Toward Scalable Patient Safety Training: A Prototype for Root Cause Analysis Simulation With AI Virtual Avatars

Abstract

Patient safety training is essential for preparing healthcare professionals to identify, investigate, and prevent adverse events. However, conventional simulation-based approaches often require substantial faculty time, physical resources, and standardized facilitation. This paper presents a prototype AI-powered simulation platform designed to support more scalable patient safety training through root cause analysis (RCA). The system provides a Unity-based 3D simulation environment, which allows trainees to investigate an ICU adverse event by interviewing five virtual team members represented as AI-powered avatars. Each avatar is driven by a large language model (LLM) agent with role-specific knowledge and variable states of mind. Moreover, emotional text-to-speech and AI-supported facial and body animation enable more realistic and immersive interactions. After completing the simulation, trainees submit a written RCA report and receive rubric-guided formative and summative feedback automatically generated by an LLM-based assessment component. The prototype is built to support patient safety training for healthcare professionals, focusing on skills in communication, investigation, thinking, and analysis, with low recurring instructional burden. We describe the design of the platform, its core technical components, and an RCA case based on a published ICU scenario. This work demonstrates the feasibility of integrating generative AI into immersive simulation for scalable patient safety education.