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

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.