8/7/2019 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
8/7/2019 PhilipPayne, PhD - EGM Panel
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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
8/7/2019 PhilipPayne, PhD - EGM Panel
3/19
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
8/7/2019 PhilipPayne, PhD - EGM Panel
4/19
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
8/7/2019 PhilipPayne, PhD - EGM Panel
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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
8/7/2019 PhilipPayne, PhD - EGM Panel
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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
8/7/2019 PhilipPayne, PhD - EGM Panel
9/19
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
8/7/2019 PhilipPayne, PhD - EGM Panel
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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.
8/7/2019 PhilipPayne, PhD - EGM Panel
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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
8/7/2019 PhilipPayne, PhD - EGM Panel
16/19
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
8/7/2019 PhilipPayne, PhD - EGM Panel
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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
8/7/2019 PhilipPayne, PhD - EGM Panel
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Questions or comments?
Getting in touch:
http://bmi.osu.edu/~payne
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