Overview
Transforming Behavioral
Medicine: Cyberinfrastructure
in Cancer Prevention and
Control
Abdul R Shaikh, PhD, MHSc
...
An interdisciplinary field concerned with
the development and integration of
behavioral, psychosocial, and biomedical
scie...
• Theory: interrelated concepts, definitions, propositions
that present a systematic view of situations/events by
specifyi...
Individual
– Health Belief Model (Janz & Becker, 1984), Theory of Planned
Behavior (Ajzen, 1991), Transtheoretical model (...
Program Announcement PA-08-239:
Impact of Health Communication Strategies on Dietary Behaviors
Ecological Framework for Di...
MISSION: DCCPS aims to reduce the risk, incidence, and
deaths from cancer as well as enhance the quality of life for
cance...
DCCPS Cancer Control Framework
Adapted from the 1994 Advisory Committee on Cancer Control, National Cancer Institute of
Ca...
Knowledge
Synthesis
Basic Science
Application &
Program Delivery
Surveillanc
e
Intervention
Research
Reducing the Cancer B...
Knowledge
Synthesis
Basic
Science
Application &
Program Delivery
Surveillance
Intervention
Research
Reducing the Cancer Bu...
Knowledge
Synthesis
Basic Science
Application &
Program
Delivery
Surveillance
Intervention
Research
Reducing the Cancer Bu...
Knowledge
Synthesis
Basic Science
Application &
Program Delivery
Surveillance
Intervention
Research
Reducing the Cancer Bu...
Public Health Informatics
• IT for improving cancer-related care and
ultimately, cancer-related outcomes
• 15+ years bench...
Public Health Informatics &
Engineering
PHI = the systematic application of
information and computer science and
technolog...
Behavioral Medicine and the
Information Landscape
Islands of
datasets,
documents,
analytic tools,
and research
communities...
Behavioral Data
Field based data
Public Health Surveillance
National Surveillance
Data (e.g., NHIS,
BRFSS, NHANEs)
Routine...
Behavioral Medicine and the
Information Landscape
- Overwhelming- Overwhelming
volume of datavolume of data
- Multitude of...
“The Petabyte Age: Sensors
everywhere. Infinite storage.
Clouds of processors. Our ability to
capture, warehouse, and
unde...
Enter Cyberinfrastructure .
caBIG
Grid
Expand the Scope of Discovery
Visualization
Decision
Support
Fusion
Policy
Planning
Application
Layer
Users
Pattern Detect...
Visualization
Discovery
Decision
Support
Fusion
Policy
Planning
Application
Layer
Users
Courtesy Ben Shneiderman, 2006; NC...
Decision Support & Policy Planning
Discovery
Visualization
Fusion
Application
Layer
Users
Courtesy Katy Börner, 2006; NCI ...
Connecting Stove-piped Data
Discovery
Visualization
Decision
Support
Policy
Planning
Application
Layer
Users
Advanced Anal...
‘Populomics’ and the Grid
Proteomics
Nanotechnology
Populomics
Genomics
Personalized Medicine:
Pharmaco-genomics…
Personal...
.
• .
caBIG®
cancer Biomedical Informatics Grid
caBIG®
Goal
A virtual web of interconnected data, individuals, and organiz...
.
• .
caBIG®
Goal
Clinical Research
PathologyMolecular Biology
Imaging
• Track clinical trial
registrations
• Automaticall...
.
• .
caBIG®
Goal
caGrid High-Level Architecture
.
• .
caBIG®
Goal
• caBIG®
adoption is unfolding in:
– 49 NCI-designated
Cancer Centers
– 16 NCI Community
Cancer Centers
...
Take a Slice of the Cake
Consortium in Abbas, A. (2004): Grid Computing: A Practical Guide to Technology and Applications....
• Proof of concept for CI in population health and cancer
control
• Use state-of-the-science technology to link data,
rese...
Challenges…Not just technology &
infrastructure
• Collaboration
– Within and across disciplines
• Privacy, de-identificati...
 Implement services on the Grid
 HINTS, NHIS, and tobacco tax data
 Basic statistics, categorical analysis, and
prevale...
• Prospective geo-
spatial analytics
PopSciGrid
• 14 datasets spanning 6 years
• Real-time access/analysis of
public healt...
Web 2.0 / Science 2.0
• Architectures for
Participation
• Collective Intelligence
• Data as the new “Intel
Inside”
Volume ...
