NSF’s Healthcare Projects

The National Science Foundation’s (NSF)  “Smart and Connected Health” (SCH) program is developing innovative approaches to support the transformation of healthcare which is going from being reactive and hospital-centered to preventive, proactive, evidence-based, and person-centered.

The interagency SCH program including NSF and HHS is looking at how to develop next generation healthcare solutions and how to encourage existing and new research communities to focus on breakthrough ideas. Specifically, the SCH program is interested in sensor technology, networking, information and machine learning technology, decision support systems, modeling of behavioral and cognitive processes, as well as system and process modeling.

The SCH program recently issued a solicitation. To read the interagency solicitation (13-543), go to https://www.nsf.gov/funding/pgm_summjsp?pime_id=504739. The solicitation due fall 2014 is looking for ideas on how to proceed with the next generation health and healthcare research in information science, technology behavior, cognition, sensors, robotics, bio-imaging, and engineering.

Collaboration on the solicitation is encouraged between academic, industry, non-profits, and other organizations to establish linkages between fundamental science, clinical practice, and technology development and use.

The solicitation includes two classes of proposals. One proposal class includes “Exploratory Projects” with one or more investigators spanning 1 to 3 years due October 10, 2014. The other proposal class called “Integrative Projects” requires multidisciplinary teams spanning 1 to 4 years and due December 10, 2014.

One project in 2011 enabled SCH to award $371,999 in 2011 to Ohio University. The project titled “Small Machine Learning Models for Blood Glucose Prediction in Diabetes Management” is estimated to be completed in July 2014.

The problem in managing diabetes is that although large volumes of blood glucose data is collected automatically, automated analysis of that data is lacking. As a result, patients do not always know when problems are impending or if problems are occurring while they are asleep.

This project is working on machine learning models that would be able to predict blood glucose levels to enable or facilitate new applications that could directly benefit patients. This information could include alerts, enable decision support systems to recommend actions to prevent problems, and provide educational simulations showing the effects of the different treatment choices or lifestyle options that can affect blood glucose levels.

Go to www.nsf.gov/awardsearch/show/Award?AWD_ID=1117489&HistoricalAwards=false for more information on the award (111748). The program manager is Sylvia J. Spengler at sspengle@nsf.gov.