Automating Pathway Extraction from Clinical Guidelines: A Conceptual Model, Datasets and Initial Experiments

Clinical pathways are structured, multidisciplinary care plans utilized by healthcare providers to standardize the management of specific clinical problems. Designed to bridge the gap between evidence and practice, clinical pathways aim to enhance clinical outcomes and improve efficiency, often reducing hospital stays and lowering healthcare costs. However, maintaining pathways with up-to-date, evidence-based recommendations is complex and time-consuming. It requires the integration of clinical guidelines, algorithmic procedures, and tacit knowledge from various institutions. A critical aspect of updating clinical pathways involves extracting procedural information from clinical guidelines, which are textual documents that detail medical procedures. This paper explores how Large Language Models (LLMs) can facilitate this extraction to support clinical pathway development and maintenance. Concretely, we present a conceptual model for using LLMs in this extraction task, provide a dataset comprising thousands of clinical guidelines for academic research, and share the results of initial experiments demonstrating the efficacy of LLMs in extracting relevant pathway information from these guidelines.