Virtual Organizations & Interdisciplinary
Science
Data Widgets
State Cancer
Profiles
CISNet Decision
Aids
Application layer (e.g., Enhanced State Cancer
Profiles; Dashboard...
(Grid Enabled Measures) Database
PopSciGrid 2.0 – Priming the Pump
A grid-enabled, interoperable, dynamic website for beha...
GEI: Genes, Environment, and Health Initiative
• NIH-wide, led by NHGRI and NIEHS
• Goal: to accelerate understanding of t...
– .
GEI – Genes and Environment
EXPOSURE BIOLOGY
PROGRAM
Identify genetic
variants
GENETICS PROGRAM
GXE
Develop
technology...
.
GEI - Timeline
FY07 FY08 FY09 FY10 FY11
U01
U54
Environmental Sensors
• Diet/Physical Activity (NCI/NHLBI)
• Psychosocia...
.
GEI – Technology (cont.)
Innovative technologies to measure diet, PA, stress, addictive
substances, chemical sensors, bi...
GEI – PALMS (Physical Activity Location Measurement System)
PI: Kevin Patrick, University of California San Diego
.
Looking Ahead: Institute for the Future
Mike Liebhold (2008); www.IFTF.org
Talk is cheap…
American Recovery and Reinvestment Act 2009:
http://www.cancer.gov/recovery
NIH Challenge Grants in Health ...
Funding – Challenge Grants
(10) Information Technology for Processing Health Care Data
10-CA-101* Cyber-Infrastructure for...
Funding – Challenge Grants (cont.)
(10) Information Technology for Processing Health Care Data
10-EB-101 Engineering impro...
Funding – SBIR / STTR
SBIR (Small Business Innovation Research): PI from small
company (51%) with academic consultants
STT...
Funding – SBIR / STTR
Products Examples: cancer-related PC software;
interactive DVDs; wireless devices; web/TV/radio prog...
.
Acknowledgements
NCI
– Rick Moser
– Glen Morgan
– Erik Augustson
– Frank White
– Jill Reedy (GEI)
– Amy Subar (GEI)
NHLB...
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  • Forum overarching questions:
    But wait, what’s new about this?
    (2) This seems a little too pioneering. Is it ready for prime time?
    (3) How in the world are we going to convince our investigators to share data?
    (4) Are we the only ones out here on a ledge? Are there others struggling with the same questions?
    (5) Are there pressures that will make us have to solve the problem, or is this just a manufactured need? Is the impending data deluge a Katrina-like hurricane, or a merely a “tempest in a teapot”?
    (6) Is the culture of science ready?
  • Population sciences research
    ---Public population data
    ---Obstacles of data collaboration
    caBIG™ as a research platform
    ---Integrate data sources and analytical services
    ---Facilitate collaboration
    PopSciGrid: a grid for population sciences
    ---Putting tobacco data on the Grid
    ---Analyze and visualize datasets in PopSciGrid
    Knowledge discovery on the Grid
  • Population sciences research
    ---Public population data
    ---Obstacles of data collaboration
    caBIG™ as a research platform
    ---Integrate data sources and analytical services
    ---Facilitate collaboration
    PopSciGrid: a grid for population sciences
    ---Putting tobacco data on the Grid
    ---Analyze and visualize datasets in PopSciGrid
    Knowledge discovery on the Grid
  • Population sciences research
    ---Public population data
    ---Obstacles of data collaboration
    caBIG™ as a research platform
    ---Integrate data sources and analytical services
    ---Facilitate collaboration
    PopSciGrid: a grid for population sciences
    ---Putting tobacco data on the Grid
    ---Analyze and visualize datasets in PopSciGrid
    Knowledge discovery on the Grid
  • Population sciences research
    ---Public population data
    ---Obstacles of data collaboration
    caBIG™ as a research platform
    ---Integrate data sources and analytical services
    ---Facilitate collaboration
    PopSciGrid: a grid for population sciences
    ---Putting tobacco data on the Grid
    ---Analyze and visualize datasets in PopSciGrid
    Knowledge discovery on the Grid
  • Population sciences research
    ---Public population data
    ---Obstacles of data collaboration
    caBIG™ as a research platform
    ---Integrate data sources and analytical services
    ---Facilitate collaboration
    PopSciGrid: a grid for population sciences
    ---Putting tobacco data on the Grid
    ---Analyze and visualize datasets in PopSciGrid
    Knowledge discovery on the Grid
  • Population sciences research
    ---Public population data
    ---Obstacles of data collaboration
    caBIG™ as a research platform
    ---Integrate data sources and analytical services
    ---Facilitate collaboration
    PopSciGrid: a grid for population sciences
    ---Putting tobacco data on the Grid
    ---Analyze and visualize datasets in PopSciGrid
    Knowledge discovery on the Grid
  • Population sciences research
    ---Public population data
    ---Obstacles of data collaboration
    caBIG™ as a research platform
    ---Integrate data sources and analytical services
    ---Facilitate collaboration
    PopSciGrid: a grid for population sciences
    ---Putting tobacco data on the Grid
    ---Analyze and visualize datasets in PopSciGrid
    Knowledge discovery on the Grid
  • IEEE

    1. 1. Overview Transforming Behavioral Medicine: Cyberinfrastructure in Cancer Prevention and Control Abdul R Shaikh, PhD, MHSc Bradford Hesse, PhD Health Communication and Informatics Research Branch Division of Cancer Control and Population Sciences National Cancer Institute March 25, 2009
    2. 2. An interdisciplinary field concerned with the development and integration of behavioral, psychosocial, and biomedical science…and the application of this knowledge to prevention, diagnosis, treatment and rehabilitation. (Society of Behavioral Medicine www.sbm.org) What is Behavioral Medicine?
