The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is found in the proposed technology development that facilitates direct, ubiquitous acquisition of key biomedical data, with little or no human intervention or introduction of errors, has the potential to reduce the costs of healthcare and to revolutionize healthcare delivery throughout our society. With readily available mobile computing platforms such as smartphones and tablets, and eventually relying on readily available or built-in sensors, the 21st century could herald an era where ?wellness maintenance? predominates over ?medical care?. Ongoing 24x7 monitoring could also facilitate the concept of more personalized time-derivative medicine, where significant changes from one?s own baseline state provide early warnings of a medical condition?rather than identifying departures from population averages, or waiting for symptoms to become severe. With widespread use, this technology, and the inherent level of automation of patient monitoring, could enable early detection of chronic disease, thereby significantly reducing mortality, morbidity and overall healthcare costs. Critically, this STTR team?s approach has the potential to expand healthcare to various rural and other underserved communities by making low-cost versions of this technology platform available and compatible with an already large installed base of personal, mobile computing platforms such as the patient?s own smartphone and digital cellular network infrastructure.

The proposed project will significantly advance knowledge on several fronts. First, this STTR team?s research involving healthcare organizations will inform us of an appropriate business model for introducing Digital Health Information Infrastructure (DHII) type technologies to the otherwise very complex healthcare marketplace. It will also provide information about which DHII features are critical, unimportant, and motivating for adoption of such systems. This is key and often-overlooked information when developing new medical technologies. This STTR team?s Phase I research will also expand the understanding about the limits of generalized monitoring across hardware and software platforms to identify areas where algorithmic optimization is needed to achieve higher levels of performance. Expectations are that the wireless data streaming from multiple sensors will require the implementation of data optimization and algorithmic triage protocols to ensure that critical information content is acquired and available for local analysis by on-board computer-aided diagnostics. This data will also be accessible through archival storage, with search and retrieval, at the internet-connected data repository implemented through traditional or cloud computing architectures.

National Science Foundation (NSF)
Division of Industrial Innovation and Partnerships (IIP)
Standard Grant (Standard)
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Jesus Soriano Molla
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Argosy Omnimedia, Inc.
United States
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