Large Language Models for Summarizing Advance Care Planning Information From Goals of Care Notes in the EHR

Embedding systematic, structured data extraction within electronic health records (EHR) is vital for improved real-time insights into care delivery. This study evaluates the feasibility of using large language models (LLMs) to extract structured advance care planning (ACP) information from unstructured Goals of Care (GoC) clinical notes in the EHR.

A sample of 100 de-identified GoC notes was manually annotated by clinicians across four ACP categories: Patient Priorities, Code Status, Decision Maker, and Documentation. Two LLMs (Mistral 24.07 and LLaMA 3.1) were prompted to extract structured outputs without domain-specific fine-tuning. Model outputs were compared to human annotations using cosine similarity of BioBERT embeddings.

Mistral 24.07 achieved high semantic similarity in Code Status (0.814), Documentation (0.781), and Patient Priorities (0.770), but lower alignment in Decision Maker (0.609).

LLMs can effectively extract structured ACP information, particularly in well-documented categories, suggesting potential for scalable, data-driven feedback loops that improve the provision of care. However, accuracy challenges remain, and further refinement is needed for nuanced qualitative content categories.