    3. 3. • Theory: interrelated concepts, definitions, propositions that present a systematic view of situations/events by specifying relations among variables to explain and predict the situations/events (Kerlinger, 1986) - Parsimoniously present complex information - Help to narrow research topics into specific questions - Designate variables to be operationalized - A priori hypotheses and parametric statistics Theories and Frameworks (bread and butter)
    4. 4. Individual – Health Belief Model (Janz & Becker, 1984), Theory of Planned Behavior (Ajzen, 1991), Transtheoretical model (Prochaska, 1979) Interpersonal – Social Cognitive Theory (Bandura, 1978;1997), social networks and social support (Israel, 1982; House, 1981) Group/community/mass-media – Community building/empowerment (Minkler & Wallerstein, 2002), Diffusion of Innovations (Rogers, 1983) Ecological – - Ecological approach (Stokols, 1992), PRECEDE-PROCEED (Green & Kreuter, 1991) Theories of Behavioral Medicine
    5. 5. Program Announcement PA-08-239: Impact of Health Communication Strategies on Dietary Behaviors Ecological Framework for Diet & Communication
    6. 6. MISSION: DCCPS aims to reduce the risk, incidence, and deaths from cancer as well as enhance the quality of life for cancer survivors. The Division conducts and supports an integrated program of the highest quality behavioral, epidemiologic, genetic, social, and surveillance cancer research.
    7. 7. DCCPS Cancer Control Framework Adapted from the 1994 Advisory Committee on Cancer Control, National Cancer Institute of Canada. Reducing the cancer burden
    8. 8. Knowledge Synthesis Basic Science Application & Program Delivery Surveillanc e Intervention Research Reducing the Cancer Burden Surveillance
    9. 9. Knowledge Synthesis Basic Science Application & Program Delivery Surveillance Intervention Research Reducing the Cancer Burden Basic Science & Intervention
    10. 10. Knowledge Synthesis Basic Science Application & Program Delivery Surveillance Intervention Research Reducing the Cancer Burden Small Business Innovation Research Grants (SBIR) Application
    11. 11. Knowledge Synthesis Basic Science Application & Program Delivery Surveillance Intervention Research Reducing the Cancer Burden “Informatics in Action” Synthesis Health Informatics
    12. 12. Public Health Informatics • IT for improving cancer-related care and ultimately, cancer-related outcomes • 15+ years bench to bedside • Accelerate discovery & cognitive support • Bioinformatics: biology, genomics/proteomics • Imaging informatics: tissues and organs • Clinical informatics: whole organisms • Public Health Informatics: populations Cancer Causes Control (2006) 17:861–869
    13. 13. Public Health Informatics & Engineering PHI = the systematic application of information and computer science and technology to public health practice, research, and learning. A. Friede, H.L. Blum, and M. McDonald (1995) Public health informatics is primarily an engineering discipline, that is, a practical activity, undergirded by science, oriented to the accomplishment of specific tasks. J Public Health Management Practice, 2000, 6(6), 67–75
    14. 14. Behavioral Medicine and the Information Landscape Islands of datasets, documents, analytic tools, and research communities Peter Schad, 2008
    15. 15. Behavioral Data Field based data Public Health Surveillance National Surveillance Data (e.g., NHIS, BRFSS, NHANEs) Routine behavioral surveys (e.g., HINTS) Medical Research Settings Patient Charts Clinical trials data University Laboratories Individually published papers Locally maintained data sets Local Interventions
    16. 16. Behavioral Medicine and the Information Landscape - Overwhelming- Overwhelming volume of datavolume of data - Multitude of- Multitude of sources/levelssources/levels Slide source – Peter Schad, 2008
    17. 17. “The Petabyte Age: Sensors everywhere. Infinite storage. Clouds of processors. Our ability to capture, warehouse, and understand massive amounts of data is changing science, medicine, business, and technology. As our collection of facts and figures grows, so will the opportunity to find answers to fundamental questions. Because in the era of big data, more isn't just more. More is different. ” - Chris Anderson 06.23.08 The End of Science?
