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Zhang Z, Gupta P, Potts-Thompson S, Prescott L, Morrison M, Sittig S, McDonald MV, Raymond C, Taylor JY, Topaz M
Identifying and Reducing Stigmatizing Language in Home Health Care With a Natural Language Processing–Based System (ENGAGE): Protocol for a Mixed Methods Study
Identifying and reducing stigmatizing language in home healthcare: Protocol for the ENGAGE 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
Background:
Stigmatizing language is prevalent in clinical notes, adversely affecting patient care quality. Natural language processing (NLP) is a promising technology for analyzing millions of clinical notes in electronic health records.
Objective:
This study proposes an NLP-driven ENGAGE system to automatically identify and replace stigmatizing language.
Methods:
This mixed-method study will extract electronic health record (EHR) data for patients admitted to two large, diverse home healthcare (HHC) organizations between January 2019, and December 2021. The study will be conducted in four aims: Aim 1 will refine the ontology of stigmatizing language in HHC by (a) interviewing a diverse sample of HHC nurses and patients to identify terms to avoid, and (b) analyzing clinical notes from various U.S. regions to categorize stigmatizing language. Aim 2 will determine the best NLP approach for accurately identifying stigmatizing language by training algorithms and comparing their performance to human annotations. Aim 3 will 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 minorities). Aim 4 will develop the NLP-driven ENGAGE system by (a) testing NLP methods (rule-based, “Delete, Retrieve, Generate,” and transformers) for suggesting alternative wording, and (b) designing and refining the user interface for clinical trial preparation.
Results:
This study was funded by the National Institute on Minority Health and Health Disparities. Recruitment, enrollment, and data curation are ongoing.
Conclusions:
This study will leverage extensive data sources to examine stigmatizing language in HHC settings, contributing to developing systems to effectively reduce such language among HHC nurses. Clinical Trial: NA
Citation
Please cite as:
Zhang Z, Gupta P, Potts-Thompson S, Prescott L, Morrison M, Sittig S, McDonald MV, Raymond C, Taylor JY, Topaz M
Identifying and Reducing Stigmatizing Language in Home Health Care With a Natural Language Processing–Based System (ENGAGE): Protocol for a Mixed Methods Study