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From Proteins to Populations: How Do We Integrate Biomedical Informatics across Kansas University?

Russ Waitman Director, Medical Informatics Associate Professor, Department of Biostatistics Kansas University Medical Center . From Proteins to Populations: How Do We Integrate Biomedical Informatics across Kansas University?. K-INBRE Seminar May 11, 2010. Disclaimer.

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From Proteins to Populations: How Do We Integrate Biomedical Informatics across Kansas University?

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  1. Russ Waitman Director, Medical Informatics Associate Professor, Department of Biostatistics Kansas University Medical Center

    From Proteins to Populations: How Do We Integrate Biomedical Informatics across Kansas University?

    K-INBRE Seminar May 11, 2010
  2. Disclaimer Warning: this talk draws extensively from the work of esteemed informaticians and should not be seen as the novel thought of the presenter. Any proposals are based on a preliminary three month assessment and are designed to promote discussion. The presenter does not have any conflicts of interest regarding the information presented.
  3. Trade Study: Preliminary Mammoth vs. Game Population Final Game Site Survey Tiger vs. Rabbit Selection Survey Selection Meat Meat Requirements Site Preparation Requirements Review Site Selection (MRR) Game Sightings Final Hunting Hunt Leader Plan Chosen Preliminary Hunting Plan Preliminary Hunter Preliminary Selection Hunting Weapon Trade Study: Review Selection Club vs. Spear Weapon (PHR) Development Weapons Obtain Blessing Meat Practice and Skill of Great God Distribution Final Hunter Qualification Thag Plan Selection Critical Hunting Site Forecast Review Inspection Schedule Weather (CHR) Hunt Weapons Inspection Examine Hunt Entrails Readiness Review Distribute Hunter (HRR) Transport to Meat Inspection Cave Catch Game KILL Game Yes, but Og assures me that Get Caught Chase Game this will improve By Game Choose New Hunt Leader our efficiency and keep us ahead of I don't know. It seemed those Lose Game Cro-Magnons in easier when we just the valley. went hunting. Why the Neanderthals Became Extinct
  4. Outline Perspectives on biomedical informatics NIH objectives regarding translational research? Strawmanfor KU and filling medical informatics gaps Discussion: What is the vision for bioinformatics in Kansas? What are the strongest stories and linkages we can tell or relationships we can build across campuses? What projects should we pursue to make contributions to informatics as a discipline versus providing clinical translational research support?
  5. Background: Charles Friedman The Fundamental Theorem of Biomedical Informatics: A person working with an information resource is better than that same person unassisted. NOT!! Charles P. Friedman: http://www.jamia.org/cgi/reprint/16/2/169.pdf
  6. Background: Randolph Miller ON THE NEED FOR DECISION SUPPORT: 1. Life is short, the art long, opportunity fleeting, experience treacherous, judgment difficult. Hippocrates. Aphorisms, ~460-400 BC ALSO ON THE NEED FOR DECISION SUPPORT: 2. Men are men; the best sometimes forget. Shakespeare. Othello, 1604-5 ON THE NEED TO EVALUATE DECISION SUPPORT SYSTEMS: (also interpreted as avoidance of medical informatics vaporware) 3. The proof of the pudding is in the eating. Miguel de Cervantes. Don Quixote, 1605
  7. Evidence Synthesis & Decision Background: William SteadThe Individual Expert Clinician Patient Record William Stead: http://courses.mbl.edu/mi/2009/presentations_fall/SteadV1.ppt
  8. The demise of expert-based practice is inevitable 1000 Proteomics and other effector molecules Facts per Decision 100 Functional Genetics: Gene expression profiles 10 Structural Genetics: e.g. SNPs, haplotypes Human Cognitive Capacity 5 Decisions by Clinical Phenotype 1990 2000 2010 2020 William Stead: http://courses.mbl.edu/mi/2009/presentations_fall/SteadV1.ppt
  9. Public Health Nursing Dentistry Visualization Clinical Medicine Veterinary Medicine Molecular Biology Applied Research Background: Edward Shortliffe Methods, Techniques, and Theories Basic Research Edward Shortliffe: http://www.dentalinformatics.com/conference/conference_presentations/shortliffe.ppt
  10. Applied Research Background: Edward Shortliffe Biomedical Informatics Methods, Techniques, and Theories Basic Research Imaging Informatics Clinical Informatics Public Health Informatics Bioinformatics Molecular and Cellular Processes Tissues and Organs Individuals (Patients) Populations And Society Edward Shortliffe: http://www.dentalinformatics.com/conference/conference_presentations/shortliffe.ppt