    18. 18. Enter Cyberinfrastructure . caBIG Grid
    19. 19. Expand the Scope of Discovery Visualization Decision Support Fusion Policy Planning Application Layer Users Pattern Detection Tools Users • Epidemiologists • Behavioral scientists • Public health planners • Geneticists…
    20. 20. Visualization Discovery Decision Support Fusion Policy Planning Application Layer Users Courtesy Ben Shneiderman, 2006; NCI Speaker Series Users • Applied/Basic scientists • Policy makers • State/City public health planners…
    21. 21. Decision Support & Policy Planning Discovery Visualization Fusion Application Layer Users Courtesy Katy Börner, 2006; NCI Speaker Series Users • Science directors • State health planners • Resource allocation • Clinicians Portfolio Analysis Tools
    22. 22. Connecting Stove-piped Data Discovery Visualization Decision Support Policy Planning Application Layer Users Advanced Analytic Tools Other Vocabularies Populomics Thesaurus University Research National Systems International Systems Psychometric Databases Users • Survey methodologists • Population scientists • Federal planners
    23. 23. ‘Populomics’ and the Grid Proteomics Nanotechnology Populomics Genomics Personalized Medicine: Pharmaco-genomics… Personalized Health Care: … Systems Integration, from cells to society + = Slide source – David Abrams, 2008
    24. 24. . • . caBIG® cancer Biomedical Informatics Grid caBIG® Goal A virtual web of interconnected data, individuals, and organizations that redefines how research is conducted, care is provided, and patients/participants interact with the biomedical enterprise. caBIG® Vision • Connect the cancer research community through a shareable, interoperable infrastructure • Deploy and extend standard rules and a common language to more easily share information • Build or adapt tools for collecting, analyzing, integrating and disseminating information associated with cancer research and care
    25. 25. . • . caBIG® Goal Clinical Research PathologyMolecular Biology Imaging • Track clinical trial registrations • Automatically capture clinical laboratory data • Manage reports describing adverse events during clinical trials • Combine proteomics, gene expression, and other basic research data • Submit and annotate microarray data • Integrate microarray data from multiple manufacturers for visualization and analysis • Utilize the National Cancer Imaging Archive repository for medical images including CT and MR images • Visualize images using DICOM-compliant tools • Annotate Images with distributed tools • Access a library of well characterized, clinically annotated biospecimens • Comprehensive inventory of a user’s own samples • Track the storage, distribution, and quality assurance of specimens Molecular MedicineMolecular Medicine caBIG® Capabilities Enable Discovery > Clinical Research > Clinical Care
    26. 26. . • . caBIG® Goal caGrid High-Level Architecture
    27. 27. . • . caBIG® Goal • caBIG® adoption is unfolding in: – 49 NCI-designated Cancer Centers – 16 NCI Community Cancer Centers • caBIG® being integrated into federal health architecture to connect National Health Information Network • Global Expansion – UK, China, India, Latin America caBIG® Cancer Center Deployment NCI-Designated Cancer Centers, Community Cancer Centers, and Community Oncology Programs
    28. 28. Take a Slice of the Cake Consortium in Abbas, A. (2004): Grid Computing: A Practical Guide to Technology and Applications. Hingham, MA: Charles Hingham (p. 319). Data Sources University Clinical Centers State & local consortia Federal Surveillance GridComm.Protocol Metadata,serviceregistry Discovery Visualization Decision Support Fusion Policy Planning Grid layer, Ontology Development Application Layer Application layer GRID infrastructure CDEs, vocabularies, metadata PopSciGrid 1.0 Behavioral Medicine
    29. 29. • Proof of concept for CI in population health and cancer control • Use state-of-the-science technology to link data, researchers, and resources: - Expert Panel Workshop (March ’08); caBIG® Annual Mtg (June’08); DCCPS Fall Forum (November ‘08); HICSS, SBM Noshir Contractor, PhD, Yun Huang, PhD, York Yao, MS Science of Networks in Communities (SONIC) Laboratory, Northwestern University PopSciGrid 1.0
    30. 30. Challenges…Not just technology & infrastructure • Collaboration – Within and across disciplines • Privacy, de-identification, and data ownership • Data Harmonization – Standardize data collection – Different national surveys, codebooks, and datasets – Different measures/instruments for same phenomena – Legacy datasets PopSciGrid 1.0 (cont.)
