Integrating Informatics Into The                                 Clinical and Translational                               ...
COI/Disclosures     Federal Funding: NCI, NLM, NCATS, AHRQ     Additional Research Funding: SAIC, Rockefeller Philanthro...
Outline     Motivation        The evolving clinical and translational science ecosystem        The role of informatics ...
Outline     Motivation        The evolving clinical and translational science ecosystem        The role of informatics ...
Defining Translation     trans·la·tion (noun): an act, process, or instance of      translating: as        a: a renderin...
Clinical and Translational Science (CTS):    Translation in the Context of Biomedicine                                   B...
The Evolving CTS Ecosystem:    From Reductionism to Systems Thinking     Historical precedence for reductionism in biomed...
Defining Systems Thinking     Systems thinking is the process of understanding how      things influence one another with...
An Argument For “Translational Informatics”:    Bridging Translation and Systems Thinking                                 ...
Extending the Argument for Translational     Informatics: Current Trends                                                  ...
A Test-Bed:      The Center for Clinical and Translational Science (OSU CCTS) was founded       in 2006, and is a collabo...
Outline      Motivation         The evolving clinical and translational science ecosystem         The role of informati...
Applying a Strategic Framework toTranslational Informatics                           Anticipating                         ...
Anticipating Needs: Simplifying Programmatic Objectives14
Challenging Assumptions: Improving Stakeholder Access     and Optimizing Resource Utilization15
Interpreting Signals: Identifying Opportunities for     Structural and Functional Improvements     • Regular environmental...
Translating Plans: Leveraging Partnerships and     Complementary Capabilities17
Alignment: Making Use of Existing Infrastructure and     Pursuing Targeted Enhancements18
Learning and Improving: Measuring Processes and     Outcomes and Providing Access to Evaluation Data19
Outline      Motivation         The evolving clinical and translational science ecosystem         The role of informati...
Next Steps: Achieving the Vision of     Translational Informatics            Strategic      Implementation         Workfor...
Strategies & Future Directions: HIT     • Eliminating traditional boundaries     • Focusing on economies of scale       ac...
Strategies & Future Directions: BMI     • Answering people-centric       questions:        • Workflow        • Usability  ...
Strategies & Future Directions: Culture      Harmonization of regulatory frameworks:          Early successes related to...
The Importance of Implementation Science:     Coping With Constant Evolution in Technology     1950-60‟s: Specialized comp...
Implementation Science: An Opportunity    to Balance Science and Service                                                  ...
Empowering Knowledge Workers           Driving                                                                      Soluti...
Aligning BMI Training and Workforce Development with     Roles and Responsibilities in the CTS Environment28
Differentiating Acculturation and Practice                                                 Familiarity with              ...
Outline      Motivation         The evolving clinical and translational science ecosystem         The role of informati...
Towards a “4I” Approach to Translational Informatics      Current       Traditional Model                  Proposed Approa...
Acknowledgements     Collaborators:               Funding:      Peter J. Embi, MD, MS       NCI: R01CA134232, R01CA10710...
Thank you for your time and attention!     • philip.payne@osumc.edu     • http://go.osu.edu/payne33
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Integrating Informatics into the Clinical and Translational Science Ecosystem

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Transcript of "Integrating Informatics into the Clinical and Translational Science Ecosystem"

