@inproceedings{ge-etal-2025-high,
    title = "High-Quality Medical Dialogue Synthesis for Improving {EMR} Generation",
    author = "Ge, Chengze  and
      Xu, Yu  and
      Shao, Qi  and
      Liu, Shengping",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-industry.181/",
    doi = "10.18653/v1/2025.emnlp-industry.181",
    pages = "2675--2687",
    ISBN = "979-8-89176-333-3",
    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."
}