    31. 31.  Implement services on the Grid  HINTS, NHIS, and tobacco tax data  Basic statistics, categorical analysis, and prevalence analysis  Visualization by region  Demonstrate the power of the Grid  Publish data  Analyze data from multiple sources  Visualize data on the Grid Behavioral Medicine - Getting ‘Grid-ified’
    32. 32. • Prospective geo- spatial analytics PopSciGrid • 14 datasets spanning 6 years • Real-time access/analysis of public health and economic data • Potential links to GEM database • http://129.105.36.86/GridServer/c/index.html#
    33. 33. Web 2.0 / Science 2.0 • Architectures for Participation • Collective Intelligence • Data as the new “Intel Inside” Volume 22: No. 2, February 2008 Psychology and the Grid by Steven Breckler, Executive Director
    34. 34. Virtual Organizations & Interdisciplinary Science
    35. 35. Data Widgets State Cancer Profiles CISNet Decision Aids Application layer (e.g., Enhanced State Cancer Profiles; Dashboards, CDC Data Widgets) Common Vocabularies: Shared ontologies, common data elements PopSciGrid 2.0 DATA SOURCES caBIG® BIRN, NHIN PopSciGrid GRID Middle Ware (Globus toolkit, XMi, security layer, discovery mechanisms) Public Surveillance • NHIS • BRFSS • HINTS • Tax, Census,... Grantees • CECCRS • TREC • TTURCS • CPHHD • GEI Clinical/Health System • CRN • QCCC projects • PopSci SIG • Registries (SEER) Mobile/Remote Sensing • Behavioral data • Environmental data • GIS • RTDC Biomedical • Biological • Genomic/proteomic
    36. 36. (Grid Enabled Measures) Database PopSciGrid 2.0 – Priming the Pump A grid-enabled, interoperable, dynamic website for behavioral and social science theoretical constructs and measures Program Lead: Rick Moser, PhD (Behavioral Research Program, DCCPS)
    37. 37. GEI: Genes, Environment, and Health Initiative • NIH-wide, led by NHGRI and NIEHS • Goal: to accelerate understanding of the genetic and environmental contributions to health and disease • 2007-2011, $46.5 million – 30 environmental technology projects – 8 genome-wide association studies – 2 genotyping centers and coordinating center Beyond Behavioral Medicine: GEI Program Leads: Jill Reedy (NCI), Amy Subar (NCI), Catherine Loria (NHLBI)
    38. 38. – . GEI – Genes and Environment EXPOSURE BIOLOGY PROGRAM Identify genetic variants GENETICS PROGRAM GXE Develop technology and biomarkers • GWA Studies • Data Analysis • Replication • Sequencing • Database • Function • Translation • Diet and Physical Activity • Psychosocial Stress and Addictive Substances • Chemical Sensors • Biological Response Indicators Program Leads: Jill Reedy (NCI), Amy Subar (NCI), Catherine Loria (NHLBI)
    39. 39. . GEI - Timeline FY07 FY08 FY09 FY10 FY11 U01 U54 Environmental Sensors • Diet/Physical Activity (NCI/NHLBI) • Psychosocial Stress/Addictive Substances (NIDA) • Chemical Sensors (NIEHS) U01 U01 U01 DEVICES APPLICATION GWA Biological Response • Biomarkers (NIEHS) FINGERPRINTS U01 U54 DEVICESCenters – biomarkers/biosensors
    40. 40. . GEI – Technology (cont.) Innovative technologies to measure diet, PA, stress, addictive substances, chemical sensors, biological response indicators 5 use cell phones to capture and/or transmit data 3 combine accelerometers with physiologic sensors (e.g., heart rate) to improve estimates of energy expenditure 3 pair camera/video/audio components with automated processing (e.g., image detection, voice recognition) 2 use GPS coordinates to track location of activities 1 uses web-based multimedia software as a tool for reporting diet among children Program Leads: Jill Reedy (NCI), Amy Subar (NCI), Catherine Loria (NHLBI)
    41. 41. GEI – PALMS (Physical Activity Location Measurement System) PI: Kevin Patrick, University of California San Diego
    42. 42. . Looking Ahead: Institute for the Future Mike Liebhold (2008); www.IFTF.org
    43. 43. Talk is cheap… American Recovery and Reinvestment Act 2009: http://www.cancer.gov/recovery NIH Challenge Grants in Health and Science Research (RC1) http://grants.nih.gov/grants/guide/rfa-files/RFA-OD-09-003.html Application Due Date: 4/27/2009 Peer Review Date: 6-7/2009 Council Review Date: 8/2009 Anticipated Start Date: 9/30/2009
