Translational Informatics: Enabling Knowledge-Driven Healthcare
The 2nd International Conference on Translational Biomedical InformaticsTaicang, China, September, 2013
Philip R.O. Payne, Ph.D.Associate Professor and Chair, Biomedical Informatics (College of Medicine)Associate Professor, Health Services Management and Policy (College of Public Health)Associate Director for Data Sciences, Center for Clinical and Translational ScienceExecutive-in-residence, Office of Technology Transfer and Commercialization
Outline Motivation
The promise of translation The evolution from reductionism to systems thinking A central dogma for Biomedical Informatics
Exemplary Trends Creating learning healthcare systems Precision medicine Big data
Next Steps Strategic research foci Implementation science Workforce development
What’s Possible…
Discussion
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Outline Motivation
The promise of translation The evolution from reductionism to systems thinking A central dogma for Biomedical Informatics
Exemplary Trends Creating learning healthcare systems Precision medicine Big data
Next Steps Strategic research foci Implementation science Workforce development
What’s Possible…
Discussion
3
Basic Science
Clinical Research
Clinical and Public Health
Practice
Clinical and Translational Science (CTS): Translation in the Context of Biomedicine
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KnowledgeGeneration
Common information needs, including: Data collection and
management Integration Knowledge
management Delivery Presentation
Application
ContinuousCycle
T1
T2
The drive for CTS has been catalyzed by two major factors: Extending timeline associated with the new therapy discovery pipeline Data “tsunami” facing the life sciences
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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.
Part of the “Puzzle”: Linking Molecules and Populations
A Catalyst: From Reductionism to Systems Thinking
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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 major
problems with this type of approach Systems biology/medicine
Reductionist approach to data, information and knowledge management is still prevalent HIT vs. Informatics Informatics sub-disciplines
A Foundational Framework: An Emerging Central Dogma for Informatics
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Data Information Knowledge
+ Context + Application
This applies across driving problems: Biological Clinical Populations
Outline Motivation
The promise of translation The evolution from reductionism to systems thinking A central dogma for Biomedical Informatics
Exemplary Trends Creating learning healthcare systems Precision medicine Big data
Next Steps Strategic research foci Implementation science Workforce development
What’s Possible…
Discussion
8
Building an Argument for Translational Informatics: Current Trends
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Learning Healthcare Systems
• Instrumenting the clinical environment
• Generating hypotheses
• Creating a culture of science and innovation
Precision Medicine
• Rapid evidence generation cycle(s)
• ‘omics’• Analytics/decision
support
Big Data• System-level thinking• Data science
Integrated and High Performing
Healthcare Research and Delivery Systems
Learning from every
patient encounter
Leveraging the best
science to improve care
Identifying and solving
complex problems
Rapid Translation
Building an Argument for Translational Informatics: Current Trends
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Learning Healthcare Systems
• Instrumenting the clinical environment
• Generating hypotheses
• Creating a culture of science and innovation
Precision Medicine
• Rapid evidence generation cycle(s)
• ‘omics’• Analytics/decision
support
Big Data• System-level thinking• Data science
Learning from every
patient encounter
Leveraging the best
science to improve care
Identifying and solving
complex problems
Integrated and High Performing
Healthcare Research and Delivery Systems
The Learning Healthcare System Dialogue
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Clinical InformaticsPublic Health Informatics
Translational BioinformaticsClinical Research Informatics
The Learning Healthcare System: A BMI Perspective
Instrument Patient Encounters
(Data + Tissue)
Generate Hypotheses
Verify and Validate Hypotheses
Formalize Evidence
Apply Evidence
Improve Patient Care
(Quality + Outcomes)
Learn from every patient encounter so that we can improve their care, their family’s
care, and their community’s care
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Multi-dimensional Data and the Learning Healthcare System
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Molecular Phenotype
Environment
Enterprise Systems and Data Repositories:EHR, CTMS, Data Warehouses
Emergent SourcesPHR, Instruments, Etc.
What Happens When We Move Beyond Organizational Boundaries?
