Informatics and the Clinical and Translational Science Ecosystem
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  • 1. Integrating Informatics Into The Clinical and Translational Science Ecosystem September, 2013 Philip R.O. Payne, Ph.D. 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
  • 2. COI/Disclosures  Federal Funding: NCI, NLM, NCATS, AHRQ  Additional Research Funding: SAIC, Rockefeller Philanthropy Associates, Academy Health, Pfizer  Academic Consulting: CWRU, Cleveland Clinic, University of Cincinnati, Columbia University, Emory University, Virginia Commonwealth University, University of California San Diego, University of California Irvine, University of California San Francisco, University of Minnesota, Northwestern University  Other Consulting/Honoraria: American Medical Informatics Association (AMIA), Institute of Medicine (IOM)  Editorial Boards: Journal of the American Medical Informatics Association, Journal of Biomedical Informatics  Study Sections: NLM (BLIRC), NCATS (formerly NCRR), NIDDK  Corporate: Epic Systems, IBM, Accelmatics (Chief Scientific Officer) 2
  • 3. 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 3
  • 4. 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 4
  • 5. Defining Translation  trans·la·tion (noun): an act, process, or instance of translating: as  a: a rendering from one language into another; also : the product of such a rendering  b: a change to a different substance, form, or appearance  c: a transformation of coordinates in which the new axes are parallel to the old ones 5 Source: Merriam Webster Dictionary (http://www.merriam-webster.com/)
  • 6. Basic Science Clinical Research Clinical and Public Health Practice Clinical and Translational Science (CTS): Translation in the Context of Biomedicine 6 Knowledge Generation Common information needs, including:  Data collection and management  Integration  Knowledge management  Delivery  Presentation Application Continuous Cycle T1 T2
  • 7. Defining Systems Thinking  Systems thinking is the process of understanding how things influence one another within a whole  Approach to problem solving where "problems" are viewed as parts of an overall system  Major goal is to avoid development of unintended consequences as a result of solving problems in isolation  Promotes organizational communication at all levels in order to avoid the “silo” effect 7 Source: Wikipedia (http://en.wikipedia.org/wiki/Systems_thinking)
  • 8. 8 An Argument For “Translational Informatics”: Bridging Translation and Systems Thinking Improved Translation Systems Thinking Advances in Human Health Enabled by Biomedical Informatics
  • 9. Extending the Argument for Translational Informatics: Current Trends 9 Learning Healthcare Systems • Instrumenting the clinical environment • Generating hypotheses • Creating a culture of science and innovation Precision Medicine • Rapid evidence generation cycle(s) • „omics‟ • Analytics/decision support Big Data • System-level thinking • Data science 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
  • 10. 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 10
  • 11. 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 11
  • 12. Applying a Strategic Framework to Translational Informatics Dynamic Informatics Strategy Anticipating needs Challenging assumptions Interpreting “signals” Translating plans Alignment Learning and improving
  • 13. Anticipating Needs: Simplifying Programmatic Objectives 13
  • 14. Challenging Assumptions: Improving Stakeholder Access and Optimizing Resource Utilization 14
  • 15. Interpreting Signals: Identifying Opportunities for Structural and Functional Improvements • Regular environmental scans (internal and external) • Stakeholder surveys (annual) • Targeted workflow and ethnographic studies 15 In this context, an “Ecosystem” = …a community of interacting and highly interdependent actors, resources, and processes, which function as a cohesive and collective whole…
  • 16. Translating Plans: Leveraging Partnerships and Complementary Capabilities 16
  • 17. Alignment: Making Use of Existing Infrastructure and Pursuing Targeted Enhancements 17
  • 18. Learning and Improving: Measuring Processes and Outcomes and Providing Access to Evaluation Data 18
  • 19. 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 19
  • 20. Strategies & Future Directions: HIT 20 • Eliminating traditional boundaries • Focusing on economies of scale across mission areas • Bridging applied informatics and HIT practice • Semantics • NLP • Temporal Reasoning • IR • Visualization • Enabling end-user self service
  • 21. Strategies & Future Directions: BMI • Answering people-centric questions: • Workflow • Usability • Software Design Patterns • True platform integration: • SOA and Cloud Computing • Semantic web • Knowledge engineering • Visualization and HCI • Reasoning: • Data mining • Text mining/NLP • Data integration • Knowledge discovery • Enable all stakeholders to ask and answer questions • Includes informaticians 21
  • 22. Strategies & Future Directions: Culture  Harmonization of regulatory frameworks:  Early successes related to universal bio-specimen collection projects and GWAS/PWAS study paradigms  HIT and BMI must be partners:  Technology and methodological silos are major barriers  Socio-technical approach to platform adoption:  Adoption means more than being on-time and under-budget 22
  • 23. Innovative Platform Development EvaluationServices Implementation Science: An Opportunity to Balance Science and Service • Knowledge representation models • Semantic reasoning algorithms • Novel architectures • Workflow modeling • Human-factors • Informatics “translation” • Workflow modeling • Human-factors • System-level models of IT adoption • “Research on research”
  • 24. 24 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
  • 25. Differentiating Acculturation and Practice 25 Steering Wheel Pedals Transmission VS  Familiarity with structure/function  Conceptual knowledge  Minimal strategic/procedural knowledge  Emphasis on strategic/procedural knowledge  Demonstrable efficacy and resiliency with regard to practice
  • 26. 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 26
  • 27. Data Generation Application AND Evaluation of Knowledge Unification “4I” Values Information- Centricity Focusing on Context Integration Connecting the Dots Interactivity Engaging End-Users Innovation Creating New Solutions Proposed ApproachTraditional Model Data Generation Application of Knowledge Linear Translation Data Focused Application Specific Silos Engineering Approach to Design Leveraging Existing Technologies Current Trends Towards a “4I” Approach to Translational Informatics 27 Evolution To
  • 28. 28 Collaborators:  Peter J. Embi, MD, MS  Albert M. Lai, PhD  Kun Huang, PhD  Po-Yin Yen, RN, PhD  Yang Xiang, PhD  Marcelo Lopetegui, MD  Tara Borlawsky-Payne, MA  Omkar Lele, MS, MBA  Marjorie Kelley  William Stephens  Arka Pattanayak  Caryn Roth  Andrew Greaves Funding:  NCI: R01CA134232, R01CA107106, P01CA081534, P50CA140158, P30CA016058  NCATS: U54RR024384  NLM: R01LM009533, T15LM011270  AHRQ: R01HS019908  Rockefeller Philanthropy Associates  Academy Health – EDM Forum Acknowledgements Laboratory for Knowledge Based Applications and Systems Engineering (KBASE):
  • 29. 29 Thank you for your time and attention! • philip.payne@osumc.edu • http://go.osu.edu/payne