EnhanCed HandOffs (ECHO)

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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 postoperative complications, based on state-of-the-art imputation and feature engineering techniques. We will enhance our model-agnostic explanation framework to support postoperative handoffs and decision-making, which will also be validated with a summative user evaluation study. Aim 2 will follow a user-centered design approach to iteratively develop and test the INTERACT bundle including handoff report design ideation, and usability testing, and lastly, the INTERACT bundle in-situ simulations. Aim 3 will adopt a Hybrid Type 1 trial design and the Care Transitions Framework to evaluate the effectiveness and implementation-potential of the INTERACT bundle. Our primary outcome is information sharing score (i.e., a measure of information completeness), while secondary outcomes include information inaccuracies, realized errors and adverse events, and ICU length of stay. With an integrated multidisciplinary approach to improving perioperative care transitions, the proposed INTERACT bundle will address the stated AHRQ FOA goals of “defragmenting information, improving communication, and assuring care team access to reliable and complete health information; and empowering care teams to improve health outcomes.”