translational informatics: the "glue" between basic science and clinical research
DESCRIPTION
Presentation from AMIA 2013 panel on re-engineering the clinical research enterprise.TRANSCRIPT
Biomedical Informatics: The “Glue” Between Basic Science and Clinical Research
Clinical Research Informatics: Re-Engineering the Clinical Research EnterpriseAMIA Annual Symposium, 2013
Philip R.O. Payne, PhD, FACMIAssociate 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 evolving clinical and translational science ecosystem The role of informatics in clinical and translational research The OSU Center for Clinical and Translational Science (CCTS)
Lessons from the OSU CCTS Next Steps
Emergent needs The importance of implementation science Workforce development
Discussion
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Basic Science
Clinical Research
Clinical and Public Health
Practice
Clinical and Translational Science (CTS): Linking Molecules to Populations
3
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
The Evolving CTS Ecosystem: From Reductionism to Systems Thinking
4
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
This viewpoint has traditionally influenced policy, education, research and practice
Recent scientific paradigms have illustrated major problems with this type of approach Systems biology/medicine Big data and “deep reasoning” Network theory
In response, there has been an evolution of CTS towards a systems thinking approach Policies Funding Career paths
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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“Data is beyond simply quantifying, it seeing measurement as the intervention” – Carol McCall (GNS Healthcare)
A Test-Bed: The Center for Clinical and Translational Science (OSU CCTS) was founded
in 2006, and is a collaboration among The Ohio State University (OSU)
All seven health sciences colleges Colleges of arts and sciences, business, and engineering
OSU Wexner Medical Center (OSUWMC) Nationwide Children's Hospital (NCH) Community health and education agencies Business partnerships Regional institutional networks
CTSA funded in 2008
The OSU CCTS provides financial, organizational, and educational support to biomedical researchers, as well as opportunities for community members to participate in credible and valuable research.
Focused on turning the scientific discoveries of today into life-changing disease prevention strategies and the health diagnostics and treatments of tomorrow
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Applying a Strategic Framework to Research Informatics Practice
Dynamic Informatics
Strategy
Anticipating needs
Challenging assumptions
Interpreting “signals”
Translating plans
Alignment
Learning and improving
Anticipating Needs: Simplifying Programmatic Objectives
9
Challenging Assumptions: Improving Stakeholder Access and Optimizing Resource Utilization
10
Interpreting Signals: Identifying Opportunities for Structural and Functional Improvements
• Regular environmental scans (internal and external)
• Stakeholder surveys (annual)
• Targeted workflow and ethnographic studies
11
In this context, an “Ecosystem” = …a community of interacting and highly interdependent actors, resources, and processes, which function as a cohesive and collective whole…
Translating Plans: Leveraging Partnerships and Complementary Capabilities
12
Alignment: Making Use of Existing Infrastructure and Pursuing Targeted Enhancements
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Learning and Improving: Measuring Processes and Outcomes and Providing Access to Evaluation Data
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Innovative Platform
Development
EvaluationService Line
Implementation Science: An Opportunity to Balance Science and Service
•Knowledge representation models
•Semantic reasoning algorithms•Novel architectures•Workflow modeling•Human-factors
•Informatics “translation”•Workflow modeling•Human-factors•System-level models of IT
adoption•“Research on research”
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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
Data Generation
Application AND Evaluation
of Knowledge
Unification
“4I” Values
Information-Centricity
Focusing on Context
IntegrationConnecting the
Dots
InteractivityEngaging End-Users
InnovationCreating New
Solutions
Proposed ApproachTraditional Model
Data Generation
Application of Knowledge
Linear Translation
Data Focused
ApplicationSpecific
Silos
Engineering Approach to
Design
Leveraging Existing
Technologies
CurrentTrends
Towards a “4I” Approach to Research Informatics
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Evolution To
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“Information liberation + new incentives = rocket fuel for innovation” – Aneesh Chopra (The Advisory Board Company)
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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):
