AI-Driven Injury Reporting in Pediatric Emergency Departments

Area of Science:

• Public Health
• Medical Informatics
• Artificial Intelligence

Background:

• Injury surveillance is crucial for child morbidity and mortality prevention.
• Manual review of emergency department (ED) records for injury tracking is time-consuming and strained by high patient volumes.
• Timely data analysis is compromised, delaying the detection of public health risks.

Purpose of the Study:

• To evaluate natural language processing (NLP) transformer models for automating injury case detection in ED medical records.
• To assess the potential for improving the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) workflow efficiency.
• To maintain high sensitivity in identifying injury-related cases.

Main Methods:

• A prognostic study analyzed 217,173 pediatric ED visits from 2017-2023 at a tertiary pediatric center.
• Two transformer-based NLP models (DistilBERT and BERT-large) were fine-tuned using supervised learning.
• Models classified medical records for CHIRPP reporting, with outcomes including true positive rate, area under the curve, and reduction in manual review.

Main Results:

• Both NLP models demonstrated high accuracy, with AUROC of 0.983 and AUPRC around 0.93.
• Models achieved a true positive rate of 0.90, identifying 90% of injury cases.
• Manual review burden was reduced from 100% to 17% of ED visits, significantly improving efficiency.

Conclusions:

• NLP transformer models effectively automate injury case detection in ED medical records.
• This automation has the potential to enable real-time injury surveillance monitoring.
• The findings suggest a significant improvement in public health risk detection and response capabilities.