The document discusses how new technologies and large datasets ("big data") can accelerate medical research and discovery. Specifically, it notes that traditional medical research can take 6 years from hypothesis to publication, while genetics initiatives using big data were able to present findings in just 8 months. The document also discusses how crowdsourcing health data through platforms that engage participants can help harness collective intelligence to power personalized medicine approaches.

1. POPULOMICS AND PERSONALIZED
MEDICINE
Bradford W. Hesse, PhD
Chief, NCI Health Comm. and Informatics Research
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2. Wired Magazine, July 2010
•Invested $50 million in
Parkinson’s research
•Started public blog on
LRRK2
•Is leveraging the power of
“big data” to accelerate
the science
•Helped found, with wife
Anne Wojcicki, 23andMe
Friday, March 14, 14

3. Data-Intensive Research Paradigm
to Speed Discovery?
Traditional Model
•Hypothesis
•Studies
•Data aggregation
•Analysis
•Writing
•Submission
•Acceptance (NEJM)
•Publication
Time Elapsed: 6 years
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4. Data-Intensive Research Paradigm
to Speed Discovery?
Genetics Initiative
•Tool construction
•Recruitment
•Analysis
•Presentation
Time Elapsed: 8 months
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5. A Fourth Paradigm in Science?
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6. A New Era in Health?
:,
DPE,.LJ
IERLTH
mAP OF DISIUPTILsE IWWDIIRTIORJ
The Institute for the Future’s Health Horizons Program has
developed a new paradigm for disruptive innovation in
the global health economy—we call it “Open Health.” This
paradigm leverages the concepts and successes of open
innovation and open-source software, and applies them
to the world of health. Open Health strategies will redefine
the research and development process and will require a
radically new way of thinking about innovation systems, the
institutional culture of firms, partnerships, and collaborations,
and the very meaning of health itself. The implications of
Open Health are relevant to all stakeholders in the global
health economy, be they beauty, food, consumer electronics,
biopharma, health care, or medical technology companies.
As it diffuses across industries, Open Health will inspire new
approaches to meeting significant global health problems,
and it will provide a framework for generating and sustaining
new business models of tomorrow.
We have identified ten core principles that serve as a
foundation for implementing Open Health strategies. This map
presents these principles in the context of emerging trends
and innovation leaders. It describes the external forces that are
driving Open Health and emphasizes the networks and culture,
the ethos and skills, the business models and strategies, and
the tools and platforms that will shape innovation systems in
the global health economy over the next decade.
The Open Health Map of Disruptive Innovation (SR-1117A)
is your guide to putting Open Health into practice. Use it,
along with its companion piece, the Open Health Toolkit: A
Framework for Innovation (SR-1117B), to build the capacity to
innovate to solve health’s pressing problems.
A Fourth Paradigm in Science?
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7. A Fourth Paradigm in Science?
Molecules to Populations
Genomics
Nanotechnology
ProteomicsPopulomics
Forman MR, Greene SM, Avis NE, et al. Bioinformatics: Tools to accelerate population science and disease control
research. Am J Prev Med. Jun 2010;38(6):646-651.
Friday, March 14, 14

8. Platforms for participation
Harnessing collective
intelligence
Data as the new “Intel
Inside”
End of the software
release cycle
Lightweight programming
models
Software above the level
of the single device
Rich user experience
From Web 2.0 to Health 2.0
*Source: http://oreilly.com/web2/archive/what-is-web-20.html
Facets of Web 2.0*
Friday, March 14, 14

9. Platforms for participation
Harnessing collective
intelligence
Data as the new “Intel
Inside”
End of the software
release cycle
Lightweight programming
models
Software above the level
of the single device
Rich user experience
From Web 2.0 to Health 2.0
*Source: http://oreilly.com/web2/archive/what-is-web-20.html
Facets of Web 2.0*Facets of Web 2.0*
Platforms for participation
Harnessing collective
intelligence
Data as the new “Intel
Inside”
End of the software
release cycle
Lightweight programming
models
Software above the level
of the single device
Rich user experience
Friday, March 14, 14

10. Hesse B, W. , Sproull L, Kiesler SB, Walsh JP. Returns to science: computer networks in oceanography. Commun.
ACM. 1993;36(8):90-101.
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11. Hesse B, W. , Sproull L, Kiesler SB, Walsh JP. Returns to science: computer networks in oceanography. Commun.
ACM. 1993;36(8):90-101.
Friday, March 14, 14

12. Hesse B, W. , Sproull L, Kiesler SB, Walsh JP. Returns to science: computer networks in oceanography. Commun.
ACM. 1993;36(8):90-101.
Friday, March 14, 14

13. Hesse B, W. , Sproull L, Kiesler SB, Walsh JP. Returns to science: computer networks in oceanography. Commun.
ACM. 1993;36(8):90-101.
• Data Liquidity
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14. Hesse, B. W., Hansen, D., Finholt, T., Munson, S., Kellogg, W., & Thomas, J. C. (2010). Social Participation in Health
2.0. IEEE Computer, 43(12), 45-52.
• Platforms for Participation
• Harnessing Collective Intelligence
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15. Hesse, B. W., Hansen, D., Finholt, T., Munson, S., Kellogg, W., & Thomas, J. C. (2010). Social Participation in Health
2.0. IEEE Computer, 43(12), 45-52.
• Platforms for Participation
• Harnessing Collective Intelligence
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16. • Software above single device
Hesse BW. Enhancing Consumer Involvement in Health Care. In: Parker JC, Thornson E, eds. Health
Communication in the New Media Landscape. New York, NY: Springer Publishing Company; 2008:119-149.
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17. Hesse BW. Time to reboot: resetting health care to support tobacco dependency treatment services. Am J Prev Med.
Dec 2010;39(6 Suppl 1):S85-87.
ClearPractice, Inc. • Rich User Experience
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18. The Learning Healthcare System
Hesse BW, Woods SS, Ahern DK. Nudging Best Practice: The HITECH Act and Behavioral Medicine. Translational
Behavioral Medicine. 2011;1(1):175-181.
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19. The Learning Healthcare System
Hesse BW, Woods SS, Ahern DK. Nudging Best Practice: The HITECH Act and Behavioral Medicine. Translational
Behavioral Medicine. 2011;1(1):175-181.
Friday, March 14, 14

20. The Learning Healthcare System
Hesse BW, Woods SS, Ahern DK. Nudging Best Practice: The HITECH Act and Behavioral Medicine. Translational
Behavioral Medicine. 2011;1(1):175-181.
Friday, March 14, 14

21. Hesse BW. Public Health Informatics. In: Gibbons MC, editor. eHealth Solutions for Healthcare
Disparities. NewYork, NY: Springer; 2007. p. 109-129.
Data-informed Personalized Medicine
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22. Stay Tuned!
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