in Home Health Care With a Natural Language Processing-Based System (ENGAGE): Protocol for a Mixed Methods Study.

Zhihong Zhang, Pallavi Gupta, Stephanie Potts-Thompson, Laura Prescott, Morgan Morrison, Scott Sittig, Margaret V McDonald, Chase Raymond, Jacquelyn Y Taylor, Maxim Topaz

Abstract read
In one paragraph

Article in JMIR research protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Zhihong ZhangColumbia University Data Science Institute, New York, NY, United States.ORCID 0000-0002-9948-8936
Pallavi GuptaColumbia University School of Nursing, New York, NY, United States.ORCID 0000-0003-0146-239X
Stephanie Potts-ThompsonColumbia University School of Nursing, New York, NY, United States.ORCID 0000-0003-3484-0852
Laura PrescottColumbia University School of Nursing, New York, NY, United States.ORCID 0009-0001-6669-3482
Morgan MorrisonColumbia University School of Nursing, New York, NY, United States.ORCID 0009-0008-5532-5618
Scott SittigUniversity of Louisiana at Lafayette Health Sciences, Lafayette, LA, United States.ORCID 0000-0002-7179-152X
Margaret V McDonaldCenter for Home Care Policy & Research, VNS Health, New York, NY, United States.ORCID 0000-0003-1045-0956
Chase RaymondDepartment of Linguistics, University of Colorado, Boulder, CO, United States.ORCID 0000-0003-4353-7345
Jacquelyn Y TaylorColumbia University School of Nursing, Center for Research on People of Color, New York, NY, United States.ORCID 0000-0002-0858-7358
Maxim TopazColumbia University Data Science Institute, New York, NY, United States.ORCID 0000-0002-2358-9837

Funding

Optimizing Clinical Documentation Quality to Reduce Health Disparities in Home Healthcare: The ENGAGE StudyR01MD018028 · NIMHD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SITTIG, SCOTT M, TAYLOR, JACQUELYN Y · 2023 to 2025
$2.0M
NIMHD NIH HHS R01 MD018028
6 · The paper itself

Abstract

backgroundStigmatizing language is common in clinical notes and can adversely affect the quality of patient care. Natural language processing (NLP) is a promising technology for identifying such language across large volumes of clinical notes in electronic health records.

objectiveThis study proposes an NLP-driven reduce stigmatizing language (ENGAGE) system to automatically identify and replace stigmatizing language.

methodsUsing a mixed methods study, we will extract electronic health record data for patients admitted to 2 large, diverse home health care (HHC) organizations between January 2019 and December 2021. We propose the following 4 aims: aim 1 is to refine the ontology of stigmatizing language in HHC by (1) interviewing a diverse sample of HHC nurses and patients to identify terms to avoid and (2) analyzing clinical notes from various regions in the United States to categorize stigmatizing language. Aim 2 is to determine the best NLP approach for accurately identifying stigmatizing language by training algorithms and comparing their performance to human annotations. Aim 3 is to analyze the prevalence of stigmatizing language based on patients' race and ethnicity using adjusted statistical analyses of a sample of approximately half a million HHC patients (34% racial and ethnic minority groups). Aim 4 is to develop the NLP-driven ENGAGE system by (1) testing NLP methods (rule based; "delete, retrieve, and generate"; and transformers) for suggesting alternative wording and (2) designing and refining the user interface for clinical trial preparation.

resultsWe received funding from the National Institute on Minority Health and Health Disparities in September 2023. Recruitment began in May 2024, and as of March 2025, interviews have been completed for 9 enrolled participants. We anticipate completing all study aims by April 2027.

conclusionsThis study will leverage extensive data sources to examine stigmatizing language in HHC settings and contribute to the development of systems aimed at effectively reducing the use of such language among HHC nurses. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69753.

Indexed as

Home Care ServicesLanguageNatural Language ProcessingStereotypingElectronic Health RecordsHumansResearch DesignUnited StatesAIartificial intelligencehome health carenatural language processingracial biasstigmatizing language

Identifiers

PMID40998318
PMCPMC12511817

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.