Organization 1 Organization 2
Organization 3
Creating Virtual Organizations
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Numerous Challenges to Creating Learning Healthcare Systems
High performance systems require rapid adaptation
Increasing demand for better, faster, safer, more cost effective therapies
Simultaneous demand for increased controls over secondary use of clinical data
Artificial partitioning of access to data for knowledge generation purposes
Critical overlaps and potential sources of conflict between these factors
Regulatory, Technical and Cultural BarriersBetween Data and Knowledge Generation
Care Providers
ResearchersHIT +
Biomedical Informatics
Clinical InvestigatorsCI, Imaging, CRI, TBI, PHI
Bioinformatics, TBI, CRI
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Building an Argument for Translational Informatics: Current Trends
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Learning Healthcare Systems
• Instrumenting the clinical environment
• Generating hypotheses
• Creating a culture of science and innovation
Precision Medicine
• Rapid evidence generation cycle(s)
• ‘omics’• Analytics/decision
support
Big Data• System-level thinking• Data science
Learning from every
patient encounter
Leveraging the best
science to improve care
Identifying and solving
complex problems
Integrated and High Performing
Healthcare Research and Delivery Systems
Precision or Personalized Medicine: The Four P’s
Predictive Preventive
Personalized Participatory
Personalized Healthcare
Use bio-marker technologies to predict risk of disease
Use risk profile to plan preventive care delivery
Design and deliver adaptive therapies
Patients are activelyinvolved in healthcare
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Enabling Precision Medicine with BMI
Challenges: Capture, represent and
manage high-throughput, multi-dimensional phenotypic data
Hypothesis discovery Rapid clinical study design and
execution Socio-cultural frameworks
and human factors Multi-scale computation and
analytics
Delivery and observation of clinical care
Hypothesis generation and
testingClinical research
Goal = generate and deliver evidence necessary to enable the provision of personalized healthcare
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Building an Argument for Translational Informatics: Current Trends
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Learning Healthcare Systems
• Instrumenting the clinical environment
• Generating hypotheses
• Creating a culture of science and innovation
Precision Medicine
• Rapid evidence generation cycle(s)
• ‘omics’• Analytics/decision
support
Big Data• System-level thinking• Data science
Learning from every
patient encounter
Leveraging the best
science to improve care
Identifying and solving
complex problems
Integrated and High Performing
Healthcare Research and Delivery Systems
Reasoning on Big Data Is Hard…
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Unexpected problems Algorithms behave differently Applicability of convention
metrics P-values don’t mean allot
in petabyte scale data Signal vs. noise
Detection Understanding of
patterns
Physical computing Data storage Computational performance
But the Promise of Big Data is Significant!
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“Sergey Brin’s Search for a Parkinson’s Cure” Wired Magazine, July 2010
Leveraging Google’s Computational Expertise to Mine Big Data Distributed computing Reasoning across
heterogeneous data types Exchanging traditional
measures of result validity for the predictive power of increasingly large data sets
Resulting in differential time scales to generate analogous results
6 months vs. 8 years
Outline Motivation
The promise of translation The evolution from reductionism to systems thinking A central dogma for Biomedical Informatics
Exemplary Trends Creating learning healthcare systems Precision medicine Big data
Next Steps Strategic research foci Implementation science Workforce development
What’s Possible…
Discussion
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Next Steps: Achieving the Vision of Translational Informatics
Strategic Research Foci
Implementation Science
Workforce Development
Translation + Systems Thinking
Strategies & Future Directions
• Answering people-centric questions:
• Workflow• Usability• Software Design Patterns
• True platform integration:• SOA and Cloud Computing• Semantic web• Knowledge engineering• Visualization and HCI
• Reasoning:• Data mining• Text mining/NLP• Data integration• Knowledge discovery
• Enable all stakeholders to ask and answer questions
• Includes informaticians
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Implementation Science and Workforce Development: 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
(including basic and translational scientists, clinical researchers, and informaticians)
