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Identifying Firearm Violence Exposure in Primary Care Clinical Notes: Protocol for Developing a National Language

Natalie Carwright1, Frances M Biel2, Megan Hoopes2

  • 1Department of Mathematics, Norwich University, Northfield, VT, United States.

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Summary
This summary is machine-generated.

This study developed a novel natural language processing (NLP) text classifier to identify firearm violence exposure in electronic health records. This tool aims to improve patient care by better understanding the health impacts of gun violence.

Keywords:
firearm injuryfirearm violencenatural language processing, electronic health recordstext classifier

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Area of Science:

  • Public Health
  • Health Informatics
  • Computational Linguistics

Background:

  • Structured electronic health record (EHR) data inadequately capture the full scope of firearm violence exposure, including secondary experiences.
  • Secondary exposure, such as witnessing violence or losing a loved one, has significant short- and long-term health consequences.
  • Clinical notes within EHRs contain rich, unstructured data that can be leveraged to identify these exposures.

Purpose of the Study:

  • To develop a natural language processing (NLP) text classifier to identify both primary and secondary firearm violence exposure.
  • To analyze exposure data from ambulatory primary care and behavioral health clinical notes for individuals aged five years and older.
  • To enhance the ascertainment of firearm violence exposure in clinical settings.

Main Methods:

  • Utilizing unstructured clinical notes from OCHIN, a multistate EHR network, between 2012 and 2022.
  • Developing a labeled dataset through lexicon identification and manual text review for supervised NLP.
  • Building, training, and evaluating machine learning, neural network, and large language models for text classification.
  • Engaging a stakeholder advisory committee to ensure methodological rigor and address potential biases.

Main Results:

  • The study is currently in the evaluation phase of NLP text classifiers, with a final model selection anticipated by August 2025.
  • Results of the NLP model development and performance are expected to be published in 2026.
  • Ongoing development and evaluation of NLP models for identifying firearm violence exposure.

Conclusions:

  • This research introduces a novel NLP text classifier for identifying firearm violence exposure within clinical notes.
  • The developed NLP model has the potential to increase the identification of exposed patients.
  • This work lays the foundation for understanding the long-term health impacts of firearm violence and improving patient care strategies.