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From Research to Practice - New Models for Data-sharing and Collaboration to Improve Health and Healthcare

Watch the webinar here: http://encore.meetingbridge.com/MB005418/140528/
Webinar transcript: http://hdc.membershipsoftware.org/Files/webinars/HDC-PwC%20NIH%20&%20PCORI%20Webinar%20Transcript%205_28_14.pdf

Patient-Centered Outcomes Research Institute (PCORI) Executive Director Joe Selby, MD, MPH; National Institutes of Health (NIH) Director and PCORI Board of Governors member Francis Collins, MD, PhD; and NIH Associate Director for Data Science Philip Bourne, PhD discussed new and emerging trends in big data for health, including:

- How researchers, patients, clinicians, and others are forging new models for data-sharing.
- Leveraging the quantity, variety, and analytic potential of health-related data for research and practice.
- Addressing patients’ perspectives, needs, and concerns in creating new opportunities for innovation and translational science.
- Exciting initiatives such as PCORnet, the National Patient-Centered Clinical Research Network initiative that PCORI is now helping to develop, and related open data and technology efforts such - as the NIH Health Systems Collaboratory and Big Data to Knowledge (BD2K) initiative.

Discover more health data resources on our website at http://www.healthdataconsortium.org/

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From Research to Practice - New Models for Data-sharing and Collaboration to Improve Health and Healthcare

