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The CIELO Project: Towards a
Research Analytics Commons
Philip R.O. Payne, PhD, FACMI
Professor and Chair, College of Medi...
Overview
1) Background and Motivation
2) Conceptualization of CIELO
3) Functional and Technical Architecture
4) Future Dir...
Overview
1) Background and Motivation
2) Conceptualization of CIELO
3) Functional and Technical Architecture
4) Future Dir...
Critical Dimensions of a Learning Healthcare System:
Systems Thinking Applied to Patient Centered Research
4
Environment
a...
The Need for Reproducible Research
 Creating a high performing healthcare research and
delivery system requires both econ...
Why is it Hard to Reproduce Research?
 Data sharing alone is insufficient to this task
 How was data pre-processed?
 Wh...
A Community Dialogue
7
BD2K and the Vision for a Research Commons
 Phil Bourne’s Vision (Associate Director for Data
Science, NIH)
 “To foster ...
9
Source: Phil Bourne, “Ask Not What the NIH Can Do For You; Ask What You Can Do For The NIH”
Overview
1) Background and Motivation
2) Conceptualization of CIELO
3) Functional and Technical Architecture
4) Future Dir...
Translating a Problem into a Solution:
The Problem Definition Process
11
Establish the
Need for a
Solution
Justify the Nee...
CIELO: Enabling Collaborative Data
Analytics in Patient-Centered Research
 Project Goals:
1) Provide members of the resea...
Process To-Date for CIELO
13
Ideation
MVP
Development
Stakeholder
Review and
Requirements
Gathering
MVP Re-
Engineering
Pr...
CIELHO Conceptual Model
14
Overview
1) Background and Motivation
2) Conceptualization of CIELO
3) Functional and Technical Architecture
4) Future Dir...
Building on Existing Tools and Approaches
16
Sharing of
Technical
Artifacts
Social
Networking
Metadata
Github/Gitlab
Activ...
CIELHO Workflow Model
17
Community-Defined Requirements
 Integration with analogous platforms and tools
 Ex. Sage Bionetworks Synapse
 Incorpora...
Community-Defined Requirements:
Focus for Public Beta
 Integration with analogous platforms and tools
 Ex. Sage Bionetwo...
How Will We Evaluate the CIEHLO?
20
Overview
1) Background and Motivation
2) Conceptualization of CIELO
3) Functional and Technical Architecture
4) Future Dir...
Future Directions: Shared Execution Environment (VAULT)
22
Overview
1) Background and Motivation
2) Conceptualization of CIELO
3) Functional and Technical Architecture
4) Future Dir...
Meeting Objectives (1)
 Provide a cross-section of stakeholders with a review
of current technical functionality and desi...
Meeting Objectives (2)
 Identify socio-cultural barriers and opportunities as
they relate to creating and sustaining a CI...
Meeting Deliverables (1)
 Stakeholder verification/validation of current CIELO
functionality;
 An enumeration and priori...
Meeting Deliverables (2)
 An enumeration of communication, advocacy, and
funding targets intended to position CIELO as a
...
Overview
1) Background and Motivation
2) Conceptualization of CIELO
3) Functional and Technical Architecture
4) Future Dir...
29
“Information liberation + new incentives = rocket fuel for
innovation”
– Aneesh Chopra (The Advisory Board Company)
Phi...
