Biomedical Informatics: The Next 10 Years of Innovation

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Biomedical Informatics: The Next 10 Years of Innovation

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Biomedical Informatics: The Next 10 Years of Innovation

  1. 1. Biomedical Informatics: A Vision for the Next Decade of Innovation Philip R.O. Payne, PhD, FACMI Professor and Chair, College of Medicine, Department of Biomedical Informatics Professor, College of Public Health, Division of Health Services Management and Policy Associate Director for Data Sciences, Center for Clinical and Translational Science Co-Director, Bioinformatics Shared Resource, Comprehensive Cancer Center Executive-in-residence, Office of Technology Commercialization and Knowledge Transfer
  2. 2. Outline 1) The changing health and biomedical science landscape  Healthcare Transformation  Big Data  Systems Thinking  Translational Science 2) Responsive trends in Biomedical Informatics (BMI)  Translational bioinformatics  in silico Hypothesis Discovery  Evidence Generating Medicine (and the Learning Healthcare System)  Cognitive and Predictive Analytics  Workflow and Human Factors  Implementation Science  Workforce Development 3) Discussion  An emerging central dogma for BMI  The evolution of the Academic Enterprise 2
  3. 3. 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, eGEMS  Study Sections: NLM (BLIRC), NCATS (formerly NCRR), NIDDK  Corporate: Epic Systems, IBM, Signet Accel LLC (Founder and President) 3
  4. 4. Outline 1) The changing health and biomedical science landscape  Healthcare Transformation  Big Data  Systems Thinking  Translational Science 2) Responsive trends in Biomedical Informatics (BMI)  Translational bioinformatics  in silico Hypothesis Discovery  Evidence Generating Medicine (and the Learning Healthcare System)  Cognitive and Predictive Analytics  Workflow and Human Factors  Implementation Science  Workforce Development 3) Discussion  An emerging central dogma for BMI  The evolution of the Academic Enterprise 4
  5. 5. Healthcare Transformation (1) • Healthcare is undergoing its most significant evolution since the launch of Medicare in 1965 • Factors: • 2.8T industry (18% of GDP) • Operationalization of the Affordable Care Act • Currently, 20% of insurance premiums go to administrative overhead • Preventative care not a priority • 25% of admissions result in medical care that harms patients, 90% of the time regular analytical methods don't detect these events • 750B in avoidable healthcare costs • Healthcare providers are accumulating 85% more data than 2 years ago • 45% of this data is imaging • IDC estimates 80% of data is unstructured • Data is changing how we deliver healthcare: • Integration of personal monitors/sensors, EHRs, and predictive modeling for decision making • Focus on reducing costs and increasing quality • HDI (health data initiative) @ HHS is focused on making data open and available 5
  6. 6. Healthcare Transformation (2) • Impact of healthcare reform • Exposing fundamental gaps • Threatening long-standing advantages • Redrawing competitive landscape • Rewriting core business models • Shifting balance of power • Creating entirely new players • Fundamental problem in healthcare today is a bad business model (misaligned incentives, etc.) • What is the most important data to help enable/drive reform to the healthcare business model? • Imagine a world where measurement has become easy! • Catalyze desire and ability to learn • Unleash the power of observation • Tightly link research and practice 6
  7. 7. A Renewed Focus on Big Data: The 3 V’s 7 Volume Velocity Variability Any data set where conventional analytical approaches are not sufficient to generate insights and/or actionable knowledge
  8. 8. Sources of Big Data in the Health and Biomedical Sciences 8 Molecular Phenotype Environment Enterprise Systems and Data Repositories: EHR, CRMS, Data Warehouse(s) Emergent Sources PHR, Instruments, Etc. UbiComp (Sensors)
