PhilipPayne, PhD - EGM Panel

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    Enhancing Evidence Generation UsingComprehensive Knowledge ManagementStrategies

    Philip R.O. Payne, Ph.D.

    Associate Professor & Chair, Biomedical InformaticsExecutive Director, Center for IT Innovation in HealthcareCo-Director, Biomedical Informatics Program, Center for Clinical and Translational ScienceCo-Director, Biomedical Informatics Shared Resource, Comprehensive Cancer Center

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    Overview

    1. Motivation

    P4 medicine and evidence generation

    Linking knowledge management and informatics

    2. A Systems Approach to Evidence Generation

    Conceptual model Knowledge resources

    Challenges and opportunities

    3. Discussion

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    Overview

    1. Motivation

    P4 medicine and evidence generation

    Linking knowledge management and informatics

    2. A Systems Approach to Evidence Generation

    Conceptual model Knowledge resources

    Challenges and opportunities

    3. Discussion

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    Delivering P4 Medicine

    Use bio-marker

    technologies topredict risk of disease

    Use risk profile to

    plan preventive caredelivery

    Design and deliver

    adaptive therapies

    Design and deliver

    adaptive therapiesPatients are actively

    involved in healthcare

    Patients are actively

    involved in healthcare

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    Informatics and P4 Medicine

    Challenges: Capture, representation and

    management of high-throughput,multi-dimensional data

    Phenotype

    Bio-molecular markers

    Environmental factors

    Patient-reported data

    Reasoning

    Hypothesis generation

    Decision support

    Rapid execution of research

    Observational

    Interventional

    Beyond organizationalboundaries!

    Delivery andobservation ofclinical care

    ReasoningResearch

    Goal = generate evidence necessary to support PHC delivery

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    Defining Knowledge Management

    Capture, represent, model, organizeand synthesize the different types ofknowledge to realize comprehensive,validated and accessible resources

    Access, share and disseminate current and case-specific knowledge tostakeholders in a usable format

    Operationalize and utilize knowledge,within existent organizational workflows, toprovide pragmatic services at the point-of-need (e.g., point-of-care decision support)

    Set of processes, methodologies and toolsaimed at maximizing organizational efficiencythrough the curation, storage, dissemination andre-use of enterprise information and experiences

    Abidi SSR. Healthcare Knowledge Management: The Art of the Possible. In: Knowledge Management for Health Care Procedures: Springer Berlin/Heidelberg; 2008, 1-20.

    Smaltz DH and RC Pinto. Organizational Knowledge Can You Really Manage It? In: Proc HIMSS Annual Conference and Exhibition, 2004.

    Slide Source: Tara Payne, Knowledge Management for Research

    Tool + Methods + Expertise

    Can support the integration and

    dissemination of heterogeneous and multi-

    dimensional biomedical data sets

    Scalability is a major challenge

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    Goal = ReplicatingGoal = Replicating Expert PerformanceExpert Performance

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    Overview

    1. Motivation

    P4 medicine and evidence generation

    Linking knowledge management and informatics

    2. A Systems Approach to Evidence Generation

    Conceptual model Knowledge resources

    Challenges and opportunities

    3. Discussion

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    Clinical InformaticsPublic Health Informatics

    Translational BioinformaticsClinical Research Informatics

    Conceptual Model: Learning from Every Patient

    Instrument PatientEncounters

    (Data + Tissue)

    Instrument PatientEncounters

    (Data + Tissue)

    Generate HypothesesGenerate Hypotheses

    Verify and ValidateHypotheses

    Verify and ValidateHypotheses

    Formalize EvidenceFormalize Evidence

    Apply EvidenceApply Evidence

    Improve Patient Care

    (Quality + Outcomes)

    Improve Patient Care

    (Quality + Outcomes)

    Learn from every patient encounter so that

    we can improve their care, their families

    care, and their communities care

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    Common Knowledge Resources

    Synthesized Data Knowledge bases

    Databases Literature Knowledge

    Collections

    CTMS

    Bio-specimenManagement

    Databases

    Research Admin. Instrumentation

    EHR

    EDW BI Platforms Registries

    ClinicalEnterprise

    ResearchEnterprise

    EducationalEnterprise

    ExternalResources

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    The hallenge: Hi -t r t t r bl

    Contemporary data sources: EHR and CTMS platforms yield high-throughput

    phenotype data

    -omics technologies generated high-throughputbio-molecular markers

    Most organizations maintain multiple bio-specimenrepositories

    Informatics research and development focusing onintegrative analyses and knowledge generation fromlarge-scale data sets is still formative

    What if we are missing critical knowledge thatcan be generated from large-scale, integrativedata sets?

    Many current approaches of problem decompositionand/or hypothesis discovery methods rely onintuitive design

    Wealth of knowledge concerning potentialrelationships between data elements distributedacross multiple types ofconceptual knowledgesources

    How to leverage these opportunities in asystematic manner

    Phenotype

    Bio-molecularMarkers

    Biospecimens

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    Toward A Solution: Knowledge-anchoredSystems Design Patterns

    Payne PR et al. Translational informatics: enabling high-throughput research paradigms. In: Physiol. Genomics 39: 131-140, 2009

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    Exemplary Barriers to theKnowledge-anchored Design Pattern

    Challenge Solution

    Domain experts with the

    technical expertise necessary to

    engage in the design process are

    often not readily available.

    Non-domain experts employ

    systematic knowledge

    engineering methods to define

    required information models.

    The use ofcentrally curated

    knowledge collections can

    make it difficult to build and

    employ local vocabularies in atimely manner.

    Methods to enable widespread

    semantic interoperability and

    model harmonization while

    retaining ability to use locallyrelevant vocabularies.

    Dhaval R, et al. Implementation of a metadata architecture and knowledge collection to support semantic interoperability in an enterprise data warehouse. Proc AMIA Annu Symp, 2008.

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    Overcoming Barriers: Socio-technicalApproaches to Enabling Comprehensive KM

    ComprehensiveKM Platformsand Practices

    OrganizationalNeeds

    Assessment

    (Top-down)

    Marketing,Communications,

    Training

    (Cross-cutting)

    Analysis of End-user

    Requirements,Workflows, and

    Attitudes

    (Bottom-up)

    Strategic plans

    Senior leaders

    Funding sources

    Workflow analysis

    Interviews

    Use cases

    Multimedia

    Workshops

    Champions

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    Overview

    1. Motivation

    P4 medicine and evidence generation

    Linking knowledge management and informatics

    2. A Systems Approach to Evidence Generation

    Conceptual model Knowledge resources

    Challenges and opportunities

    3. Discussion

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    Generating Evidence from Multi-modal KnowledgeResources

    Gender

    Ethnicity

    Age

    Weight

    Diagnosis

    Medical History

    Literature Databases

    Terminologies

    Ontologies

    Lab Tests

    Genes

    Proteins

    Biological Models

    Technologies

    Algorithms

    Integration, Management,

    Analysis, and Dissemination

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    Durable KM Strategies Are Not Based onTechnology: People and Processes are Critical

    1950-60s: Specialized computing

    facilities, programming languages,decision support, bibliographic

    databases, basic clinical documentation

    systems, first training programs

    Today: Tele-health, mobile computing,

    widespread EHR adoption, service-oriented architectures, genomic and

    personalized medicine applications,

    translational research

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    Questions or comments?

    Getting in touch:

    [email protected]

    http://bmi.osu.edu/~payne