  11. Biomedical Research Planning & Data Analysis Knowledge Acquisition Data Acquisition Model Development Image Generation Information Retrieval Treatment Planning Human Interface Diagnosis Teaching Background: Edward ShortliffeBiomedical Informatics Research Areas Biomedical Knowledge Biomedical Data Real-time acquisition Imaging Speech/language/text Specialized input devices Machine learning Text interpretation Knowledge engineering Knowledge Base Data Base Inferencing System Edward Shortliffe: http://www.dentalinformatics.com/conference/conference_presentations/shortliffe.ppt
  12. Background: Dan MasysNIH Goal to Reduce Barriers to Research Administrative bottlenecks Poor integration of translational resources Delay in the completion of clinical studies Difficulties in human subject recruitment Little investment in methodologic research Insufficient bi-directional information flow Increasingly complex resources needed Inadequate models of human disease Reduced financial margins Difficulty recruiting, training, mentoring scientists
  13. Clinical and Translational Science AwardsA NIH Roadmap Initiative “It is the responsibility of those of us involved in today’s biomedical research enterprise to translate the remarkable scientific innovations we are witnessing into health gains for the nation.”
  14. CTSA Objectives: The purpose of this initiative is to assist institutions to forge a uniquely transformative, novel, and integrative academic home for Clinical and Translational Science that has the consolidated resources to: 1) captivate, advance, and nurture a cadre of well-trained multi- and inter-disciplinary investigators and research teams; 2) create an incubator for innovative research tools and information technologies; and 3) synergize multi-disciplinary and inter-disciplinary clinical and translational research and researchers to catalyze the application of new knowledge and techniques to clinical practice at the front lines of patient care.
  15. NIH CTSAs: Home for Clinical and Translational Science NIH Other Institutions Clinical Research Ethics Trial Design Gap! Advanced Degree-Granting Programs Biomedical Informatics CTSA HOME Industry Participant & Community Involvement Clinical Resources Biostatistics Regulatory Support Dan Masys: http://courses.mbl.edu/mi/2009/presentations_fall/masys.ppt
  16. Reengineering Clinical Research Interdisciplinary Research Innovator Award Public-Private Partnerships Bench Bedside Practice Building Blocks and Pathways Molecular Libraries Bioinformatics Computational Biology Nanomedicine Integrated Research Networks Clinical Research Informatics NIH Clinical Research Associates Clinical outcomes Harmonization Training Translational Research Initiatives Dan Masys: http://courses.mbl.edu/mi/2009/presentations_fall/masys.ppt
  17. Role of Informatics in Clinical and Translational Research Structured observation and record keeping are the essence of science Informatics Centric Efforts: Clinical Trial enrollment Clinical Trial software Reuse, integration, and sharing of electronic health data to support translational research Bioinformatics and Biospecimen management Methods Applicable to other large infrastructure needs: NCI Cancer Center designation. CTSA Informatics cross institutional goals?
  18. IKFC Ops Committee; Dec, 2008 CTSA Consortium Steering Committee (CCSC) Informatics Key Function Committee and Operations subcommittee IKFC Prioritized Projects, Special Interest Groups (SIGS), Projects Human Study Database Project group (Sim and Team) Data Repositories (Kamerick) Standards & Interoperability (Chute) Education (Klee, Hersh) Collaboration Facilitation (Kahlon) Resource Inventory (Becich, Athey & Team) Data Sharing (Silverstein, Anderson) Nat’l Human Subject Volunteer Registry(Harris) CTSA PI Priorities National Clinical and Translational Research Capability Clinical Research Management Research Infrastructure Phenotyping-human and pre-clinical models Training & Career Development of Clinical/Translational Scientists Enhancing Consortium-Wide Collaborations Members Social Networking Inventory of Resources Data sharing Enhancing the Health of Our Communities and the Nation Community Engagement Public Health Policy CTSA Strategic Goal Committees CTSA PI Liaisons Other Key Function Committees IKFC Prioritization Process
  19. Building a Vision: Environmental Comparison VU/KU VUMC: unified leadership across hospitals, clinics, academics Unified informatics: from network jack and server, to library and bioinformatics cores Build/buy mix legacy -> complexity Large consolidated academic home for informatics Data sharing for research a non issue KU/KUMC, Rest of the world: not so homogenous What can one do with EPIC or Cerner + added informatics? Validated solutions more likely to scale. Data sharing involves multiple organizations
