Translational Informatics: The "Glue" Between Basic Science and Clinical Research


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Presentation from AMIA 2013 panel on re-engineering the clinical research enterprise.

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Translational Informatics: The "Glue" Between Basic Science and Clinical Research

  1. 1. Biomedical Informatics: The “Glue” Between Basic Science and Clinical Research Philip R.O. Payne, PhD, FACMI Associate Professor and Chair, Biomedical Informatics (College of Medicine) Associate Professor, Health Services Management and Policy (College of Public Health) Associate Director for Data Sciences, Center for Clinical and Translational Science Executive-in-residence, Office of Technology Transfer and Commercialization Clinical Research Informatics: Re-Engineering the Clinical Research Enterprise AMIA Annual Symposium, 2013
  2. 2. Outline  Motivation  The evolving clinical and translational science ecosystem  The role of informatics in clinical and translational research  The OSU Center for Clinical and Translational Science (CCTS)  Lessons from the OSU CCTS  Next Steps  Emergent needs  The importance of implementation science  Workforce development  Discussion 2
  3. 3. Clinical and Translational Science (CTS): Linking Molecules to Populations The drive for CTS has been catalyzed by two major factors:  Extending timeline associated with the new therapy discovery pipeline  Data “tsunami” facing the life sciences Basic Science Knowledge Generation T1 Common information needs, including:  Data collection and management  Integration  Knowledge management  Delivery  Presentation Clinical Research Continuous Cycle T2 Clinical and Public Health Practice Application 3
  4. 4. The Evolving CTS Ecosystem: From Reductionism to Systems Thinking  Historical precedence for reductionism in biomedical and life sciences  Break down problems into fundamental units  Study units and generate knowledge  Reassemble knowledge into systems-level models  This viewpoint has traditionally influenced policy, education, research and practice  Recent scientific paradigms have illustrated major problems with this type of approach  Systems biology/medicine  Big data and “deep reasoning”  Network theory  In response, there has been an evolution of CTS towards a systems thinking approach  Policies  Funding  Career paths 4
  5. 5. Building an Argument for Translational Informatics: Current Trends Learning Healthcare Systems Rapid Translation Integrated and High Performing Healthcare Research and Delivery Systems 5 • Instrumenting the clinical environment • Generating hypotheses • Creating a culture of science and innovation Precision Medicine Big Data • Rapid evidence generation cycle(s) • „omics‟ • Analytics/decision support • System-level thinking • Data science Learning from every patient encounter Leveraging the best science to improve care Identifying and solving complex problems
  6. 6. “Data is beyond simply quantifying, it seeing measurement as the intervention” – Carol McCall (GNS Healthcare) 6
  7. 7. A Test-Bed:  The Center for Clinical and Translational Science (OSU CCTS) was founded in 2006, and is a collaboration among  The Ohio State University (OSU)  All seven health sciences colleges  Colleges of arts and sciences, business, and engineering  OSU Wexner Medical Center (OSUWMC)  Nationwide Children's Hospital (NCH)  Community health and education agencies  Business partnerships  Regional institutional networks  CTSA funded in 2008  The OSU CCTS provides financial, organizational, and educational support to biomedical researchers, as well as opportunities for community members to participate in credible and valuable research.  Focused on turning the scientific discoveries of today into life-changing disease prevention strategies and the health diagnostics and treatments of tomorrow 7
  8. 8. Applying a Strategic Framework to Research Informatics Practice Anticipating needs Learning and improving Challenging assumptions Dynamic Informatics Strategy Interpreting “signals” Alignment Translating plans
  9. 9. Anticipating Needs: Simplifying Programmatic Objectives 9
  10. 10. Challenging Assumptions: Improving Stakeholder Access and Optimizing Resource Utilization 10
  11. 11. Interpreting Signals: Identifying Opportunities for Structural and Functional Improvements • Regular environmental scans (internal and external) In this context, an “Ecosystem” = …a community of interacting and •highly interdependent actors,(annual) and processes, which function as a Stakeholder surveys resources, cohesive and collective whole… • Targeted workflow and ethnographic studies 11
  12. 12. Translating Plans: Leveraging Partnerships and Complementary Capabilities 12
  13. 13. Alignment: Making Use of Existing Infrastructure and Pursuing Targeted Enhancements 13
  14. 14. Learning and Improving: Measuring Processes and Outcomes and Providing Access to Evaluation Data 14
  15. 15. Implementation Science: An Opportunity to Balance Science and Service • • • • • Informatics “translation” Workflow modeling Human-factors System-level models of IT adoption “Research on research” Innovative Platform Development Service Line • • • • • Knowledge representation models Semantic reasoning algorithms Novel architectures Workflow modeling Human-factors Evaluation
  16. 16. Empowering Knowledge Workers Driving Biological and Clinical Problems Knowledge Workers Solutions to Real World Problems Critical Issues:  Workflows that enable engagement by Subject Matter Experts  Tight coupling of engineering efforts and research programs that can define driving “real world” problems  Facilitation and support of interdisciplinary, team science models (including basic and translational scientists, clinical researchers, and informaticians)  Incorporation of human and cognitive factors in all aspects of projects Biomedical Informatics ≠ Engineering Systems-level Approaches To Interoperability and Usability Are Essential 16
  17. 17. Towards a “4I” Approach to Research Informatics Current Trends Data Focused Application Specific Silos Proposed Approach Traditional Model Data Generation Linear Translation Data Generation Evolution To Unification Engineering Approach to Design InformationCentricity Focusing on Context Integration Connecting the Dots Interactivity Engaging End-Users Leveraging Existing Technologies Innovation Creating New Solutions Application of Knowledge 17 “4I” Values Application AND Evaluation of Knowledge
  18. 18. “Information liberation + new incentives = rocket fuel for innovation” – Aneesh Chopra (The Advisory Board Company) 18
  19. 19. Acknowledgements Collaborators: Funding:  Peter J. Embi, MD, MS  Albert M. Lai, PhD  NCI: R01CA134232, R01CA107106, P01CA081534, P50CA140158, P30CA016058  Kun Huang, PhD  NCATS: U54RR024384  Po-Yin Yen, RN, PhD  NLM: R01LM009533, T15LM011270  Yang Xiang, PhD  AHRQ: R01HS019908  Marcelo Lopetegui, MD  Rockefeller Philanthropy Associates  Tara Borlawsky-Payne, MA  Academy Health – EDM Forum  Omkar Lele, MS, MBA  Marjorie Kelley  William Stephens  Arka Pattanayak  Caryn Roth  Andrew Greaves 19 Laboratory for Knowledge Based Applications and Systems Engineering (KBASE):
  20. 20. Thank you for your time and attention! • • 20