ABSTRACT: ECHO PROJECT Patients undergoing complex surgeries are most vulnerable during the immediate postoperative period; thus, handoffs from the OR (operating room) to ICU (intensive care unit) require seamless communication and coordination between surgical, anesthesia, and critical care teams. Postoperative handoffs are a threat to patient safety, causing ~35% of medical errors in the US. To mitigate these errors, the National Patient Safety Goal (2E) necessitated the “standardization” of handoff process and content, which resulted in adoption of information transfer checklists, handoff process-based protocols, or both. Although such strategies have improved handoff quality, our meta-analysis found that such improvements were temporary and had limited sustainability, due to the structured formats imposing “rigid” standardization with limited flexibility and support for interactive and personalized communication. Our central hypothesis is that a flexible standardization approach will lead to not only improvements in information sharing, but also improvements in shared understanding of patient risks, handoff interactivity, and handoff duration. Towards this end, we propose to develop the INTERACT (Intelligent interactive care continuity) handoff bundle, a flexible, standardized, EHR- integrated, and resilient sociotechnical intervention comprised of a: (1) telemedicine-augmented handoff process (i.e., the social component) supported by a (2) machine learning (ML)-augmented handoff report (i.e., the technical component). INTERACT underscores the importance of using a perioperative telemedicine suite as a safety net to support resilience to errors in OR-ICU handoff process and content. The ML-augmented handoff report supports personalized communication of core (i.e., standardized) and tailored (flexible) content based on predicted patient risks for postoperative complications. Aim 1 will focus on updating our current ML models for predicting risks associated with p