Abstract
The shift toward patient-centric healthcare requires understanding comprehensive patient journeys. Current healthcare data systems often fail to provide holistic representations, hindering coordinated care. Patient Journey Knowledge Graphs (PJKGs) solve this by integrating diverse patient information into unified, structured formats. This paper presents a methodology for constructing PJKGs using Large Language Models (LLMs) to process both clinical documentation and patient-provider conversations. These graphs capture temporal and causal relationships between clinical events, enabling advanced reasoning and personalized insights. Our evaluation of four LLMs (Claude 3.5, Mistral, Llama 3.1, ChatGPT4o) shows all achieved perfect structural compliance but varied in medical entity processing, computational efficiency, and semantic accuracy. This work advances patient-centric healthcare through actionable knowledge graphs (KGs) that enhance care coordination and outcome prediction.