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Transforming Healthcare through Network-Centric Biomedicine

Learn about the paradigm shift in healthcare through network-centric biomedicine, where data-driven knowledge and collaboration networks are used to revolutionize the diagnosis, treatment, and understanding of diseases.

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Transforming Healthcare through Network-Centric Biomedicine

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  1. Network-centric Biomedicne:toward a learning healthcare system Ken Buetow, Ph.D. Director Computational Science and Informatics Core Program,Complex Adaptive Systems InitiativeArizona State UniversityBiomedical Knowledge Cloud: A Network to Transform Healthcare Spring 2012 Internet2 Member Meeting

  2. The Fourth Paradigm:Data-Driven Knowledge, Intelligence and Actionable Decisions • changing the nature of discovery • hypothesis-driven versus hypothesis-generating unbiased analytics of large datasets (patterns, rules) • changing the nature of explanation • statistical probabilities versus unitary values • changing the cultural process of knowledge acquisition • large scale collaboration networks, open systems • changing knowledge application • increased quantification and decision-support systems • changing cognitive frameworks, intellectual capabilities and competencies for knowledge-intensive competitiveness in multiple domains • changing education and training Courtesy G. Poste

  3. Determinng The Molecular Basis of Disease:The Intellectual Foundation of Rational Diagnosis and Treatment Selection Molecular Pathwaysand Networks Network Regulatory Mechanisms Genomics Proteomics ID of Causal Relationships Between Network Perturbations and Disease Patient-Specific Signals and Signatures of Disease or Predisposition to Disease Courtesy G. Poste

  4. Data are the life blood of biomedicine • Diverse types • Clinical Observation • Clinical Laboratory • Imaging • Registry • Molecular Characterization • Biospecimens • Reference • Distributed sources • Research Center • Care Delivery Setting • Hospital • Practice • Laboratory • Registry • Consumer • Industry

  5. The Multiple Users and Complex Connectivities for Seamless Information Transfer in the HIT Ecosystem W. Dalton et al, Clin Cancer Res; 16 (24) December 15, 2010

  6. The Rise of Data-Driven, Data-Enabled Science and Technology • data changed by computing • computing changed by data • data are now fundamentally networked • increasing fraction of data is ‘born digital’ • ever larger data sets become increasingly unmovable with existing infrastructure • simulations using data and meta-analytics amplify the data metaverse Courtesy G. Poste

  7. Biomedicine: “fallen and can’t get up” • Impending “Pharmageddon”*: Declining R&D Productivity with Rising Costs • Healthcare ecosystem is broken • Poor understanding of the underlying biological complexity – current dominance of reductionist paradigm • Vertically integrated development model (FIPCo) vs networked model (FIPNet) that dominates other sectors • Exponential fragmentation of health information need to embrace biomedicine as SYSTEM * from M. King Jolly, Pharm.D. Quintiles, Inc. DIA 2011

  8. Biomedicine: a Complex Adaptive System“the whole is more than the sum of the parts” • Diverse stakeholders: multidimensional, interacting “ecosystem” • Industry, Academe, Government, NGOs • Physicians, Regulators, Researchers, Payors, Consumers, Public Health Officials • Biology, Chemistry, Medicine, Business, Sociology, Anthropology • Adaptive behaviors (dynamic as opposed to static) • Emergent properties (or unintended consequences) • Interdependencies • Resourses • Information

  9. Strategies for “Managing” Complexity • Networking • Differentiated functions connected though well-defined interfaces – e.g. • Biologic processes • Manufacturing • Layering • Abstracted combinations of functions into hierarchical/multidimensional strata which connect through well defined interfaces –e.g. • Quantum physics – Newtonian physics • Biologic complexity : cell, organism, society • Organizational hierarchies

  10. A military doctrine or theory of war pioneered by the United States Department of Defense. It seeks to translate an information advantage, enabled in part by information technology, into a competitive warfighting advantage through the robust networking of well informed geographically dispersed forces. This networking, combined with changes in technology, organization, processes, and people - may allow new forms of organizational behavior. Specifically, the theory contains the following four tenets in its hypotheses: A robustly networked force improves information sharing; Information sharing enhances the quality of information and shared situational awareness; Shared situational awareness enables collaboration andself-synchronization, andenhances sustainabilityand speed of command; and These, in turn, dramatically increase mission effectiveness. (Wikipedia) Network-centric “warfare”

  11. Define modules that address specific needs Connect through “well-defined electronic interfaces” Semantic Interoperability Defined syntax Defined semantics Applying CAS Principles to Facilitate Information Flow

  12. Can be heterogeneous Can restrict access Can be commodity (proprietary) components that connect at (open) defined interfaces The glue that binds parts together is metadata infrastructure Shape of boundary is defined in APIs Application Programming Interfaces

  13. Componentized knowledge representation Permits information to be “pivoted” Based on international standards Interoperability through Metadata-based “Knowledge Stack” biomedical objects data elements ontologies

