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Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
Reducing The Distance Between Data & Knowledge
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Reducing The Distance Between Data & Knowledge

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Payne on Reducing The Distance Between Data & Knowledge: Realizing the Promise of HIT and Biomedical Informatics

Payne on Reducing The Distance Between Data & Knowledge: Realizing the Promise of HIT and Biomedical Informatics

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  • The clinical template should be used for presentation to a clinical audience and focused on clinical topics. Examples include clinical grand rounds, clinical conferences and seminars and internal clinical meetings.
  • Transcript

    • 1. Reducing The Distance Between Data & Knowledge: Realizing the Promise of HIT and Biomedical Informatics
      Philip R.O. Payne, Ph.D.
      Associate Professor & Chair, Department of Biomedical Informatics
      Executive Director, Center for IT Innovations in Healthcare
      Co-Director, Center for Clinical and Translational Science, Biomedical Informatics Program
      Co-Director, Comprehensive Cancer Center, Biomedical Informatics Shared Resource
      OSU Center for Personalize Healthcare
      National Conference
      October 6, 2011
    • 2. Outline
      Problem Statement
      The Promise of HIT and Biomedical Informatics
      Creating a Learning Healthcare System
      Generating Knowledge
      Strategies and Future Directions
      HIT
      Biomedical Informatics
      Cultural
      Discussion
      2
    • 3. The Problem
      Data
      Generation
      EHR
      Increasing
      Distance
      Management,
      Integration,
      Delivery
      EDW
      Knowledge
      Generation
      3
    • 4. Contributing Factors (1)
      Regulatory, Technical, and Cultural Barriers Between Data and Knowledge Generation
      High performance systems require rapid adaptation
      Increasing demand for better, faster, safer, more cost effective therapies
      Simultaneous demand for increased controls over secondary use of clinical data
      Artificial partitioning of access to data for knowledge generation purposes
      Critical overlaps and potential sources of conflict between these factors
      CI, Imaging, CRI, TBI, PHI
      Clinical Investigators
      Bioinformatics, TBI, CRI
    • 5. Contributing Factors (2)
      5
      Historical precedence for reductionism in the biomedical and life sciences
      Break-down problems into fundamental units
      Study units and generate knowledge
      Reassemble knowledge into systems-level models
      Influences policy, education, research, and practice
      Recent scientific paradigms have illustrated major problems with this type of approach
      Systems biology/medicine
      Reductionist approach to data, information, and knowledge management is still prevalent
      HIT vs. Informatics
      Informatics sub-disciplines
    • 6. Point
      “The whole is more than the sum of its parts.” - Aristotle
      Counter-Point
      “To make progress in understanding all this, we probably need to begin with simplified (oversimplified?) models and ignore the critics' tirade that the real world is more complex. The real world is always more complex, which has the advantage that we shan't run out of work.” - John Ball
      Is it time for a systems-approach to secondary use of data in healthcare? If so, how do we reduce the “distance” between data and knowledge generation?
    • 7. The Promise of Healthcare IT (HIT)
      Delivering timely and contextually appropriate data, information, and knowledge in support of basic science, clinical and translational research, clinical care, and public health.
    • 8. Creating a Learning Healthcare System: Learning from Every Patient and Improving Care
      Clinical Informatics
      Public Health Informatics
      Translational Bioinformatics
      Clinical Research Informatics
      Learn from every patient encounter so that we can improve their care, their families care, and their communities care
    • 9. Many Sources of Data
      9
      Emergent Sources
      PHR, Instruments, Etc.
      Molecular Phenotype
      Enterprise Systems and Data Repositories:
      EHR, CTMS, Data Warehouses
      Environment
    • 10. The Biomedical Informatics Continuum: From Data to Knowledge
      10
      + Application
      + Context
    • 11. Significant Barriers
      11
      • Technical
      • 12. Regulatory
      • 13. Cultural
      • 14. Achieving shared language and understanding between stakeholders
      The Construction of the Tower of Babel (Hendrick van Clev)
      Source: Wikimedia Commons
    • 15. Strategies & Future Directions: HIT
      12
      • Eliminating traditional boundaries
      • 16. Focusing on economies of scale across mission areas
      • 17. Bridging applied informatics and HIT practice
      • 18. Semantics
      • 19. NLP
      • 20. Temporal Reasoning
      • 21. IR
      • 22. Visualization
      • 23. Enabling end-user self service
    • Strategies & Future Directions: BMI
      13
      • Answering people-centric questions:
      • 24. Workflow
      • 25. Usability
      • 26. Software Design Patterns
      • 27. True platform integration:
      • 28. SOA and Cloud Computing
      • 29. Semantic web
      • 30. Knowledge engineering
      • 31. Visualization and HCI
      • 32. Reasoning:
      • 33. Data mining
      • 34. Text mining/NLP
      • 35. Data integration
      • 36. Knowledge discovery
      • 37. Enable all stakeholders to ask and answer questions
      • 38. Includes informaticians
    • Strategies & Future Directions: Culture
      14
      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
    • 39. Revisiting “The Problem”
      Data
      Generation
      EHR
      Increasing
      Distance
      Management,
      Integration,
      Delivery
      EDW
      Knowledge
      Generation
      15
    • 40. Towards a Solution: A Systems Approach to Biomedicine
      HIT & Biomedical Informatics “Fabric”
      Knowledge
      Generation
      Data
      Generation
      16
    • 41. 17
      Thank you for your time and attention!
      • philip.payne@osumc.edu
      • 42. http://bmi.osu.edu/~payne

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