Translational Informatics: Enabling Knowledge-Driven Healthcare

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Translational Informatics: Enabling Knowledge-Driven Healthcare The 2 nd International Conference on Translational Biomedical Informatics Taicang, 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 Science Executive-in-residence, Office of Technology Transfer and Commercialization

Transcript of Translational Informatics: Enabling Knowledge-Driven Healthcare

Page 1: Translational Informatics: Enabling Knowledge-Driven Healthcare

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

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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

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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

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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

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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

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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

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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

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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

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

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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

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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.

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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

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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

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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

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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

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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

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Next Steps: Achieving the Vision of Translational Informatics

Strategic Research Foci

Implementation Science

Workforce Development

Translation + Systems Thinking

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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

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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

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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

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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

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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

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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)

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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

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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

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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

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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