    44. 44. Funding – Challenge Grants (10) Information Technology for Processing Health Care Data 10-CA-101* Cyber-Infrastructure for Health: Building Technologies to Support Data Coordination and Computational Thinking. The National Science Foundation has identified research based on “cyberinfrastructure” as the single most important challenge confronting the nation’s science laboratories (http://www.nsf.gov/news/special_reports/cyber/index.jsp). The challenge is based on a “grand convergence” of three trends: (a) maturation of the Internet as connective data technology; (b) ubiquity of microchips in computers, appliances, and sensors; and (c) an explosion of data from the research enterprise. The NIH, for example, has invested millions within its Genes, Environment, and Health Initiative (GEI) to develop new technologies for measuring environmental exposure to accompany the millions already spent on data from Genome Wide Association studies. The DHHS is spending millions to catalyze the deployment of interoperable electronic health records as a springboard for research (i.e., in the “learning health system”). Relatively little has been spent on accommodating the petabytes (i.e., 10 15 bytes of data) of data expected from these investments. What is needed is a focused concentration of resources to stimulate the creation of new technologies to accommodate these data and accelerate knowledge discovery through computational means. Such a stimulus should help bootstrap a new sector of the knowledge economy, one that is dedicated to accelerating the pace by which data are turned into population health. Contact: Dr. Bradford Hesse, 301-594-9904, hesseb@mail.nih.gov NIH Challenge Grants in Health and Science Research (RC1) http://grants.nih.gov/grants/guide/rfa-files/RFA-OD-09-003.html
    45. 45. Funding – Challenge Grants (cont.) (10) Information Technology for Processing Health Care Data 10-EB-101 Engineering improved quality of health care at a reduced cost. 10-HL-101 Develop data sharing and analytic approaches to obtain from large-scale observational data, especially those derived from electronic health records, reliable estimates of comparative treatment effects and outcomes of cardiovascular, lung, and blood diseases . 10-LM-101 Informatics for post-marketing surveillance. 10-LM-102 Advanced decision support for complex clinical decisions. 10-OD-101 Adapt existing genetic and clinical databases to make them interoperable for pharmacogenomics studies. 10-RR-101 Information Technology Demonstration Projects Facilitating Secondary Use of Healthcare Data for Research NIH Challenge Grants in Health and Science Research (RC1) http://grants.nih.gov/grants/guide/rfa-files/RFA-OD-09-003.html
    46. 46. Funding – SBIR / STTR SBIR (Small Business Innovation Research): PI from small company (51%) with academic consultants STTR (Small Business Technology Transfer Research): PI from non-profit org. working with small businesses Established to promote collaborations between small businesses and non-profit organizations for the purpose of developing science-based commercially viable products that help meet the goals of different federal agencies. Phase I: $100k, 6-12 months; feasibility, pilot/prototype Phase II: $750k 2-4 years; implementation & evaluation (moving to Phase I: $300,000; Phase II: up to $2.2 million)
    47. 47. Funding – SBIR / STTR Products Examples: cancer-related PC software; interactive DVDs; wireless devices; web/TV/radio programs; videos or PSAs; EHR apps. Target Audience: cancer survivors and their families, decision making tools; educational and/or training tools; devices that improve health behaviors or change lifestyle habits; and screening, assessment, or management programs. NCI DCCPS SBIR/STTR Program Advisor Connie Dresser, RDPH, LN HCIRB, BRP, DCCPS, NCI 301-435-2846, cd34b@nih.gov www.cancercontrol.cancer.gov/hcirb/sbir
    48. 48. . Acknowledgements NCI – Rick Moser – Glen Morgan – Erik Augustson – Frank White – Jill Reedy (GEI) – Amy Subar (GEI) NHLBI – Catherine Loria (GEI) BAH – Paul Courtney Northwestern University – Noshir Contractor – Yun Huang – York Yao
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