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        <title>High-Quality Medical Dialogue Synthesis for Improving EMR Generation</title>
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    <abstract>High-quality doctor–patient dialogues, by which we mean realistic and human-like interactions that are intent-consistent, clinically faithful, and free of contradictions, are crucial for accurate Electronic Medical Record (EMR) generation. However, collecting large-scale real dialogues is costly and constrained by privacy regulations, while existing synthetic methods often yield rigid and medically inconsistent dialogues. We propose a scalable framework integrating (1) Intent Graph Planning for diverse clinical flows, (2) Dual-Agent Simulation for realistic doctor-patient interactions, and (3) Rule-Reward Quality Control combining explicit medical rules with a self-supervised reward model. Experiments across multiple clinical domains demonstrate that our synthesized dialogues significantly enhance realism, diversity, and downstream EMR quality, substantially reducing physician editing efforts. Our framework provides a practical and privacy-compliant solution for deploying robust clinical NLP systems.</abstract>
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%0 Conference Proceedings
%T High-Quality Medical Dialogue Synthesis for Improving EMR Generation
%A Ge, Chengze
%A Xu, Yu
%A Shao, Qi
%A Liu, Shengping
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F ge-etal-2025-high
%X High-quality doctor–patient dialogues, by which we mean realistic and human-like interactions that are intent-consistent, clinically faithful, and free of contradictions, are crucial for accurate Electronic Medical Record (EMR) generation. However, collecting large-scale real dialogues is costly and constrained by privacy regulations, while existing synthetic methods often yield rigid and medically inconsistent dialogues. We propose a scalable framework integrating (1) Intent Graph Planning for diverse clinical flows, (2) Dual-Agent Simulation for realistic doctor-patient interactions, and (3) Rule-Reward Quality Control combining explicit medical rules with a self-supervised reward model. Experiments across multiple clinical domains demonstrate that our synthesized dialogues significantly enhance realism, diversity, and downstream EMR quality, substantially reducing physician editing efforts. Our framework provides a practical and privacy-compliant solution for deploying robust clinical NLP systems.
%R 10.18653/v1/2025.emnlp-industry.181
%U https://aclanthology.org/2025.emnlp-industry.181/
%U https://doi.org/10.18653/v1/2025.emnlp-industry.181
%P 2675-2687

Markdown (Informal)

[High-Quality Medical Dialogue Synthesis for Improving EMR Generation](https://aclanthology.org/2025.emnlp-industry.181/) (Ge et al., EMNLP 2025)

• High-Quality Medical Dialogue Synthesis for Improving EMR Generation (Ge et al., EMNLP 2025)

ACL

• Chengze Ge, Yu Xu, Qi Shao, and Shengping Liu. 2025. High-Quality Medical Dialogue Synthesis for Improving EMR Generation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2675–2687, Suzhou (China). Association for Computational Linguistics.