Classifying Firearm Injury Intent in Electronic Hospital Records Using Natural Language Processing.

Importance: International Classification of Diseases-coded hospital discharge data do not accurately reflect whether firearm injuries were caused by assault, unintentional injury, self-harm, legal intervention, or were of undetermined intent. Applying natural language processing (NLP) and machine learning (ML) techniques to electronic health record (EHR) narrative text could be associated with improved accuracy of firearm injury intent data.

Objective: To assess the accuracy with which an ML model identified firearm injury intent.

Hospital Capacity Command Centers: A Benchmarking Survey on an Emerging Mechanism to Manage Patient Flow.

Background: Delayed hospital and emergency department (ED) patient throughput, which occurs when demand for inpatient care exceeds hospital capacity, is a critical threat to safety, quality, and hospital financial performance. In response, many hospitals are deploying capacity command centers (CCCs), which co-locate key work groups and aggregate real-time data to proactively manage patient flow. Only a narrow body of peer-reviewed articles have characterized CCCs to date.

Assessment of the Accuracy of Firearm Injury Intent Coding at 3 US Hospitals.

Importance: The absence of reliable hospital discharge data regarding the intent of firearm injuries (ie, whether caused by assault, accident, self-harm, legal intervention, or an act of unknown intent) has been characterized as a glaring gap in the US firearms data infrastructure.

Objective: To use incident-level information to assess the accuracy of intent coding in hospital data used for firearm injury surveillance.

Design, Setting, And Participants: This cross-sectional retrospective medical review study was conducted using case-level data from 3 level I US trauma centers (for 2008-2019) for patients presenting to the emergency department with an incident firearm injury of any severity.