ReflectOR: an LLM-based Agent for Post-Operative Surgical Debriefing

Ineffective teamwork and communication can generate medical errors in the high-pressure environment of surgery, making post-operative debriefings essential for enhancing team performance and patient safety. However, these sessions are frequently rushed or incomplete due to clinicians' limited time. This paper introduces ReflectOR, an Agentic-AI architecture designed to support surgical debriefings by processing audio recordings from the operating room. The system employs specialized sub-agents that perform tasks such as generating summaries, constructing timelines of intraoperative events, identifying potential errors, and counting the materials used. A qualitative evaluation indicates that the system effectively contextualizes transcripts, demonstrating its potential as a valuable tool for surgical debriefing. The paper also outlines key considerations for applying such an architecture in real-world clinical environments.