informatics and the clinical and translational science ecosystem

Integrating Informatics Into The Clinical and Translational Science Ecosystem

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

COI/Disclosures

Federal Funding: NCI, NLM, NCATS, AHRQ

Additional Research Funding: SAIC, Rockefeller Philanthropy Associates, Academy Health, Pfizer

Academic Consulting: CWRU, Cleveland Clinic, University of Cincinnati, Columbia University, Emory University, Virginia Commonwealth University, University of California San Diego, University of California Irvine, University of California San Francisco, University of Minnesota, Northwestern University

Other Consulting/Honoraria: American Medical Informatics Association (AMIA), Institute of Medicine (IOM)

Editorial Boards: Journal of the American Medical Informatics Association, Journal of Biomedical Informatics

Study Sections: NLM (BLIRC), NCATS (formerly NCRR), NIDDK

Corporate: Epic Systems, IBM, Accelmatics (Chief Scientific Officer)

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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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Outline




Discussion

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Defining Translation

trans·la·tion (noun): an act, process, or instance of translating: as a: a rendering from one language into another; also :

the product of such a rendering b: a change to a different substance, form, or

appearance c: a transformation of coordinates in which the new

axes are parallel to the old ones

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Source: Merriam Webster Dictionary (http://www.merriam-webster.com/)

http://www.merriam-webster.com/



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

Defining Systems Thinking

Systems thinking is the process of understanding how things influence one another within a whole Approach to problem solving where "problems" are

viewed as parts of an overall system Major goal is to avoid development of unintended

consequences as a result of solving problems in isolation Promotes organizational communication at all levels in

order to avoid the “silo” effect

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Source: Wikipedia (http://en.wikipedia.org/wiki/Systems_thinking)

http://en.wikipedia.org/wiki/Systems_thinking




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An Argument For “Translational Informatics”: Bridging Translation and Systems Thinking

Improved Translation

Systems Thinking

Advances in Human

Health

Enabled by Biomedical Informatics

Extending the 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

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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Outline




Discussion

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Applying a Strategic Framework to Translational Informatics

Dynamic Informatics

Strategy

Anticipating needs

Challenging assumptions

Interpreting “signals”

Translating plans

Alignment

Learning and improving

Anticipating Needs: Simplifying Programmatic Objectives

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Challenging Assumptions: Improving Stakeholder Access and Optimizing Resource Utilization

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Interpreting Signals: Identifying Opportunities for Structural and Functional Improvements

• Regular environmental scans (internal and external)

• Stakeholder surveys (annual)

• Targeted workflow and ethnographic studies

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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

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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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Outline




Discussion

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Strategies & Future Directions: HIT

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Informatics “translation”

Holistic approach to planning, implementation and management

Adoption of knowledge management practices as a core competency

Transition to agile, lightweight technologies as the “edge” of enterprise systems

• Eliminating traditional boundaries• Focusing on economies of scale

across mission areas• Bridging applied informatics and

HIT practice• Semantics• NLP• Temporal Reasoning• IR• Visualization

• Enabling end-user self service

Strategies & Future Directions: BMI

• 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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Strategies & Future Directions: Culture Harmonization of regulatory frameworks:

Early successes related to universal bio-specimen collection projects and GWAS/PWAS study paradigms

HIT and BMI must be partners: Technology and methodological silos are major barriers

Socio-technical approach to platform adoption: Adoption means more than being on-time and under-budget

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Innovative Platform

Development

EvaluationServices

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

Differentiating Acculturation and Practice

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Steering Wheel

Pedals

Transmission

VS

Familiarity with structure/function Conceptual knowledge Minimal strategic/procedural

knowledge

Emphasis on strategic/procedural knowledge

Demonstrable efficacy and resiliency with regard to practice

Outline




Discussion

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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 Translational Informatics

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Evolution To

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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