@inproceedings{hidayah-etal-2026-healthtrajectory,
title = "{H}ealth{T}rajectory: Patient Journey Summaries and Visualizations for Patient-Clinician Communication Support",
author = "Hidayah, Rohmah and
Nishiyama, Tomohiro and
Wakamiya, Shoko and
Aramaki, Eiji",
editor = "Gupta, Deepak and
Thompson, Paul and
Ananiadou, Sophia and
Demner-Fushman, Dina",
booktitle = "Proceedings of the Third Workshop on Patient-Oriented Language Processing ({CL}4{H}ealth) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.cl4health-1.14/",
doi = "10.63317/5p5zhpr2wknb",
pages = "149--158",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T HealthTrajectory: Patient Journey Summaries and Visualizations for Patient-Clinician Communication Support
%A Hidayah, Rohmah
%A Nishiyama, Tomohiro
%A Wakamiya, Shoko
%A Aramaki, Eiji
%Y Gupta, Deepak
%Y Thompson, Paul
%Y Ananiadou, Sophia
%Y Demner-Fushman, Dina
%S Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F hidayah-etal-2026-healthtrajectory
%X 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.
%R 10.63317/5p5zhpr2wknb
%U https://aclanthology.org/2026.cl4health-1.14/
%U https://doi.org/10.63317/5p5zhpr2wknb
%P 149-158
Markdown (Informal)
[HealthTrajectory: Patient Journey Summaries and Visualizations for Patient-Clinician Communication Support](https://aclanthology.org/2026.cl4health-1.14/) (Hidayah et al., CL4Health 2026)
ACL