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Contract Topics

278 Data Harmonization and Advanced Computation of Population Health Data

Number of anticipated awards: 2

(Fast-Track proposals will NOT be accepted. Phase II information is provided only for informational purposes to assist Phase I proposers with their long-term strategic planning.)

Budget (total costs): Phase I: $100,000;
Phase II: $750,000

(Note: It is strongly suggested that Proposals adhere to the above budget amounts. Proposals with budgets exceeding the above amounts may not be funded. Phase I project periods may last a maximum of 9 months.)

The deadline for receipt of all contract proposals submitted in response to this solicitation is: November 9, 2009.

Summary:
The National Institutes of Health (NIH) face a key inflection point in confronting the 'cells to society' deluge of health-related electronic data spanning the genomics/proteomics, biomedical, behavioral, and population health disciplines (HHS, 2008). The challenge of understanding and, more importantly, making effective use of this fragmented information environment underscores what the Institute of Medicine (IOM) describes as the "structural inability" of conventional medicine to exploit the unprecedented amount of electronic information available at our fingertips (IOM, 2007). Partially in response to this challenge, the Federal government's national HIT strategic plan has been predicated on the notion of appropriate access to and sharing of electronic health information through a national health information infrastructure (HHS, 2008). This is elaborated on in the National Research Council's recent recommendations on Federal investment in cyberinfrastructure and computational techniques for cancer prevention and control (National Research Council, 2009).

NCI is responding to these challenges by connecting the cancer community through the cancer Biomedical Informatics Grid (caBIG^®), an Internet-scale distributed network for sharing health related information, promoting scientific collaboration, and applying advanced computational techniques for large volumes of electronic data. To date, caBIG^® has been utilized most effectively by the biomedical and genomic/proteomics communities. Consequently, there is a clear need for development of computational applications, services, and tools for behavioral and population health sciences that can 1. Mount and harmonize population health data including behavioral, psychosocial, environmental, and clinical data onto caBIG^®; and 2. Extract useful information from the harmonized datasets using innovative computational techniques (both linear and non-linear methods including signal detection, data-driven modeling approaches such as neural networks, clustering, functional modeling, and many other data/signal processing techniques) and data visualization methods for decision support tools that help improve cancer prevention and control in patients, providers, and/or the research enterprise.

Health and Human Services (2008). Coordinated Federal Health Information Technology Strategic Plan: 2008-2012. The Office of the National Coordinator for Health IT. Washington, DC: Department of Health and Human Services.

Institute of Medicine (2007). The Learning Healthcare System: Workshop Summary. Edited by L.A. Olsen, D. Aisner, and J. M. McGinnis. Washington, DC: National Academies Press.

National Research Council (2009). Computational Technology for Effective Healthcare: Immediate steps and strategic directions. Edited by W. Stead and H. Lin. Washington, DC: National Academies Press.

Project Goals:
The goal of this topic is to encourage development of innovative computational applications, services, and tools for caBIG^® that can accommodate the electronic population health data described in the topic summary to accelerate knowledge discovery. Potential applications could target any specific problem or domain in cancer prevention and control including decision support tools in health communication and informatics, screening, survivorship, diet and physical activity, tobacco use, health disparities, biological mechanisms of psychosocial effects on disease, and clinical and public health systems (see http://cancercontrol.cancer.gov/). Proposals must be capable of harmonizing disparate population health datasets (e.g., data from behavioral surveys, the built environment, geographic information systems, large public health surveillance datasets, clinical EHR systems, PHR systems,.) onto caBIG^®, with at least one of these data sources being behavioral medicine and social science-related data. The caBIG^®-based application should also be able to extract useful information from the harmonized data using innovative computational techniques (both linear and non-linear methods including signal detection, data-driven modeling approaches such as neural networks, clustering, functional modeling, and many other data/signal processing techniques) and data visualization methods for scientists, patients, and/or providers for cancer prevention and control.