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.