  1. 1. From Research to Practice: New Models for Data-sharing and Collaboration to Improve Health and Healthcare Joe Selby, MD, MPH, Executive Director, PCORI Francis Collins, MD, PhD, Director, National Institutes of Health Philip Bourne, PhD, Associate Director for Data Science, NIH Moderator: Dwayne Spradlin, CEO Health Data Consortium May 28, 2014
  2. 2. Presenters and Moderator Joe Selby, MD, MPH Executive Director PCORI Francis Collins, MD, PhD Director NIH Philip Bourne, PhD Associate Director for Data Science NIH Dwayne Spradlin CEO Health Data Consortium
  3. 3. Agenda Time Agenda Item 1:00 – 1:10 p.m. Welcome 1:10 – 1:20 p.m. Dr. Joe Selby, Executive Director, PCORI 1:20 – 1:30 p.m. Dr. Francis Collins, Director, NIH 1:30 – 1:40 p.m. Dr. Philip Bourne, Associate Director for Data Science, NIH 1:40 – 1:55 p.m. Question and Answer Session 1:55 – 2:00 p.m. Wrap Up and Conclusion
  4. 4. 4 1. Click in the Q&A box on the right side of your screen, type your question into the dialog box, click Send button 2. You can also submit questions via twitter at @hdconsortium Questions may be submitted at any time Reminder: for audio, Dial 866-640-4044 - Entry Code: 416641# Need help? Press *0 on phone to reach the operator
  5. 5. Joe Selby, MD, MPH Executive Director PCORI
  6. 6. Joe Selby, MD MPH, Executive Director PCORI PCORnet: Harnessing Real-World Health Data in Patient-Centered Research
  7. 7. PCORI’s Mission PCORI helps people make informed health care decisions, and improves health care delivery and outcomes, by producing and promoting high integrity, evidence-based information that comes from research guided by patients, caregivers and the broader health care community.
  8. 8. Influence Research Funded by Others Speed the Implementation and Use of Evidence Increase Quantity, Quality and Timeliness of Research Information PCORI’s Strategic Goals…
  9. 9. …Set the Stage for PCORNet Improve the nation’s capacity to conduct clinical research more efficiently, by creating a large, highly representative, national patient-centered clinical research network with a focus on conducting CER – both randomized and observational. Support a learning US healthcare system, which would allow for large-scale research to be conducted with enhanced accuracy and efficiency within real-world care delivery systems.
  10. 10. 10 PCORnet – Toward a Learning Healthcare System
  11. 11. Geographic Coverage of PPRNs and CDRNs
  12. 12. PCORnet Goals for Phase I Each CDRN will have a defined set of standardized clinical data that is fully inter-operable with data from other CDRNs; each PPRN will also have a standard database with varying amounts of clinical and patient-generated data. PCORnet will have clear policies on decision-making, uses of data, collaboration and knowledge sharing, data sharing, data privacy and security Within each participating CDRN, patients, clinicians and health systems will be actively engaged in governance and use of the network and its data Both CDRNs and PPRNs will have capacity to participate in both large observational studies and pragmatic (simple) randomized clinical trials Networks will demonstrate a readiness to collaborate with researchers from outside PCORnet By 18 Months:
  13. 13. Francis Collins, MD, PhD Director NIH
  14. 14. NIH: Data Sharing Challenges and Solutions Francis S. Collins, M.D., Ph.D. Director, National Institutes of Health From Research to Practice: New Models for Data Sharing and Collaboration to Improve Health and Healthcare May 28, 2014
  15. 15. Value of Data Sharing  Increases return on investment  Facilitates additional research  Helps to validate findings  Promotes transparency  Many ongoing efforts to increase and facilitate data sharing – Big Data to Knowledge (BD2K) – Plan for increasing public access to data
  16. 16. Explosion of Big Data By Daily Users of NCBI 0 1 2 3 4 5 Users(Millions) Daily Page Views: 28 Million Daily Users: ~4 Million Daily Downloads: 35 Terabytes Peak Hits: 7000 Per Second
  17. 17. Data Sharing Challenges and Solutions  Genomic Data Sharing  Clinical Data Sharing  Human Subjects Protection
  18. 18. Data Sharing Challenges and Solutions  Genomic Data Sharing  Clinical Data Sharing  Human Subjects Protection
  19. 19. $1,000 $10,000 $100,000 $1,000,000 $10,000,000 $100,000,000 S-01 J-02 M-02 S-02 J-03 M-03 S-03 J-04 M-04 S-04 J-05 M-05 S-05 J-06 M-06 S-06 J-07 M-07 S-07 J-08 M-08 S-08 J-09 M-09 S-09 J-10 M-10 S-10 J-11 M-11 S-11 J-12 M-12 S-12 J-13 M-13 S-13 J-14 Cost of Sequencing a Human Genome September 2001–January 2014 4,008
  20. 20. NIH Genomic Data Sharing (GDS) Policy  Expands expectations to share genomic data under the current NIH Genome-Wide Association Studies (GWAS) Policy to large-scale non- human and human genomic data  Ensures the broad, responsible sharing of genomic research data – Responsibilities of investigators submitting data • Provide data sharing plan to NIH with grant application • Submit data in a timely manner • For human data, obtain consent for data to be used for future research purposes and shared broadly and submit Institutional Certification – Responsibilities of investigators accessing and using data • Terms and conditions for research use of controlled-access data • Conditions for use of unrestricted-access data  Final will be implemented in January 2015
