Smart and Connected Health (SCH)
RFP Summary provided by the agency
The work to be funded by this solicitation must make fundamental contributions to two or more disciplines, such as computer or information sciences, engineering, social, behavioral, cognitive and/or economic sciences and address a key health problem. Traditional disease-centric medical, clinical, pharmacological, biological or physiological studies and evaluations are outside the scope of this solicitation. The research teams must include members with appropriate and demonstrable expertise in the major areas involved in the work. Proposals submitted to this solicitation must be consistent with the project class defined below:
Integrative projects (INT) undertake research addressing key application areas by solving problems in multiple scientific domains. The work must make fundamental contributions to two or more disciplines, such as computer or information sciences, engineering, social, behavioral, cognitive and/or economic sciences and address a key health problem. For example, these projects are expected to advance understanding of how computing and engineering, combined with advances in behavioral and social science research, would support transformations in health, medicine and/or healthcare and improve the quality of life. Projects are expected to include several students and postdocs.
What is the mission and focus of the program: research, social, economic or others?
The goal of the interagency Smart and Connected Health (SCH): Connecting Data, People and Systems program is to accelerate the development and integration of innovative computer and information science and engineering approaches to support the transformation of health and medicine. Approaches that partner technology-based solutions with biomedical and biobehavioral research are supported by multiple agencies of the federal government including the National Science Foundation (NSF) and the National Institutes of Health (NIH).
The purpose of this program is to develop next-generation multidisciplinary science that encourages existing and new research communities to focus on breakthrough ideas in a variety of areas of value to health, such as networking, pervasive
computing, advanced analytics, sensor integration, privacy and security, modeling of socio-behavioral and cognitive processes and system and process modeling. Effective solutions must satisfy a multitude of constraints arising from clinical/medical needs, barriers to change, heterogeneity of data, semantic mismatch and limitations of current cyberphysical systems and an aging population. Such solutions demand multidisciplinary teams ready to address issues ranging from fundamental science and engineering to medical and public health practice.
How do you submit to this opportunity?
Proposers may opt to submit proposals in response to this Program Solicitation via Grants.gov or via the NSF FastLane system.
Who are the target applicants: cities, universities, companies, small business, nonprofits, or others?
Institutions of Higher Education (IHEs) – Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members. Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of sub-awards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus.
Non-profit, non-academic organizations: Independent museums, observatories, research labs, professional societies and similar organizations in the U.S. associated with educational or research activities.
Example project(s) summaries from past RFPs:
RAPID: COLLABORATIVE RESEARCH: Building Infrastructure to Prevent Disasters like Hurricane Maria, Awarded Amount to Date: $32,574, This project aims to expand access to environmental and drinking water quality disaster response and recovery data in a publicly available format using a widely used collaborative online sharing platform named HydroShare. Curating a central repository of assembled data has the potential to greatly facilitate coordinated disaster responses of all types, and improve the monitoring of the recovery process. The project team will prototype this system with an assessment of drinking water, environment, and public health concerns unique to Puerto Rico in the aftermath of Hurricane Maria. By working directly with public water utilities, the project team intends to characterize and map the severity of impaired water resources and distribution systems in Puerto Rico, inform communities about how to protect themselves against hazards specific to their water, and to contribute to rebuilding so the nation is better prepared for future hurricanes. Developing cyber and social infrastructure to understand the dynamics of drinking water contamination after natural disasters will improve disaster preparedness and response, and contribute to efforts across the nation and the world to build for a resilient future. https://www.nsf.gov/awardsearch/showAward?AWD_ID=1810172&HistoricalAwards=false
(ii) Example project(s) summaries from past RFPs:
SCH: INT: Collaborative Research: Crowd in Action: Human-Centric Privacy-Preserving Data Analytics for Environmental Public Health, Awarded Amount to Date: $307,999, This multidisciplinary research advances the state-of-the-art public health by combining multi-scale data collection and analysis. Specifically, the project redesigns current healthcare monitoring systems for both severe infectious diseases and long-term environment-related diseases and their exacerbation (e.g., air pollutant-induced pulmonary diseases, such as chronic obstructive pulmonary disease and lung cancer). By considering the high sensitivity and distributed manner of the data from patients and users, this project addresses the privacy preservation in two-fold: 1) completely redesign efficient collaborative classification schemes by applying novel metrics without leaking individual’s privacy; and 2) introduce new architectures to perform crowdsourcing data analysis by using light-weighted and verifiable encryption schemes. This project also grounds the theoretical outcomes to actual crowdsensing systems and social networks for validation. Finally, a new methodology on public health prediction model is developed with practical systematic implementation in healthcare systems. https://www.nsf.gov/awardsearch/showAward?AWD_ID=1722731&HistoricalAwards=false