  9. 9. 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 9 Source: Wikipedia (http://en.wikipedia.org/wiki/Systems_thinking)
  10. 10. An Evolution from Reductionism to Systems Thinking 10  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  Influences policy, education, research and practice  Recent scientific paradigms have illustrated benefits of alternative approach (systems thinking) approaches  Systems biology/medicine  Network theory  Reductionist approach to data, information and knowledge management is still prevalent  HIT vs. Informatics  Informatics sub-disciplines
  11. 11. Basic Science Clinical Research Clinical and Public Health Practice Translational Science: From Lab to Laptop 11 Knowledge Generation Common informatics needs, including:  Data collection and management  Data Integration  Knowledge management  Information delivery (including visualization and HCI)  Advanced analytics Application Continuous Cycle T1 T2 The drive for Translational Science has been catalyzed by two major factors:  Extending timeline associated with the new therapy discovery pipeline  Data “tsunami” facing the life sciences
  12. 12. Outline 1) The changing health and biomedical science landscape  Healthcare Transformation  Big Data  Systems Thinking  Translational Science 2) Responsive trends in Biomedical Informatics (BMI)  Translational bioinformatics  in silico Hypothesis Discovery  Evidence Generating Medicine (and the Learning Healthcare System)  Cognitive and Predictive Analytics  Workflow and Human Factors  Implementation Science  Workforce Development 3) Discussion  An emerging central dogma for BMI  The evolution of the Academic Enterprise 12
  13. 13. BMI: A Sampling of Where We’ve Been Over the Past 60+ years  Basic Science  Standards and data representation  Knowledge engineering  Cognitive and decision science  Human factors and usability  Computational biology  Applied Science  Clinical Decision Support Systems (CDSS)  Clinical Information Systems (incl. EHRs)  Consumer-facing tools (incl. PHRs)  Bio-molecular data analysis “pipelines”  At the Intersection of Basic and Applied Science  Information Retrieval (IR)  Text Mining and Natural Language Processing (NLP)  Visualization  Image Analysis 13 AI in Medicine Computers in Medicine Medical Informatics Biomedical Informatics AnEvolvingNomenclature…
  14. 14. BMI: A Vision for the Next 10+ Years 1)Translational bioinformatics 2) in silico Hypothesis Discovery 3)Evidence Generating Medicine  Situated in the Learning Healthcare System 4)Cognitive and Predictive Analytics 5)Workflow and Human Factors 6)Implementation Science 7)Workforce Development 14 Making Sense of High- Throughput Data Delivering Knowledge- Based Healthcare Informatics as an Intervention Training The Future Health and Biomedical Workforce
  15. 15. Sarkar IN, Butte AJ, Lussier YA, Tarczy-Hornoch P, Ohno-Machado L. “Translational Bioinformatics: Linking Knowledge Across Biological and Clinical Realms” Journal of the American Medical Informatics Association. 2011. Jul-Aug;18(4):354-7. A Solution to the Biological Data Tsunami: Linking Molecules and Populations
  16. 16. Translational Bioinformatics (TBI) In Action: Generating Actionable Knowledge Slide courtesy of: Marcelo A. Lopetegui, MD; James Chen, MD
  17. 17. in silico Hypothesis Generation 17
  18. 18. Cytogenetic Abnormalities Treatment Response Bone Marrow Morphology Lymphomas Leukemia's Chromosome Loss Laboratory Findings Protein Expression Molecular Abnormalities Tissues of Origin Tissues of Origin Semantic Reasoning Across Bio-Molecular and Clinical Data in CLL
  19. 19. Evidence Generating Medicine (EGM) 19
  20. 20. EGM in Action: Instrumenting the EHR 20 Slide courtesy of: Marcelo A. Lopetegui, MD; Randi Foraker, PhD
  21. 21. Cognitive Systems Analytics Model 21
  22. 22. Enabling the Identification and Use of “Care Trajectories” to Optimize Outcomes and Cost 22
  23. 23. From Predictive Analytics to Decision Support 23
