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Large language model-based simulated patient training for heart failure palliative care communication: a pilot study.

Created on 05 Aug 2026

Authors

Risa Kishikawa, Hiroyuki Morita, Satoshi Kodera

Published in

European heart journal. Digital health. Volume 7. Issue 7. Pages ztag119. Epub Jul 22, 2026.

Abstract

Heart failure (HF) palliative care communication is essential but difficult to train at scale because conventional role-play programmes require facilitators and standardized patients. Large language models (LLMs) have emerged as potential tools for scalable communication training. This pilot study aimed to evaluate the feasibility of a web-based LLM-driven communication training application and to explore its early educational signal on physicians' self-efficacy.
This single-arm pilot study included physicians who completed one session using a Japanese-language web-based LLM application designed to simulate patients with advanced HF and provide automated framework-based feedback. The primary outcome was change in self-efficacy scores assessed by pre- and post-session questionnaires. Ten sessions were analysed. Physicians engaged in a mean of 7.6 ± 2.0 dialogue turns. Mean response time per model output and feedback generation were approximately 3 and 17 s, respectively. Significant improvements were observed in knowledge of palliative care communication (mean difference +1.7, adjusted P < 0.01) and confidence in HF palliative care communication (+1.2, adjusted P = 0.03). Other domains showed non-significant changes.
This pilot study demonstrated the feasibility of a web-based LLM-simulated patient system and suggested an early educational signal in physicians' self-efficacy for HF palliative care communication. Our scalable LLM-driven communication training may complement traditional educational approaches with further evaluation in larger controlled studies.
Trial registration number: UMIN000059988.

PMID:
42553746
Bibliographic data and abstract were imported from PubMed on 05 Aug 2026.

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