Clinicians are expected to disclose harmful medical errors to patients and families in line with ethical, regulatory, and patient care standards, yet these conversations remain challenging because of their emotional complexity and limited training opportunities. Most physicians still learn primarily through lectures and observation, while static video tools-though available-are underused, lack adaptability across specialties, and deliver delayed, generic feedback. These gaps restrict skill development, reduce self-efficacy, and contribute to avoidance of disclosure conversations, ultimately compromising patient care and eroding trust. To address these needs, we designed CandorMD -- an AI-assisted simulation system that provides real-time practice, actionable feedback, and diverse practice environments tailored to individual learning needs. We conducted semi-structured interviews with physicians, risk managers, patient advocates, and communication experts to understand current practices, identify gaps, and collect feedback on CandorMD. Based on these insights, we present findings and design recommendations for the future of AI-supported medical communication training.

翻译：临床医生应遵循伦理、监管和患者护理标准，向患者及其家属披露有害的医疗差错，但由于此类对话的情感复杂性和有限的培训机会，实施起来仍颇具挑战。大多数医生仍主要通过讲座和观察进行学习，而静态视频工具——尽管可用——却未被充分利用，缺乏跨专业适应性，且提供的反馈迟滞且笼统。这些缺陷限制了技能发展，降低了自我效能感，并导致医生回避披露对话，最终损害患者护理并侵蚀信任。为应对这些需求，我们设计了CandorMD——一个AI辅助的模拟系统，可提供实时练习、可操作性反馈以及根据个人学习需求定制的多样化练习环境。我们通过对医生、风险管理者、患者权益倡导者以及沟通专家进行半结构化访谈，以了解当前实践、识别差距，并收集关于CandorMD的反馈。基于这些见解，我们为未来AI支持的医学沟通培训提出了研究结果和设计建议。