  14. Idealized Modular “Framework” supportingBiomedical Research Data Liquidity hardware platform Mobile devices, Personal computers, Servers Special function applications accessible from code repositories, app stores, etc app. defined api Exposed application programming interfaces (APIs) that support connecting components defined api data resource Various digital capabilities on multiple platforms

  15. Complicating Considerations • Nature of Data • “Data Validity”: Garbage In- Garbage Out • Human Subjects Protections • Intellectual Property • Technical • Secure access • Volume/Magnitude • Need for integration • Diverse Data • Multiple Source • Need for choreography • One size does not fit all • Nature of the data to be accessed • The question one wants to answer Continuum of need mediates the need for adding layers of complexity

  16. Strategies for Addressing Complexity • Diversity of APIs that support paradigms within given communities (expose multiple “flavors” where possible) • Adding modules to address issues ONLY when necessary • Federating Access: Data control remains local • Escalating introduction of standards-based metadata • Analytics go to the data/co-reside with the data • Virtual Communities where access to individual level data is needed

  17. A Biomedical Informatics Ecosystem app app app app app • datamart • datamart app app high speedconnectivity high speedconnectivity big data resource • big data resource app app app • big data anaytics • big data analytics ultra-high speedconnectivity • SecurityServices • ServicesRegistry high speedconnectivity app app • MetadataResources • datamart app • datamart high speedconnectivity app app app app app app

  18. Escalating complexity facilitatingBiomedical Research Data Liquidity hardware platform browser Web api Web api data resource

  19. Escalating complexity facilitatingBiomedical Research Data Liquidity hardware platform browser Web api security Web api data resource

  20. Escalating complexity facilitatingBiomedical Research Data Liquidity hardware platform browser Web api security security Web api Web api data resource analytic resource

  21. Escalating complexity facilitatingBiomedical Research Data Liquidity hardware platform browser Web api MetaDataRepository (data elements) security security security Web api Web api Web api data resource data resource analytic resource

  22. Escalating complexity facilitatingBiomedical Research Data Liquidity hardware platform browser Web api MetaDataRepository (ontologies,data elements) security security security Web api Web api Web api data resource data resource analytic resource

  23. Escalating complexity facilitatingBiomedical Research Data Liquidity hardware platform browser Web api MetaDataRepository (ontologies,data elements, models) security security security Web api Web api Web api data resource data resource analytic resource

  24. Escalating complexity facilitatingBiomedical Research Data Liquidity hardware platform browser Web api MetaDataRepository (ontologies,data elements,models) ResourceRegistry security security security Web api Web api Web api data resource data resource analytic resource WorkflowManagement

  25. Escalating complexity facilitatingBiomedical Research Data Liquidity hardware platform browser Web api MetaDataRepository (ontologies,data elements,models ResourceRegistry security security security Web api Web api adapter data resource analytic resource Web api data resource WorkflowManagement

  26. Escalating complexity facilitatingBiomedical Research Data Liquidity hardware platform browser Web api MetaDataRepository (ontologies,data elements,models ResourceRegistry security security security Web api Web api adapter data resource analytic resource Web api data resource WorkflowManagement CommunityGrouper

  27. Analytical Tools Microarray Data Medical Center ResearchUnit MedicalCenter ResearchCenter ResearchCenter ResearchCenter MedicalUnit ResearchCenter ResearchCenter MedicalCenter Biospecimens Clinical Trials GME Schema Management Vocabularies &Ontologies Common Data Elements IndexService WorkflowManagement Service FederatedQuery Service Dorian GTS FederatedQuery Advertisement/ Discovery Workflow Security MetadataManagement

  28. MedicalCenter ResearchCenter ResearchCenter ResearchUnit ResearchCenter MedicalCenter Medical Unit ResearchCenter MedicalCenter ResearchCenter Common Data Elements GME Schema Management Vocabularies &Ontologies IndexService WorkflowManagement Service FederatedQuery Service Dorian GTS FederatedQuery Advertisement/ Discovery Workflow Security MetadataManagement Biomedical Knowledge Cloud (Services Infrastructure)

  29. MedicalCenter ResearchCenter ResearchCenter ResearchCenter ResearchUnit MedicalCenter MedicalUnit ResearchCenter MedicalCenter ResearchCenter Common Data Elements GME Schema Management Vocabularies &Ontologies IndexService WorkflowManagement Service FederatedQuery Service Dorian GTS FederatedQuery Advertisement/ Discovery Workflow Security MetadataManagement Biomedical Knowledge Cloud (Services Infrastructure) Biomedical Knowledge Cloud (Services Infrastructure)

  30. How do we get there from here? • Approach as Ultra Large Scale Systems problem • “City planning” as opposed to “building architecture” • “Building codes” • Over-arching framework • Incremental, problem-directed, implementation • Bias toward “working code” • Coalition of the Willing • Policy to address regulated environment and cultural barriers

  31. Summary • Approaching Biomedicine as a Complex Adaptive System may help address some of the challenges it currently faces • Information, and as such Information Technology can serve as the glue to connect the Ecosystem • It is technically feasible to create and deploy technology to exchange information within and between members of the ecosystem • A multi-stakeholder, multidimensional community will be necessary to create a sustainable ecosystem

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