  21. 21. More to come? Genomic Sequencing in the Clinic  Authorized Platform: llumina’s MiSeqDx  FDA cleared two CF tests that use the Illumina platform – Panel of 139 mutations – Sequencing assay  Paves the way for more genomic technologies to gain regulatory clearance  Will allow for the development and use of new genome-based tests MiSeq Benchtop Sequencer (Credit: Illumina)
  22. 22. Data-sharing Challenges and Solutions  Genomic Data Sharing  Clinical Data Sharing  Human Subjects Protection
  23. 23. Source: BMJ 2012;344:d7292. Publication of Clinical Trial Results  NIH-Funded trials published within 100 months of completion  Less than 50% are published within 30 months of completion
  24. 24. Publication of Clinical Trial Results NHLBI Clinical Trial Data: Time to Publication by End Point Gordon, et al. N Engl J Med 2013; 369(20): 1926-34
  25. 25. ClinicalTrials.gov: Public Benefits  Enhance patient access to enrollment in clinical trials  Prevent unnecessary or unwitting duplication of trials, especially those found to be unsafe  Honor ethical obligation to participants (results inform science)  Mitigate bias (non publication of negative results)  Inform future research and funding decisions  Increase access to data about marketed products  Facilitate use of findings to improve health All contribute to public trust in clinical research
  26. 26. Data Sharing Challenges and Solutions  Genomic Data Sharing  Clinical Data Sharing  Human Subjects Protection
  27. 27. Revisions to the Common Rule Rationale for the reforms: human subjects research is changing  Growth in research volume  Increase in multi-site studies  Increase in health services and social science research  New technologies: e.g., genomics, imaging, informatics  Increased role of private sector  Increased sharing of specimens and data The nature and volume of potential research data is one key rationale for reforms
  28. 28. Common Rule Reforms – July 2011 ANPRM Enhancing Protections  Require consent for research with biospecimens/data  Enhance data security and information protection standards  Extend protections to all research conducted at federally-funded institutions Reducing Burden  Promote use of broad consent for future research with biospecimens/data  Broaden exemptions for low risk research  Eliminate redundant IRB reviews and reduce impact of IRB reviews
  29. 29. NIH… Turning Discovery Into Health
  30. 30. Philip Bourne, PhD Associate Director for Data Science NIH
  31. 31. Towards the NIH as a Digital Enterprise Philip E. Bourne, Ph.D. Associate Director for Data Science, National Institutes of Health From Research to Practice: New Models for Data Sharing and Collaboration to Improve Health and Healthcare May 28, 2014
  32. 32. Some Observations  Good News – Data sharing offers unprecedented opportunities to improve healthcare – We have a plan – We are beginning to quantify the issues – We have some of the best data scientists in the world to work on the problems
  33. 33. Some Observations  Bad News – Sustainability will not be possible without change – OSTP have defined the why but not the how – We do not know how the data we currently have are used – It is difficult to estimate supply and demand  Good News – Data sharing offers unprecedented opportunities to improve healthcare – We have a plan – We are beginning to quantify the issues – We have some of the best data scientists in the world to work on the problems
  34. 34. We have identified 5 programmatic themes and associated deliverables …
  35. 35. Associate Director for Data Science Commons Training Center BD2K Modified Review Sustainability Education Innovation Process • Cloud – Data & Compute • Search • Security • Reproducibility Standards • App Store • Coordinate • Hands-on • Syllabus • MOOCs • Community • Centers • Training Grants • Catalogs • Standards • Analysis • Data Resource Support • Metrics • Best Practices • Evaluation • Portfolio Analysis The Biomedical Research Digital Enterprise Communication Collaboration Programmatic Theme Deliverable Example Features • IC’s • To Researchers • Federal Agencies • International Partners • Computer Scientists Scientific Data Council External Advisory Board
  36. 36. Associate Director for Data Science Commons Training Center BD2K Modified Review Sustainability Education Innovation Process • Cloud – Data & Compute • Search • Security • Reproducibility Standards • App Store • Coordinate • Hands-on • Syllabus • MOOCs • Community • Centers • Training Grants • Catalogs • Standards • Analysis • Data Resource Support • Metrics • Best Practices • Evaluation • Portfolio Analysis The Biomedical Research Digital Enterprise Communication Collaboration Programmatic Theme Deliverable Example Features • IC’s • To Researchers • Federal Agencies • International Partners • Computer Scientists Scientific Data Council External Advisory Board
  37. 37. Associate Director for Data Science Commons Training Center BD2K Modified Review Sustainability Education Innovation Process • Cloud – Data & Compute • Search • Security • Reproducibility Standards • App Store • Coordinate • Hands-on • Syllabus • MOOCs • Community • Centers • Training Grants • Catalogs • Standards • Analysis • Data Resource Support • Metrics • Best Practices • Evaluation • Portfolio Analysis The Biomedical Research Digital Enterprise Communication Collaboration Programmatic Theme Deliverable Example Features • IC’s • To Researchers • Federal Agencies • International Partners • Computer Scientists Scientific Data Council External Advisory Board
  38. 38. The Power of the Commons Commons == Extramural NCBI == Research Object Sandbox == Collaboratory