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The Cielo Project: Towards a Research Analytics Commons

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The Cielo Project: Towards a Research Analytics Commons

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Transcript of "The Cielo Project: Towards a Research Analytics Commons"

  1. 1. The CIELO Project: Towards a Research Analytics Commons Philip R.O. Payne, PhD, FACMI Professor and Chair, College of Medicine, Department of Biomedical Informatics Professor, College of Public Health, Division of Health Services Management and Policy Associate Director for Data Sciences, Center for Clinical and Translational Science Executive-in-residence, Office of Technology Commercialization and Knowledge Transfer
  2. 2. Overview 1) Background and Motivation 2) Conceptualization of CIELO 3) Functional and Technical Architecture 4) Future Directions 5) Today’s Objectives 6) Discussion 2
  3. 3. Overview 1) Background and Motivation 2) Conceptualization of CIELO 3) Functional and Technical Architecture 4) Future Directions 5) Today’s Objectives 6) Discussion 3
  4. 4. Critical Dimensions of a Learning Healthcare System: Systems Thinking Applied to Patient Centered Research 4 Environment and Culture • Instrumenting the clinical environment • Generating hypotheses • Creating a culture of science and innovation Precision Medicine • Rapid evidence generation cycle(s) • ‘omics’ • Analytics/decision support Data Science • System-level analyses • Data science • Visualization • Reproducible analytics Integrated and High Performing Healthcare Research and Delivery Systems Learning from every patient encounter Leveraging the best science to improve care Identifying and solving complex problems Rapid Translation Our Focus!
  5. 5. The Need for Reproducible Research  Creating a high performing healthcare research and delivery system requires both economies of scale and increased efficiencies  Timeliness  Resource utilization  Data “liquidity”  Central to this argument is a need to exchange research findings and evidence between and among stakeholders in a consumable manner  Design  Data  Analysis  Doing so allows for reproducible research with cumulative benefits 5
  6. 6. Why is it Hard to Reproduce Research?  Data sharing alone is insufficient to this task  How was data pre-processed?  What analytical workflows were utilized?  What additional parameters influenced data analysis?  How were results “packaged” for dissemination?  Many socio-technical barriers to addressing these questions, including:  Intellectual property and data-level concerns  Availability of technology platforms/tools  Documentation  Metadata  Standards  Many, many other issues… 6
  7. 7. A Community Dialogue 7
  8. 8. BD2K and the Vision for a Research Commons  Phil Bourne’s Vision (Associate Director for Data Science, NIH)  “To foster an ecosystem that enables biomedical research to be conducted as a digital enterprise that enhances health, lengthens life and reduces illness and disability”  Creation of a commons providing for:  Cloud infrastructure for data and computing  Search  Security  Reproducibility standards  App store 8 Source: Phil Bourne, “Ask Not What the NIH Can Do For You; Ask What You Can Do For The NIH”
  9. 9. 9 Source: Phil Bourne, “Ask Not What the NIH Can Do For You; Ask What You Can Do For The NIH”
  10. 10. Overview 1) Background and Motivation 2) Conceptualization of CIELO 3) Functional and Technical Architecture 4) Future Directions 5) Today’s Objectives 6) Discussion 10
  11. 11. Translating a Problem into a Solution: The Problem Definition Process 11 Establish the Need for a Solution Justify the Need Contextualize the Problem Write the Problem Statement Adapted from: Spradlin, “Are You Solving The Right Problem?”, HBR, September 2012
  12. 12. CIELO: Enabling Collaborative Data Analytics in Patient-Centered Research  Project Goals: 1) Provide members of the research community with access to an open-source/-standards “app store” for data analysis and software sharing 2) Reduce time and cost of research while enhancing the reproducibility and transparency of data analysis. 3) Evolve and meet emerging community needs 12 Blue-Sky: not grounded in the realities of the present: visionary <blue–sky thinking> (Merriam Webster Dictionary)
  13. 13. Process To-Date for CIELO 13 Ideation MVP Development Stakeholder Review and Requirements Gathering MVP Re- Engineering Process and Outcomes Measure Team formation and proposal development CIEHLO Prototype Review and Feedback from Stakeholders Iterative User-Centered Design You Are Here! Contextualizes
