biomedical informatics: the next 10 years of innovation

Biomedical Informatics: A Vision for the Next Decade of Innovation

Philip R.O. Payne, PhD, FACMI

Professor and Chair, College of Medicine, Department of Biomedical InformaticsProfessor, College of Public Health, Division of Health Services Management and Policy

Associate Director for Data Sciences, Center for Clinical and Translational ScienceCo-Director, Bioinformatics Shared Resource, Comprehensive Cancer Center

Executive-in-residence, Office of Technology Commercialization and Knowledge Transfer

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Outline

1) The changing health and biomedical science landscape Healthcare Transformation Big Data Systems Thinking Translational Science

2) Responsive trends in Biomedical Informatics (BMI) Translational bioinformatics in silico Hypothesis Discovery Evidence Generating Medicine (and the Learning Healthcare System) Cognitive and Predictive Analytics Workflow and Human Factors Implementation Science Workforce Development

3) Discussion An emerging central dogma for BMI The evolution of the Academic Enterprise

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, eGEMS

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

Corporate: Epic Systems, IBM, Signet Accel LLC (Founder and President)

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Outline




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Healthcare Transformation (1)• Healthcare is undergoing its most significant evolution

since the launch of Medicare in 1965

• Factors:• 2.8T industry (18% of GDP)• Operationalization of the Affordable Care Act• Currently, 20% of insurance premiums go to administrative overhead• Preventative care not a priority• 25% of admissions result in medical care that harms patients, 90% of

the time regular analytical methods don't detect these events• 750B in avoidable healthcare costs• Healthcare providers are accumulating 85% more data than 2 years

ago• 45% of this data is imaging• IDC estimates 80% of data is unstructured

• Data is changing how we deliver healthcare:• Integration of personal monitors/sensors, EHRs, and predictive

modeling for decision making• Focus on reducing costs and increasing quality• HDI (health data initiative) @ HHS is focused on making data open and

available

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Healthcare Transformation (2)

• Impact of healthcare reform• Exposing fundamental gaps• Threatening long-standing advantages• Redrawing competitive landscape• Rewriting core business models• Shifting balance of power• Creating entirely new players

• Fundamental problem in healthcare today is a bad business model (misaligned incentives, etc.)

• What is the most important data to help enable/drive reform to the healthcare business model?

• Imagine a world where measurement has become easy!

• Catalyze desire and ability to learn• Unleash the power of observation• Tightly link research and practice

A Renewed Focus on Big Data: The 3 V’s

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Volume

Velocity

Variability

Any data set where conventional analytical approaches are not sufficient to generate insights and/or actionable knowledge

Sources of Big Data in the Health and Biomedical Sciences

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Molecular Phenotype

Environment

Enterprise Systems and Data Repositories:EHR, CRMS, Data Warehouse(s)

Emergent SourcesPHR, Instruments, Etc.

UbiComp (Sensors)

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

An Evolution 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 benefits of alternative approach (systems thinking) approaches Systems biology/medicine Network theory

Reductionist approach to data, information and knowledge management is still prevalent HIT vs. Informatics Informatics sub-disciplines

Basic Science

Clinical Research

Clinical and Public Health

Practice

Translational Science: From Lab to Laptop

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KnowledgeGeneration

Common informatics needs, including: Data collection and

management Data Integration Knowledge management Information delivery

(including visualization and HCI)

Advanced analytics Application

ContinuousCycle

T1

T2

The drive for Translational Science 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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Outline




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BMI: A Sampling of Where We’ve Been Over the Past 60+ years Basic Science

Standards and data representation Knowledge engineering Cognitive and decision science Human factors and usability Computational biology

Applied Science Clinical Decision Support Systems (CDSS) Clinical Information Systems (incl. EHRs) Consumer-facing tools (incl. PHRs) Bio-molecular data analysis “pipelines”

At the Intersection of Basic and Applied Science Information Retrieval (IR) Text Mining and Natural Language Processing (NLP) Visualization Image Analysis

AI in Medicine

Computers in Medicine

Medical Informatics

Biomedical Informatics

An

Evo

lving

No

men

clature…

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BMI: A Vision for the Next 10+ Years

1) Translational bioinformatics

2) in silico Hypothesis Discovery

3) Evidence Generating Medicine Situated in the Learning Healthcare

System

4) Cognitive and Predictive Analytics

5) Workflow and Human Factors

6) Implementation Science

7) Workforce Development

Making Sense of High-Throughput Data

Delivering Knowledge-Based Healthcare

Informatics as an Intervention

Training The Future Health and Biomedical

Workforce

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.