Incorporation of human and cognitive factors in all aspects of projects
Biomedical Informatics ≠ EngineeringSystems-level Approaches To Interoperability and Usability Are Essential
Outline Motivation
The promise of translation The evolution from reductionism to systems thinking A central dogma for Biomedical Informatics
Exemplary Trends Creating learning healthcare systems Precision medicine Big data
Next Steps Strategic research foci Implementation science Workforce development
What’s Possible…
Discussion
26
High Throughput Hypothesis Generation
Asking and answering important questions in large scale, multi-dimensional data sets
Challenges: Heterogeneity of data sets Availability of knowledge resources
that can be used to annotate targeted data
Methods: Constructive induction
Outcomes: Able to identify novel hypotheses
relating bio-molecular markers and clinical phenotypes that may be able to inform diagnostic or therapy planning approaches to multiple cancers
Phenotype
Bio-molecular MarkersBiospecimens
Putting Conceptual Knowledge to Work:Constructive Induction (CI) & Hypothesis Generation
Conceptual Knowledge Constructs (CKCs)• Conceptual knowledge-anchored concepts + relationships• Higher order constructs (multiple intermediate concepts)• Controls for concept granularity (search depth)• Basis for inference of hypotheses concerning relationships between data elements
Experimental Context: CLL Research Consortium
NCI-funded Program/Project (PO1) Translational research targeting Chronic Lymphocytic Leukemia
(CLL) Established in 1999 Cohort of over 6,000 patients Comprehensive phenotypic and bio-molecular data sets, as well as
bio-specimens
8 participating sites
Informatics platform: Research networking Clinical trials management Correlative data management Bio-specimen management
Multi-part CI Evaluation Study in CLL
(1) Efficacy (2) Verification & Validation
(3) Mining Domain
Literature
CKC Evaluation• 108 data elements• 822 UMLS concepts• 5800 CKCs• 5 SMEs• Random sample (250)
• 86% valid• 90% “meaningful”
Search depth controls
TOKEn browser
Automated lit. queries
• Random sample (50)
SME “gold standard”
•Support metric Critical
relationship• support metric• “meaningful”• Significant correlation1. Payne PR, Borlawsky T, Kwok A, Dhaval R, Greaves A. Ontology-anchored Approaches to Conceptual Knowledge Discovery in a
Multi-dimensional Research Data Repository. AMIA Translational Bioinformatics Summit Proc. 2008.2. Payne PR, Borlawsky T, Kwok A, Greaves A. Supporting the Design of Translational Clinical Studies Through the Generation and
Verification of Conceptual Knowledge-anchored Hypotheses. AMIA Annu Symp Proc. 2008.3. Payne PR, Borlawsky T, Lele O, James S, Greaves AW. The TOKEN Project: Knowledge Synthesis for in-silico Science. Journal
of American Medical Informatics Association (JAMIA). 2011
Mining CLL literature
• Medline, 2005-2008
Comparison•Literature-based
CKCs•Ontology-based CKCs
Critical findings• No overlap• Differing granularity
• More timely (SMEs)
CKC Visualization
Cytogenetic & Chromosomal abnormalities
Bio-molecular Products
HematologicMalignancies
Bone Marrow Morphology
Tissues of Origin
Solid Tumors
Myelogenous Malignancies
TOKEn CKC Network: CLL Research Consortium Metadata
Cytogenetic Abnormalities
TreatmentResponse
Bone Marrow Morphology
Lymphomas
Leukemia's
Chromosome Loss
Laboratory Findings
Protein Expression
Molecular Abnormalities
Tissues of Origin
Tissues of Origin
TOKEn CKC Network: Semantic Partitions
Critical Dimensions of this Project…
Focus on a translational informatics approach to knowledge generation
Based upon a systems-level conceptual modelLeverages data generated during clinical care to support
hypothesis generation (learning healthcare system)Deals with big-data (3 V’s)Targets hypotheses that can support adaptive therapies
for CLL Involves a multi-disciplinary research team with cross-
cutting Biomedical Informatics acumenSupported by rigorous implementation science principles
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Outline Motivation
The promise of translation The evolution from reductionism to systems thinking A central dogma for Biomedical Informatics
Exemplary Trends Creating learning healthcare systems Precision medicine Big data
Next Steps Strategic research foci Implementation science Workforce development
What’s Possible…
Discussion
34
A Multi-Scalar Approach to Knowledge Synthesis
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Collaborators: Peter J. Embi, MD, MS
Albert M. Lai, PhD
Kun Huang, PhD
Po-Yin Yen, RN, PhD
Yang Xiang, PhD
Marcelo Lopetegui, MD
Tara Borlawsky-Payne, MA
Omkar Lele, MS, MBA
Marjorie Kelley
William Stephens
Arka Pattanayak
Caryn Roth
Andrew Greaves
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):
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Thank you for your time and attention!• [email protected]• http://go.osu.edu/payne