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

BACKGROUND

Structured data codes capture acute bodily injury from firearm violence but do not necessarily describe follow-up care from bodily injury and secondary exposure to firearm violence (eg, witnessing a shooting, being threatened by a firearm, or losing a loved one to gun violence and injury from firearms) even though such exposure is associated with many short- and long-term health impacts. Clinical notes from electronic health records (EHRs) often contain data not otherwise captured in structured data fields and can be categorized using natural language processing (NLP).

OBJECTIVE

This study protocol outlines the steps being taken to develop an NLP text classifier for determination of exposure to firearm violence (both primary and secondary exposure) from ambulatory primary care and behavioral health EHR clinical notes for persons aged ≥5 years.

METHODS

The study will use unstructured data from clinical notes taken between 2012 and 2022 from OCHIN, a multistate network of community health organizations using a single instance of Epic EHR. We describe the process of developing a labeled dataset for supervised NLP development that includes establishing a lexicon (words related to firearm violence) to identify potentially relevant notes, followed by a review of text extracted from a sample of these notes. We then describe the process of building, training, and evaluating candidate machine learning, neural network, and large language model NLP text classifiers. From this, a final NLP model is chosen then evaluated on a new set of randomly selected notes. An engaged stakeholder advisory committee will provide input and guidance on methods and results to identify and address potential biases in the NLP text classifiers.

RESULTS

The study was funded in September 2023. Study activities have been ongoing through July 2025 and we are currently evaluating NLP text classifiers. We expect that the final model will be selected by August 2025 and we will publish results of NLP model development and the final model performance in 2026.

CONCLUSIONS

This work describes the development of a novel NLP text classifier to identify exposure to firearm violence in ambulatory primary care and behavioral health clinical notes. The NLP model developed in this study may lead to increased ascertainment of patients with exposure, laying the groundwork for understanding the long-term impacts and outcomes of firearm violence exposure and presenting opportunities for improved patient care.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/76681.