Enhancing pediatric emergency care with LLMs: a retrospective study in a non-English setting

Abstract Background Efficient clinical documentation in emergency departments (EDs) is vital for timely, equitable care, especially for children with complex medical needs. Yet, documentation remains a resource-intensive bottleneck. While Large Language Models (LLMs) offer potential to automate this task, their utility in non-English healthcare systems is largely unexplored. This study evaluates the effectiveness of an LLM in structuring free-text data from pediatric ED records in Italy, highlighting implications for health system responsiveness and quality of care. Methods We conducted a retrospective study at the pediatric ED of Padova University Hospital, analyzing anonymized free-text admission records (2007-2023) from children with medical complexities. A manually labeled subset (n = 697) served as a gold standard, compared against LLM-extracted data, operating via GDPR-compliant, prompt-based interaction. We assessed extraction accuracy for key variables (e.g., triage codes, outcomes, referrals) and measured time efficiency gains. Results LLM reduced data extraction time from 5 minutes to 6 seconds per record. Accuracy was high for triage color codes (99.3%, 95% CI: 98.3-99.8%) and ED outcomes (98.6%, 95% CI: 97.4-99.3%). Performance was robust for procedural classifications but lower for medications (76.8%) and specialist consultations (72.2%), reflecting ambiguity in narrative notes. The model successfully adapted to non-English clinical texts, supporting its generalizability in multilingual health systems. Conclusions LLMs can streamline ED documentation while maintaining high data fidelity, easing clinician workload, and enabling faster analytics for public health surveillance. This approach shows promise in enhancing pediatric emergency care quality and can scale to support digital health transformation across diverse European settings. Challenges remain in interpreting ambiguous inputs, underscoring the need for further model refinement and real-world validation. Key messages • LLMs substantially accelerate clinical data extraction in pediatric emergency care with high accuracy. • This AI-based solution supports scalable, multilingual digital health infrastructure for timely public health action.