A SNAPpy use of large language models: using large language models to classify treatment plans in pediatric acute otitis media.

Abstract

Background and significance: Acute otitis media (AOM) is a leading cause of pediatric antibiotic overuse. Safety Net Antibiotic Prescriptions (SNAPs) are recommended for antibiotic stewardship but are difficult to identify due to lack of structured documentation.

Objective: This study validates the accuracy of Versa, a GPT-4o based HIPAA-compliant large language model (LLM), to classify AOM treatment plans from physician notes.

Methods: A retrospective cross-sectional study analyzed pediatric AOM encounters. Multiple prompting strategies were used to classify treatment plans and validated against a representative sample of manual reviews by 2 pediatricians. A locally fine-tuned model, Clinical-Longformer was also trained and tested against Versa and human review.

Results: In total, 5707 encounters were included; 374 reviewed manually. Zero-shot accuracy was 97.8%; few-shot accuracy was 85%. Clinical-Longformer achieved 93.3% accuracy.

Conclusion: Versa effectively identifies AOM treatment plans, providing a cost-efficient quality improvement tracking tool for prescription practice patterns in pediatric antibiotic stewardship efforts.