Prompts to Table: Specification and Iterative Refinement for Clinical Information Extraction with Large Language Models.

Abstract

Background: Extracting structured data from free-text medical records is laborious and error-prone. Traditional rule-based and early neural network methods often struggle with domain complexity and require extensive tuning. Large language models (LLMs) offer a promising solution but must be tailored to nuanced clinical knowledge and complex, multipart entities.

Methods: We developed a flexible, end-to-end LLM pipeline to extract diagnoses, per-specimen anatomical-sites, procedures, histology, and detailed immunohistochemistry results from pathology reports. A human-in-the-loop process to create validated reference annotations for a development set of 152 kidney tumor reports guided iterative pipeline refinement. To drive nuanced assessment of performance we developed a comprehensive error ontology-categorizing by clinical significance (major vs. minor), source (LLM, manual annotation, or insufficient instructions), and contextual origin. The finalized pipeline was applied to 3,520 internal reports (of which 2,297 had pre-existing templated data available for cross referencing) and evaluated for adaptability using 53 publicly available breast cancer pathology reports.

Results: After six iterations, major LLM errors on the development set decreased to 0.99% (14/1413 entities). We identified 11 key contexts from which complications arose- including medical history integration, entity linking, and specification granularity- which provided valuable insight in understanding our research goals. Using the available templated data as a cross reference, we achieved a macro-averaged F1 score of 0.99 for identifying six kidney tumor subtypes and 0.97 for detecting metastasis. When adapted to the breast dataset, three iterations were required to align with domain-specific instructions, attaining 89% agreement with curated data.

Conclusion: This work illustrates that LLM-based extraction pipelines can achieve near expert-level accuracy with carefully constructed instructions and specific aims. Beyond raw performance metrics, the iterative process itself-balancing specificity and clinical relevance-proved essential. This approach offers a transferable blueprint for applying emerging LLM capabilities to other complex clinical information extraction tasks.