Pediatric Transport Learning Laboratory

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Project Summary/Abstract The overall goal of this project is to advance the care of pediatric patients (up to 21 years of age) during medical ground or air transport from one hospital to another within a regional network. The project will build on our previous experience with neonatal patient transport and use a five-stage innovation cycle, including: problem analysis, design, development, implementation, and evaluation to identify and address the salient issues and risks of regional neonatal transportation for which new and innovative approaches are needed. In collaboration with transport providers and other stakeholders, we will analyze current workflow processes, transport records (local and statewide databases), and facilities at referral and receiving facilities to develop a complete understanding of system issues and to define the current and ideal states. This detailed problem analysis phase will enable the integration of real-time data into a transport “digital twin” model to optimize regional consultation, triage, and transport of pediatric patients to facilities with the appropriate level of care and availability of space and staffing. With the input of clinical transport team staff and stakeholders, the project team will work with collaborators from the University of Washington Industrial and Systems Engineering Department to integrate machine learning into the Transport Monitoring and Communications (T-MAC) system to support the analysis of data feeds while on transport. The machine learning augmented T-MAC system will undergo repeated testing and revision to ensure that it can functionally and efficiently facilitate information flow between the medical control physician, referring facility and transport team. In addition, we will develop robust processes to support information flow to patients and families in the peri- transport period. To accomplish this, we will work with families to identify gaps in communication and connection to local and receiving facility resources and identify transport-specific needs. The efficacy of the T-MAC system will be evaluated in a realistic in situ simulation and clinical settings. We are confident that the lessons learned through this study will improve pediatric patient safety on medical transports and will be transferable to patient populations that undergo both short and long-range interfacility transports.