AI-Driven Injury Reporting in Pediatric Emergency Departments.

ABSTRACT:

Importance

Injury is a leading cause of morbidity and mortality among children worldwide. Prevention strategies rely on timely and accurate injury surveillance. Many national programs, including the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP), depend on manual review of emergency department (ED) medical records to track injury trends. Rising ED volumes have strained manual processes, delaying data analysis and compromising early detection of public health risks.

Objective

To evaluate whether natural language processing (NLP) transformer models can automate injury case detection in ED medical records, improving CHIRPP workflow efficiency while maintaining high sensitivity.

Design, setting, and participants

Prognostic study of ED visits from Janu

SUBMITTER: Singh D 

PROVIDER: S-EPMC12314733 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature