HealthTrajectory: Patient Journey Summaries and Visualizations for Patient-Clinician Communication Support

Abstract

In recent years, patient narratives have been used to understand subjective experiences that are not recorded in clinical notes. However, narratives tend to be long and unstructured, requiring summarization. However, text-based summaries often require a lot of clarification from patients and make it difficult for clinicians to review events and changes in symptoms over time. In this study, we expanded the summary output by presenting a visualization of the patient’s journey to facilitate communication between patients and medical staff. Referring to the widespread use of LLM for summarization, we compared GPT-4.1 and Gemini-2.5-pro, and used Gemini-3-pro-image-preview for visualization. Data was collected from DIPEx-Japan, then the quality of the summaries was evaluated quantitatively and the visualizations qualitatively. Quantitative evaluation using BLEU and ROUGE metrics showed that Gemini-2.5-pro achieved higher summary scores than GPT-4.1, and Japanese summaries scored higher than English ones. Conversely, English performed better than Japanese in temporal expression extraction using precision, recall, and F1 metrics, and the Gemini-2.5-pro model consistently outperformed GPT-4.1. In qualitative evaluation using the pairwise method, the timetable-based model was far superior with an overall win rate of 0.865 in Japanese and 0.969 in English compared to the baseline.

Resources

Details