Development of a large language model clinical variable extraction pipeline for the study of lymphedema

For many disease processes, clinical information is often captured in unstructured clinical notes that require manual data extraction for research. Large language models (LLMs) have the potential to automate the data extraction process, enabling study for diseases with fewer tabular metrics available. Here, we report the development and validation of an LLM-based clinical note data extraction approach for a retrospective cohort study of lymphedema etiologies and treatments. After using International Classification of Diseases, 10th Revision codes to identify potential lymphedema patients and text-matching to find potentially relevant clinical notes, an LLM was used to further filter notes, summarize for relevant content, and extract per-patient variables concerning etiology, treatments and efficacies, clinical stage, and wound status. Prompts were optimized for accuracy with an iterative review strategy, starting with zero-shot approaches and modified to few-shot approaches where necessary, validated by subset human review. A Protected Health Information-compliant Application Programming Interface version of OpenAI's GPT-5 was used for all LLM queries. Human review of LLM-extracted final variables revealed high accuracy when compared against both an intermediate summarization step (99% accurate) and against original clinical notes (98% accurate) across 2858 patients/32,288 notes. LLMs are a valuable tool for conducting clinical research at scale using unstructured text data.