  39. 39. The Power of the Commons Data Commons == Extramural NCBI == Research Object Sandbox == Collaboratory
  40. 40. The Power of the Commons Data The Why: Data Sharing Plans Commons == Extramural NCBI == Research Object Sandbox == Collaboratory
  41. 41. The Power of the Commons Data The Why: Data Sharing Plans The How: Commons == Extramural NCBI == Research Object Sandbox == Collaboratory
  42. 42. The Power of the Commons Data The Why: Data Sharing Plans The How: Commons == Extramural NCBI == Research Object Sandbox == Collaboratory The End Game:
  43. 43. The Power of the Commons Data The Long Tail Core Facilities/HS Centers Clinical /Patient The Why: Data Sharing Plans The How: Commons == Extramural NCBI == Research Object Sandbox == Collaboratory The End Game:
  44. 44. The Power of the Commons Data The Long Tail Core Facilities/HS Centers Clinical /Patient The Why: Data Sharing Plans The How: Sustainable Storage Quality Scientific Discovery Usability Security/ Privacy Commons == Extramural NCBI == Research Object Sandbox == Collaboratory The End Game: Knowledge Metrics/ Standards
  45. 45. The Power of the Commons Data The Long Tail Core Facilities/HS Centers Clinical /Patient The Why: Data Sharing Plans The How: Sustainable Storage Quality Scientific Discovery Usability Security/ Privacy Commons == Extramural NCBI == Research Object Sandbox == Collaboratory The End Game: KnowledgeNIH Awardees Metrics/ Standards
  46. 46. The Power of the Commons Data The Long Tail Core Facilities/HS Centers Clinical /Patient The Why: Data Sharing Plans Government The How: Sustainable Storage Quality Scientific Discovery Usability Security/ Privacy Commons == Extramural NCBI == Research Object Sandbox == Collaboratory The End Game: KnowledgeNIH Awardees Private Sector Metrics/ Standards Rest of Academia
  47. 47. The Power of the Commons Data The Long Tail Core Facilities/HS Centers Clinical /Patient The Why: Data Sharing Plans Government The How: Data Discovery Index Sustainable Storage Quality Scientific Discovery Usability Security/ Privacy Commons == Extramural NCBI == Research Object Sandbox == Collaboratory The End Game: KnowledgeNIH Awardees Private Sector Metrics/ Standards Rest of Academia Software Standards Index BD2K Centers
  48. 48. The Power of the Commons Data The Long Tail Core Facilities/HS Centers Clinical /Patient The Why: Data Sharing Plans The Commons Government The How: Data Discovery Index Sustainable Storage Quality Scientific Discovery Usability Security/ Privacy Commons == Extramural NCBI == Research Object Sandbox == Collaboratory The End Game: KnowledgeNIH Awardees Private Sector Metrics/ Standards Rest of Academia Software Standards Index BD2K Centers
  49. 49. The Power of the Commons Data The Long Tail Core Facilities/HS Centers Clinical /Patient The Why: Data Sharing Plans The Commons Government The How: Data Discovery Index Sustainable Storage Quality Scientific Discovery Usability Security/ Privacy Commons == Extramural NCBI == Research Object Sandbox == Collaboratory The End Game: KnowledgeNIH Awardees Private Sector Metrics/ Standards Rest of Academia Software Standards Index BD2K Centers Cloud, Research Objects, Business Models
  50. 50. What Will the Commons Accomplish?  Community Building - support sharing, accessibility, and discoverability of biomedical data and analytical tools  Enable Innovation - data resources co-located with advanced computing resources  Provide cost effectiveness – through economies of scale, new business models, including public private partnerships  Provide opportunities for interagency and international cooperation
  51. 51. BD2K will Empower the Commons  Data discovery index  Data/metadata standards  Software index and software development  Training centers and grants  Centers engaged in advanced biomedical data science for the community at large
  52. 52. NIH… Turning Discovery Into Health
  53. 53. Q&A
  54. 54. 55 To submit a question: 1. Click in the Q&A box on the right side of your screen, type your question into the dialog box, click Send button 2. You can also submit questions via twitter at @hdconsortium Questions may be submitted at any time
  55. 55. Thank you!

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  • AbeerShalaby1

    Sep. 15, 2018

Watch the webinar here: http://encore.meetingbridge.com/MB005418/140528/ Webinar transcript: http://hdc.membershipsoftware.org/Files/webinars/HDC-PwC%20NIH%20&%20PCORI%20Webinar%20Transcript%205_28_14.pdf Patient-Centered Outcomes Research Institute (PCORI) Executive Director Joe Selby, MD, MPH; National Institutes of Health (NIH) Director and PCORI Board of Governors member Francis Collins, MD, PhD; and NIH Associate Director for Data Science Philip Bourne, PhD discussed new and emerging trends in big data for health, including: - How researchers, patients, clinicians, and others are forging new models for data-sharing. - Leveraging the quantity, variety, and analytic potential of health-related data for research and practice. - Addressing patients’ perspectives, needs, and concerns in creating new opportunities for innovation and translational science. - Exciting initiatives such as PCORnet, the National Patient-Centered Clinical Research Network initiative that PCORI is now helping to develop, and related open data and technology efforts such - as the NIH Health Systems Collaboratory and Big Data to Knowledge (BD2K) initiative. Discover more health data resources on our website at http://www.healthdataconsortium.org/

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