MedSlice: fine-tuned large language models for secure clinical note sectioning.

Objectives: Extracting sections from clinical notes is crucial for downstream analysis but is challenging due to variability in formatting and labor-intensive nature of manual sectioning. This study develops a pipeline for automated note sectioning using open-source large language models (LLMs), focusing on three sections: History of Present Illness, Interval History, and Assessment and Plan. Materials and Methods: We fine-tuned three open-source LLMs to extract sections using a curated dataset of 487 progress notes, comparing results relative to proprietary models (GPT-4o, GPT-4o mini). Internal and external validity were assessed via precision, recall, and F1 score. Results: Fine-tuned Llama 3.1 8B (F1 = 0.92) outperformed GPT-4o. On the external validity test set, performance remained high (F1 = 0.85). Discussion: While proprietary LLMs have shown promise, privacy concerns limit their utility in medicine; fine-tuned, open-source LLMs offer advantages in cost, performance, and accessibility. Conclusion: Fine-tuned, open-source LLMs can surpass proprietary models in clinical note sectioning.