ArticleJournal of the American Medical Informatics Association : JAMIA2025
A SNAPpy use of large language models: using large language models to classify treatment plans in pediatric acute otitis media.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Linking Dispense Data to Electronic Health Orders: Tutorial for Querying Commercial Pharmacy Databases to Support Systemwide Quality Improvement.Journal of medical Internet research · 2026Article
- RxReduce: a medication deprescribing agent framework leveraging large language models to optimize proton pump inhibitor use.JAMIA open · 2026Article
- Characterising 'Watch and Wait' prescribing patterns in paediatric otitis media using large language models and pharmacy dispense data.BMJ health & care informatics · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and significanceAcute 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.
objectiveThis study validates the accuracy of Versa, a GPT-4o based HIPAA-compliant large language model (LLM), to classify AOM treatment plans from physician notes.
methodsA 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.
resultsIn 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.
conclusionVersa effectively identifies AOM treatment plans, providing a cost-efficient quality improvement tracking tool for prescription practice patterns in pediatric antibiotic stewardship efforts.
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Registered trials
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