  24. 24. Cognitive Systems Analytics in Action: Tailored Clinical Decision Support 24 Slide courtesy of: Marcelo A. Lopetegui, MD; Tim Hewett, PhD
  25. 25. Workflow and Human Factors 25
  26. 26. Implementation Science: Understanding and 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 (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
  27. 27. 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”
  28. 28. Rethinking Training: Aligning Workforce Development Plans with Roles and Responsibilities 28
  29. 29. Mapping Competencies to Career Trajectories 29  Implementation of competency-based curricula  Emphasis on mastery, with practical applications at all levels  Leveraging emergent educational paradigms • “reverse classroom model” • Asynchronous content delivery • Project-based “threads”
  30. 30. Differentiating Acculturation and Practice 30 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
  31. 31. Outline 1) The changing health and biomedical science landscape  Healthcare Transformation  Big Data  Systems Thinking  Translational Science 2) Responsive trends in Biomedical Informatics (BMI)  Translational bioinformatics  in silico Hypothesis Discovery  Evidence Generating Medicine (and the Learning Healthcare System)  Cognitive and Predictive Analytics  Workflow and Human Factors  Implementation Science  Workforce Development 3) Discussion  An emerging central dogma for BMI  The evolution of the Academic Enterprise 31
  32. 32. Towards a Foundational Framework: An Emerging Central Dogma for Informatics 32 This applies across driving problems:  Biological  Clinical  Populations
  33. 33. A Few Thoughts Regarding the New Academic Enterprise 33 Traditional Model Emerging Model Departments and Divisions Multi-disciplinary Centers and Institutes Tuition, Grant, and Service Revenue Technology Transfer Revenue, Public-Private Partnerships, Contracts, Multi- Center Consortia Separation of Science and Service Service as Science: • Institutional • Community Publications and Presentations Commercialization, Translation into Healthcare Delivery Organizations Scholarly Home Revenue Dissemination Culture How To Achieve Balance?
  34. 34. A Survival Guide for BMI: 5 Key Points 1) Fully embrace interdisciplinary:  Structure  Function  Competency-based Training 2) Pursue emerging (or remerging) research foci:  Data science  Health services and quality improvement  Decision science and support (in the context of “Big Data”)  Human factors and workflow  Integrating patients and communities into the healthcare and research “fabric” 3) Engage with health system(s):  Analytics  Workflow and human factors  Transformation 4) Develop robust technology transfer and commercialization agendas  Partnerships and networking  “De-risking” technologies 5) Adapt strategies from the private sector  Identify and place disproportionate emphasis on “blue oceans”  Behave like a start-up (speed, agility, “real artists ship”) 34
  35. 35. A Final Word on BMI Training 35 March 26, 2014: http://www.usnews.com/education/best-graduate-schools/articles/2014/03/26/consider-pursuing-a-career-in-health-informatics
  36. 36. 36 Collaborators:  Peter J. Embi, MD, MS  Albert M. Lai, PhD  Kun Huang, PhD  Po-Yin Yen, RN, PhD  Tara Borlawsky-Payne, MA  Omkar Lele, MS, MBA  Marjorie Kelley, MS  Bobbie Kite, PhD  Cartik Saravanamuthu, PhD  Tasneem Motiwala, PhD  Zach Abrams  Kelly Regan  Arka Pattanayak  Andrew Greaves  Marcelo Lopetegui, MD, MS 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):
  37. 37. 37 “Information liberation + new incentives = rocket fuel for innovation” – Aneesh Chopra (The Advisory Board Company) Philip R.O. Payne, PhD, FACMI philip.payne@osumc.edu "Without feedback from precise measurement, invention is doomed to be rare and erratic. With it, invention becomes commonplace” – Bill Gates (2013 Gates Foundation Annual Letter) “Data is beyond simply quantifying, it is seeing measurement as the intervention” – Carol McCall (GNS Healthcare) Slides: http://go.osu.edu/InformaticsNext10Years

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