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

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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.
  • Reducing The Distance Between Data & Knowledge

    1. 1. Reducing The Distance Between Data & Knowledge: Realizing the Promise of HIT and Biomedical Informatics<br />Philip R.O. Payne, Ph.D.<br />Associate Professor & Chair, Department of Biomedical Informatics<br />Executive Director, Center for IT Innovations in Healthcare<br />Co-Director, Center for Clinical and Translational Science, Biomedical Informatics Program<br />Co-Director, Comprehensive Cancer Center, Biomedical Informatics Shared Resource<br />OSU Center for Personalize Healthcare<br />National Conference<br />October 6, 2011<br />
    2. 2. Outline<br />Problem Statement<br />The Promise of HIT and Biomedical Informatics<br />Creating a Learning Healthcare System<br />Generating Knowledge<br />Strategies and Future Directions<br />HIT<br />Biomedical Informatics<br />Cultural<br />Discussion<br />2<br />
    3. 3. The Problem<br />Data <br />Generation<br />EHR<br />Increasing<br />Distance<br />Management,<br />Integration,<br />Delivery<br />EDW<br />Knowledge<br />Generation<br />3<br />
    4. 4. Contributing Factors (1)<br />Regulatory, Technical, and Cultural Barriers Between Data and Knowledge Generation<br />High performance systems require rapid adaptation<br />Increasing demand for better, faster, safer, more cost effective therapies<br />Simultaneous demand for increased controls over secondary use of clinical data<br />Artificial partitioning of access to data for knowledge generation purposes<br />Critical overlaps and potential sources of conflict between these factors<br />CI, Imaging, CRI, TBI, PHI <br />Clinical Investigators<br />Bioinformatics, TBI, CRI<br />
    5. 5. Contributing Factors (2)<br />5<br />Historical precedence for reductionism in the biomedical and life sciences<br />Break-down problems into fundamental units<br />Study units and generate knowledge<br />Reassemble knowledge into systems-level models<br />Influences policy, education, research, and practice<br />Recent scientific paradigms have illustrated major problems with this type of approach<br />Systems biology/medicine<br />Reductionist approach to data, information, and knowledge management is still prevalent<br />HIT vs. Informatics<br />Informatics sub-disciplines<br />
    6. 6. Point<br />“The whole is more than the sum of its parts.” - Aristotle<br />Counter-Point<br />“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<br />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? <br />
    7. 7. The Promise of Healthcare IT (HIT)<br />Delivering timely and contextually appropriate data, information, and knowledge in support of basic science, clinical and translational research, clinical care, and public health.<br />
    8. 8. Creating a Learning Healthcare System: Learning from Every Patient and Improving Care<br />Clinical Informatics<br />Public Health Informatics<br />Translational Bioinformatics<br />Clinical Research Informatics<br />Learn from every patient encounter so that we can improve their care, their families care, and their communities care<br />
    9. 9. Many Sources of Data<br />9<br />Emergent Sources<br />PHR, Instruments, Etc.<br />Molecular Phenotype<br />Enterprise Systems and Data Repositories:<br />EHR, CTMS, Data Warehouses<br />Environment<br />
    10. 10. The Biomedical Informatics Continuum: From Data to Knowledge<br />10<br />+ Application<br />+ Context<br />
    11. 11. Significant Barriers<br />11<br /><ul><li>Technical
    12. 12. Regulatory
    13. 13. Cultural
    14. 14. Achieving shared language and understanding between stakeholders</li></ul>The Construction of the Tower of Babel (Hendrick van Clev) <br />Source: Wikimedia Commons <br />
    15. 15. Strategies & Future Directions: HIT<br />12<br /><ul><li>Eliminating traditional boundaries
    16. 16. Focusing on economies of scale across mission areas
    17. 17. Bridging applied informatics and HIT practice
    18. 18. Semantics
    19. 19. NLP
    20. 20. Temporal Reasoning
    21. 21. IR
    22. 22. Visualization
    23. 23. Enabling end-user self service</li></li></ul><li>Strategies & Future Directions: BMI<br />13<br /><ul><li>Answering people-centric questions:
    24. 24. Workflow
    25. 25. Usability
    26. 26. Software Design Patterns
    27. 27. True platform integration:
    28. 28. SOA and Cloud Computing
    29. 29. Semantic web
    30. 30. Knowledge engineering
    31. 31. Visualization and HCI
    32. 32. Reasoning:
    33. 33. Data mining
    34. 34. Text mining/NLP
    35. 35. Data integration
    36. 36. Knowledge discovery
    37. 37. Enable all stakeholders to ask and answer questions
    38. 38. Includes informaticians</li></li></ul><li>Strategies & Future Directions: Culture<br />14<br />Harmonization of regulatory frameworks:<br />Early successes related to universal bio-specimen collection projects and GWAS/PWAS study paradigms<br />HIT and BMI must be partners:<br />Technology and methodological silos are major barriers<br />Socio-technical approach to platform adoption:<br />Adoption means more than being on-time and under-budget<br />
    39. 39. Revisiting “The Problem”<br />Data <br />Generation<br />EHR<br />Increasing<br />Distance<br />Management,<br />Integration,<br />Delivery<br />EDW<br />Knowledge<br />Generation<br />15<br />
    40. 40. Towards a Solution: A Systems Approach to Biomedicine<br />HIT & Biomedical Informatics “Fabric”<br />Knowledge<br />Generation<br />Data <br />Generation<br />16<br />
    41. 41. 17<br />Thank you for your time and attention!<br /><ul><li>
    42. 42.</li>