  1. 1. Integrating Informatics Into The Clinical and Translational Science Ecosystem 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 CommercializationStanford Center for Biomedical Informatics Research (BMIR) ColloquiaMarch 14, 2013
  2. 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: Oracle, HP, Epic, Accelmatics (interim-CEO)2
  3. 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  Discussion3
  4. 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  Discussion4
  5. 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 Source: Merriam Webster Dictionary (http://www.merriam-webster.com/)5
  6. 6. Clinical and Translational Science (CTS): Translation in the Context of Biomedicine Basic Science Knowledge Generation T1 Clinical Virtuous Common information Research Cycle needs, including:  Data collection and T2 management  Integration  Knowledge management Clinical and  Delivery Public Health  Presentation Practice Application6
  7. 7. 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 paths7
  8. 8. 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 Source: Wikipedia (http://en.wikipedia.org/wiki/Systems_thinking)8
  9. 9. An Argument For “Translational Informatics”: Bridging Translation and Systems Thinking Advances Improved Systems in Human Translation Thinking Health Enabled by Biomedical Informatics9
  10. 10. Extending the Argument for Translational Informatics: Current Trends • Instrumenting the Learning clinical environment from every Learning • Generating patient Healthcare hypotheses encounter Systems • Creating a culture of science and innovation • Rapid evidence Leveraging generation cycle(s) the best Rapid Precision Translation • „omics‟ science to Medicine • Analytics/decision improve care support Identifying and solving Integrated and High • System-level thinking complex Big Data • Data science problems Performing Healthcare Research and Delivery Systems10
  11. 11. 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 Childrens 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 tomorrow11
  12. 12. 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  Discussion12
  13. 13. Applying a Strategic Framework toTranslational Informatics Anticipating needs Learning and Challenging improving assumptions Dynamic Informatics Strategy Interpreting Alignment “signals” Translating plans
  14. 14. Anticipating Needs: Simplifying Programmatic Objectives14
  15. 15. Challenging Assumptions: Improving Stakeholder Access and Optimizing Resource Utilization15
  16. 16. 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 studies16
  17. 17. Translating Plans: Leveraging Partnerships and Complementary Capabilities17
  18. 18. Alignment: Making Use of Existing Infrastructure and Pursuing Targeted Enhancements18
  19. 19. Learning and Improving: Measuring Processes and Outcomes and Providing Access to Evaluation Data19
  20. 20. 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  Discussion20
  21. 21. Next Steps: Achieving the Vision of Translational Informatics Strategic Implementation Workforce Research Foci Science Development Translation + Systems Thinking21
  22. 22. Strategies & Future Directions: HIT • 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 service22
  23. 23. 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 informaticians23
  24. 24. 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-budget24
  25. 25. The Importance of Implementation Science: Coping With Constant Evolution in Technology 1950-60‟s: Specialized computing Today: Tele-health, mobile computing, facilities, programming languages, widespread EHR adoption, service- decision support, bibliographic oriented architectures, genomic and databases, basic clinical documentation personalized medicine applications, systems, first training programs translational research25
  26. 26. Implementation Science: An Opportunity to Balance Science and Service • Knowledge representation models• Informatics “translation” • Semantic reasoning algorithms•• Workflow modeling Human-factors Innovative • • Novel architectures Workflow modeling•• System-level models of IT adoption “Research on research” Platform • Human-factors Development Service Evaluation Line
  27. 27. Empowering Knowledge Workers Driving Solutions to Biological Knowledge Real World and Clinical Workers Problems 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 Essential27
  28. 28. Aligning BMI Training and Workforce Development with Roles and Responsibilities in the CTS Environment28
  29. 29. Differentiating Acculturation and Practice  Familiarity with Steering Wheel structure/function  Conceptual knowledge  Minimal strategic/procedural Transmission knowledge Pedals VS  Emphasis on strategic/procedural knowledge  Demonstrable efficacy and resiliency with regard to practice29
  30. 30. 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  Discussion30
  31. 31. Towards a “4I” Approach to Translational Informatics Current Traditional Model Proposed Approach “4I” Trends Values Information- Centricity Data Focusing on Focused Data Data Context Generation Generation Application Integration Specific Connecting the Silos Linear Dots Translation Evolution To Unification Engineering Interactivity Approach to Engaging Design End-Users Leveraging Innovation Existing Creating New Technologies Solutions Application of Application AND Knowledge Evaluation of Knowledge31
  32. 32. Acknowledgements Collaborators: Funding:  Peter J. Embi, MD, MS  NCI: R01CA134232, R01CA107106, P01CA081534, P50CA140158,  Albert M. Lai, PhD 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 Laboratory for Knowledge  Marjorie Kelley Based Applications and  William Stephens Systems Engineering (KBASE):  Arka Pattanayak  Caryn Roth  Andrew Greaves32
  33. 33. Thank you for your time and attention! • philip.payne@osumc.edu • http://go.osu.edu/payne33

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