Smarter Early-Warning for Newborns: Reducing Delays and Alarm Fatigue in the NICU

Early recognition of infection in the NICU can mean the difference between recovery and severe complications. Today’s monitors often overwhelm staff with false alarms, making it harder to act quickly when something is. This session introduces a practical, data-driven framework that uses the bedside information hospitals already collect to provide smarter early warnings. The approach was designed to ensure timely alerts while keeping alarm rates manageable. Attendees will learn how subtle changes were detected hours before infection was typically recognized. The broader takeaway is a framework for balancing patient safety, workload, and technology. It shows how smarter monitoring can reduce delays, improve outcomes, and support staff in high-stress environments.

Explain how smarter monitoring can improve newborn safety by detecting deterioration earlier while reducing alarm fatigue.

Describe how to set and maintain acceptable alarm thresholds that balance timely detection with manageable clinical workload.

Apply the lessons from this project to other healthcare settings where early warning and workflow integration are critical.

Authors

Ryan McCullough is a Doctor of Engineering candidate at Penn State focused on healthcare systems innovation, with research in neonatal intensive care monitoring and early-warning systems. He brings over a decade of experience leading technology and process improvement initiatives in healthcare and defense sectors, specializing in data-driven decision making and system resilience. His professional interests center on applying advanced analytics to improve patient outcomes, reduce alarm fatigue, and support clinician workflows. Personally, he is passionate about bridging the gap between research and practice to deliver sustainable, real-world healthcare improvements.