Interfacility transport (IFT) is essential when a patient's care needs exceed the capabilities of their current hospital. However, pediatric patients face a disproportionately high demand for IFT due to the specialized nature of pediatric care. Pediatric IFT is a complicated and risky process with adverse events occurring in up to 70% of critical care ambulance transports even with a highly trained team. This project aims to explore pediatric transport stakeholder workflows, decision making, technology, and communication to identify potential areas for improvement. In this phase of the project, we conducted semi-structured interviews with key stakeholders, including Medical Control Physicians (MCPs), Referring Providers (RPs), and Pediatric Critical Care Transport Teams (PCCT). Participants were asked to describe their roles, tasks, decision-making processes, and communication strategies throughout the transport process. The goal of the study is to qualitatively analyze these interviews to uncover key themes and insights. For methods, we employed NVivo qualitative analysis software to analyze data from 16 interviews. Participants were contacted and voluntarily agreed to take part in the study. Through this research, we aim to gain a deeper understanding of stakeholder experiences in the IFT process, which will inform future efforts to improve pediatric transport practices.

During neonatal inter-facility transport there is a critical need to accurately measure heart rate. The electrocardiogram (ECG)  signals are particularly noisy during transport due to factors such as road noise and infant movement. This inaccuracy leads to false alarms from patient monitors when the measured heart rate values fall out of range. The Pan-Tompkins algorithm is commonly used to measure heart rate from ECG signals but frequently fails under these conditions. This project introduces a novel variation of the Pan-Tompkins algorithm, using the derivative of the ECG signal with additional filters specifically designed to target transport-related noise in neonatal ECGs. We test this modified Pan-Tompkins against the traditional Pan-Tompkins on neonatal transport data to determine if it is more effective for neonatal transport. Each algorithm is applied to a common set of ECG signal patterns taken from a real neonatal transport. The different patterns are classified as clean, somewhat noisy, or very noisy. Each algorithm will be evaluated on Sensitivity and Positive Predictability for each pattern. This research will help save the lives of neonates by reducing false alarms, which will in turn reduce alarm fatigue for providers and draw their attention only when it is truly necessary.