SBIR Phase I: Utilization, Condition, and Location Tracking for Clinical Assets

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to improve the quality of healthcare in the United States by reducing the costs to maintain clinical equipment. US hospitals spend $93 billion yearly on medical equipment life cycle costs (second only to personnel). Large inefficiencies exist; a typical hospital has 25% over-inventory of equipment, resulting in $1 M annually in unnecessary capital and operational expense. Current monitoring systems use wireless tags to locate equipment but these do not reduce equipment maintenance costs. This proposal will develop a new tag with sensors and software to monitor not only equipment location but also utilization and condition to optimize inventory levels. This system will generate further savings by optimizing equipment service intervals based on usage and condition rather than simply elapsed time. Most importantly, this system will improve patient outcomes by detecting faults requiring immediate service, such as drops and failing mechanical components. This Small Business Innovation Research (SBIR) Phase I project will advance development of technology enabling hospitals to optimally manage clinical equipment. The solution mounts small battery-powered wireless tags (with sensors and machine learning algorithms) on equipment for monitoring. The research plan will address three main technical challenges: applicability, scalability and readiness as follows: 1) Applicability: Measure a wide variety of device types, analyze collected sensor data, identify algorithms mapping sensor data to context, and test performance under real-world scenarios; 2) Scalability: Develop procedures and tools to create an algorithm library for the thousands of device-type/make/models in the hospital market; and 3) Readiness: Characterize the cost-size-battery life trade space, with a goal of tag life of 10 years, including exploring battery alternatives,sensors with deep-sleep modes, and adaptive algorithms maintaining device context with maximum sleep intervals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.