CandorMD: An AI-Assisted Audio Simulation and Feedback System for Training Clinicians for Medical Error Disclosure

2026-05-20

AI Summary

This study addresses the lack of effective methods for clinicians to develop communication skills for disclosing medical errors in emotionally complex situations, a domain where traditional training struggles to provide personalized, real-time feedback. To bridge this gap, the authors propose an AI-driven audio-based simulation and feedback system that integrates speech interaction, natural language processing, and scenario-based modeling. The system enables physicians to practice disclosure conversations across diverse, specialty-specific contexts and receive dynamic, actionable feedback in real time. Validation through multi-stakeholder expert interviews demonstrates that the platform not only fills critical gaps in interactivity and adaptability within current medical communication training but also articulates key design principles for AI-supported communication skill development, ultimately enhancing clinician self-efficacy and fostering patient trust.

Abstract

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.

Problem

• medical error disclosure
• clinical communication training
• simulation-based learning
• AI-assisted feedback
• healthcare education

Innovation

• AI-assisted simulation
• medical error disclosure
• real-time feedback
• clinical communication training
• adaptive learning