Unlocking the Potential of Free Text in Electronic Health Records with Large Language Models (LLM): Enhancing Patient Safety and Consultation Interactions.

Computer-mediated clinical consultation, involving clinicians, electronic health record (EHR) systems, and patients, yield rich narrative data. Despite advancements in Natural Language Processing (NLP), these narratives remain underutilised. Free text recording in EHRs allows expressivity, complements structured data from clinical coding systems, and facilitates collaborative care. Large language models (LLMs) excel in understanding and generating natural language, enabling complex dialogue processing. Integrating LLM tools into consultations could harness the untapped potential of free text to identify patient safety concerns, support diagnosis and provide content to enhance clinical-patient interactions. Tailoring LLMs for specific consultation tasks through pre-training and fine-tuning is viable. This paper outlines approaches for adopting LLMs in primary care and suggests that using fine-tuned LLMs with prompt engineering could enhance computer-mediated clinical consultation cost-effectively.