  14. 14. CIELHO Conceptual Model 14
  15. 15. Overview 1) Background and Motivation 2) Conceptualization of CIELO 3) Functional and Technical Architecture 4) Future Directions 5) Today’s Objectives 6) Discussion 15
  16. 16. Building on Existing Tools and Approaches 16 Sharing of Technical Artifacts Social Networking Metadata Github/Gitlab Activity Feeds Discussion Forums Folksonomy Semantic Search  Partitioning of access  Bundling code and data  Data model harmonization  Cross-linkage (URIs/APIs)  Project-level feeds  Linkage to metadata  Current ontologies  Linkage to social functions
  17. 17. CIELHO Workflow Model 17
  18. 18. Community-Defined Requirements  Integration with analogous platforms and tools  Ex. Sage Bionetworks Synapse  Incorporation of data security/confidentiality controls  Particularly in the context of analyses involving PHI or similarly privileged data sets  Convergence towards common data model for submission and reuse of data sets  Ex. OMOP  Multi-tiered sharing model  Open access  Limited access  Private (for defined collaborators)  Semantic search and discovery of code and data  Connectivity to linked open data sets  Social networking at a project and individual level 18
  19. 19. Community-Defined Requirements: Focus for Public Beta  Integration with analogous platforms and tools  Ex. Sage Bionetworks Synapse  Incorporation of data security/confidentiality controls  Particularly in the context of analyses involving PHI or similarly privileged data sets  Convergence towards common data model for submission and reuse of data sets  Ex. OMOP  Multi-tiered sharing model  Open access  Limited access  Private (for defined collaborators)  Semantic search and discovery of code and data  Connectivity to linked open data sets  Social networking at a project and individual level 19
  20. 20. How Will We Evaluate the CIEHLO? 20
  21. 21. Overview 1) Background and Motivation 2) Conceptualization of CIELO 3) Functional and Technical Architecture 4) Future Directions 5) Today’s Objectives 6) Discussion 21
  22. 22. Future Directions: Shared Execution Environment (VAULT) 22
  23. 23. Overview 1) Background and Motivation 2) Conceptualization of CIELO 3) Functional and Technical Architecture 4) Future Directions 5) Today’s Objectives 6) Discussion 23
  24. 24. Meeting Objectives (1)  Provide a cross-section of stakeholders with a review of current technical functionality and design decisions surrounding the CIELO platform;  Identify needs for future functional/technical extensions to the platform, with a particular emphasis on: 1) Shared analytic tool execution environments and mechanisms 2) Data model promotion/harmonization 3) Crowd-sourced feedback and rating systems 4) Minimum standards for “bundle” population (code and example data) 24
  25. 25. Meeting Objectives (2)  Identify socio-cultural barriers and opportunities as they relate to creating and sustaining a CIELO user community;  Identify opportunity to promote and fund the ongoing development and adoption of CIELO as it can be positioned as a solution to enhancing research credibility and reproducibility. 25
  26. 26. Meeting Deliverables (1)  Stakeholder verification/validation of current CIELO functionality;  An enumeration and prioritization of future functional needs/requirements;  An enumeration of socio-cultural barriers and opportunities as they related to the creation and sustainability of an adopter/adapter community; 26
  27. 27. Meeting Deliverables (2)  An enumeration of communication, advocacy, and funding targets intended to position CIELO as a solution to enhancing research credibility and reproducibility;  An enumeration of targeted end-users and their communities;  A whitepaper and project plan that formalizes all of the preceding deliverables and provides a “roadmap” for future CIELO development and dissemination efforts. 27
  28. 28. Overview 1) Background and Motivation 2) Conceptualization of CIELO 3) Functional and Technical Architecture 4) Future Directions 5) Today’s Objectives 6) Discussion 28
  29. 29. 29 “Information liberation + new incentives = rocket fuel for innovation” – Aneesh Chopra (The Advisory Board Company) Philip R.O. Payne, PhD, FACMI philip.payne@osumc.edu "Without feedback from precise measurement, invention is doomed to be rare and erratic. With it, invention becomes commonplace” – Bill Gates (2013 Gates Foundation Annual Letter) “Data is beyond simply quantifying, it is seeing measurement as the intervention” – Carol McCall (GNS Healthcare)
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