  20. KU Opportunities Quality Focused Hospital Without every solution involving informatics CTSA goal: Data “Warehouse” Advance research and clinical quality “Green Field” for newer technologies State and Region KUMC strong in community outreach research Link our data to external information? (Ex: KHPA Medicaid data) Health Information Exchange “window” KU Lawrence Informatics, Stowers Long term: Cerner
  21. Medical Informatics: Short Term Approach Data sharing aggrementand data access This is not a one size fits all solution Develop terms of agreement and oversight Understand current information strategy and timelines of our partner organizations Engage research community Establish development environment Gain experience with KUH/KUPI/KUMC information systems Focus on practical pilot projects Ideally, benefits to clinical quality and research
  22. Data Sharing roles: entities with justifiable (and variable) rights to medical data First order role definitions: Provider, Patient, Payer, “Society” Second order: Providers: primary vs. consultant provider, ancillary support staff Patient: self, family, legally authorized reps Payer: billing staff and subcontractors, clearinghouses, insurers Society: public health agencies, state medical boards, law enforcement agencies Dan Masys: http://crypto.stanford.edu/portia/workshops/2004_7_slides/masys.ppt
  23. Data Sharing roles: entities with justifiable (and variable) rights to medical data Third order: Providers: internal and external QA entities (peer review, JCAHO), sponsors of clinical research Patient: community support groups, personal friends Payers: fraud detection (Medical Information Bureau), business consultants Society: national security, bioterrorism detection Dan Masys: http://crypto.stanford.edu/portia/workshops/2004_7_slides/masys.ppt
  24. Healthcare Information Access Roles Community Support Internal QA External accreditation orgs Primary care Friends Legally Authorized Reps Specialists Ancillaries Clinical Trials Sponsors Immediate Family Extended Family Patient Provider Admin. Staff Claims Processors Public Health Payer Society Fraud Detection Subcontractors State Licensure Boards Clearinghouses National Security Medical Information Bureau Law Enforcement Insurers Bioterrorism Detection Business Consultants Dan Masys: http://crypto.stanford.edu/portia/workshops/2004_7_slides/masys.ppt
  25. Intermediate CTSA aligned goals Build team, evaluate, and choose appropriate informatics products and underlying technologies Implement incremental construction of “warehouse” + information strategy Balance retrospective with near real time opportunities Clinical data foundation, then link to other resources and provide research opportunity Potential linkages with biospecimens State data for epidemiology research NDNQI nursing quality indicators
  26. Public Health Nursing Dentistry Visualization Clinical Medicine Veterinary Medicine Molecular Biology Applied Research Strawman: Recall Shortliffe Model Methods, Techniques, and Theories Basic Research Edward Shortliffe: http://www.dentalinformatics.com/conference/conference_presentations/shortliffe.ppt
  27. KU CTSA and Overall Strawman Academic Homes KU-L Academic Homes KUMC Academic Structure Dept. Health Info Mgt (BS) Center for Bioinformatics ITTC EECS Bioinformatics Center forHealth Informatics Operational Service Layer BioInformatics (K-INBRE coord) Medical Informatics Health Informatics UKP: clinics Public: SSDI KUH: hospital Cores/Labs Organizations/ Data sources Biospecimen Repositories State/KHPA: Medicaid+ CRIS: trials Public: GenBank State: Exchanges Extension Centers Molecular and Cellular Processes Tissues and Organs Individuals (Patients) Populations And Society
  28. Medical and Health Informatics Vision By directly engaging in clinical and health informatics databases, we in turn learn about the delivery of care and the effectiveness of informatics methods as mechanisms for influencing care. If we can develop strong relationships with our provider, state, and clinical research organizations, we will provide a rich environment for clinical and informatics research The hospital and clinic data is our core resource Engagement in state wide data in complementary to our existing research strength in preventative medicine
  29. Share with me your “Vision” What is the vision for bioinformatics in Kansas? What are the strongest stories and linkages we can tell or relationships we should build across campuses? What projects might we pursue to make contributions to informatics as a discipline versus providing clinical translational and other research support?
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