A Solution to the Biological Data Tsunami: Linking Molecules and Populations

Translational Bioinformatics (TBI) In Action: Generating Actionable Knowledge

Slide courtesy of: Marcelo A. Lopetegui, MD; James Chen, MD

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in silico Hypothesis Generation

Cytogenetic Abnormalities

TreatmentResponse

Bone Marrow Morphology

Lymphomas

Leukemia's

Chromosome Loss

Laboratory Findings

Protein Expression

Molecular Abnormalities

Tissues of Origin

Tissues of Origin

Semantic Reasoning Across Bio-Molecular and Clinical Data in CLL

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Evidence Generating Medicine (EGM)

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EGM in Action: Instrumenting the EHR

Slide courtesy of: Marcelo A. Lopetegui, MD; Randi Foraker, PhD

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Cognitive Systems Analytics Model

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Enabling the Identification and Use of “Care Trajectories” to Optimize Outcomes and Cost

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From Predictive Analytics to Decision Support

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Cognitive Systems Analytics in Action: Tailored Clinical Decision Support

Slide courtesy of: Marcelo A. Lopetegui, MD; Tim Hewett, PhD

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Workflow and Human Factors

Implementation Science: Understanding and 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 (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

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”

Rethinking Training: Aligning Workforce Development Plans with Roles and Responsibilities

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Mapping Competencies to Career Trajectories

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Implementation of competency-based curricula

Emphasis on mastery, with practical applications at all levels

Leveraging emergent educational paradigms

• “reverse classroom model”

• Asynchronous content delivery

• Project-based “threads”

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

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Outline




Towards a Foundational Framework: An Emerging Central Dogma for Informatics

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This applies across driving problems: Biological Clinical Populations

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A Few Thoughts Regarding the New Academic Enterprise

Traditional Model Emerging Model

Departments and Divisions Multi-disciplinary Centers and Institutes

Tuition, Grant, and Service Revenue

Technology Transfer Revenue, Public-Private Partnerships, Contracts, Multi-Center Consortia

Separation of Science and Service

Service as Science:• Institutional• Community

Publications and Presentations

Commercialization, Translation into Healthcare Delivery Organizations

Scholarly Home

Revenue

Dissemination

Culture

How To Achieve Balance?

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A Survival Guide for BMI: 5 Key Points1) Fully embrace interdisciplinary:

Structure Function Competency-based Training

2) Pursue emerging (or remerging) research foci: Data science Health services and quality improvement Decision science and support (in the context of “Big Data”) Human factors and workflow Integrating patients and communities into the healthcare and research “fabric”

3) Engage with health system(s): Analytics Workflow and human factors Transformation

4) Develop robust technology transfer and commercialization agendas Partnerships and networking “De-risking” technologies

5) Adapt strategies from the private sector Identify and place disproportionate emphasis on “blue oceans” Behave like a start-up (speed, agility, “real artists ship”)

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A Final Word on BMI Training

March 26, 2014: http://www.usnews.com/education/best-graduate-schools/articles/2014/03/26/consider-pursuing-a-career-in-health-informatics

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Collaborators: Peter J. Embi, MD, MS

Albert M. Lai, PhD

Kun Huang, PhD

Po-Yin Yen, RN, PhD

Tara Borlawsky-Payne, MA

Omkar Lele, MS, MBA

Marjorie Kelley, MS

Bobbie Kite, PhD

Cartik Saravanamuthu, PhD

Tasneem Motiwala, PhD

Zach Abrams

Kelly Regan

Arka Pattanayak

Andrew Greaves

Marcelo Lopetegui, MD, MS

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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“Information liberation + new incentives = rocket fuel for innovation” – Aneesh Chopra (The Advisory Board Company)

Philip R.O. Payne, PhD, [email protected]

"Without feedback from precise measurement, invention is doomed to be rare and erratic. With it, invention becomes commonplace” – Bill Gates (2013 Gates Foundation Annual Letter)

“Data is beyond simply quantifying, it is seeing measurement as the intervention” – Carol McCall (GNS Healthcare)

Slides: http://go.osu.edu/InformaticsNext10Years

biomedical informatics: the next 10 years of innovation

Health & Medicine