Project Summary More than a third of pediatric adverse events have been attributed to diagnostic error—the failure to establish an accurate and timely explanation of a child’s health problem. Within the pediatric intensive care unit (PICU), a high-stakes, complex, team-driven environment, diagnostic error rates vary between 8% and 25%. To improve diagnostic accuracy, clinical decision support (CDS) tools, including artificial intelligence and machine learning algorithms, have been developed but have failed to be adopted within the PICU. Implementation of CDS into an active high-risk clinical context without a comprehensive understanding of how providers and staff interface with the system can lead to patient safety consequences. These consequences can include disrupted workflows, alert fatigue, and inaccurate documentation that can lead to direct patient harm. We propose leveraging a currently built highly realistic immersive virtual reality PICU clinical environment to assess implementation strategies on clinician acceptance of CDS tools to improve diagnostic excellence within a patient safety learning lab. We will evaluate three aims using a systems-engineering approach combining the expertise of LiveWell, an award-winning design collaborative with user-centered design and human factors expertise, with the clinical knowledge, implementation science expertise, and world-renowned virtual simulation program of Cincinnati Children’s Hospital Medical Center (CCHMC). We will: redesign how clinicians interface with CDS tools for diagnosis-intensive events within the pediatric critical care environment including admission and clinical deterioration; develop and implement design-informed CDS tools for diagnosis and to evaluate their appropriateness/acceptability and impact on diagnostic uncertainty, accuracy, and timeliness within a digital twin immersive virtual reality simulation; and establish best practice guidance for implementation of CDS tools in the critical care environment to improve diagnostic excellence. Outcomes will include 1) appropriateness, acceptability, and adoption of design informed CDS tools; 2) impact on diagnostic accuracy and timeliness in the simulated clinical environment; and 3) a proactive implementation plan for the integration of design informed CDS tools to improve diagnostic excellence into the critical care environment.