applied biomedical informatics: data-driven approaches to discovery and practice

Applied Biomedical Informatics: Data-Driven Approaches to Discovery and Practice

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

Director, Translational Data Analytics @ Ohio State, Discovery Themes InitiativeAssociate Director for Data Sciences, Center for Clinical and Translational Science

Co-Director, Bioinformatics Shared Resource, Comprehensive Cancer Center

COI/Disclosures Federal Funding: NCI, NLM, NCATS, AHRQ Additional Research Funding: BAH, SAIC, Rockefeller Philanthropy

Associates, AcademyHealth, Pfizer, Hairy Cell Leukemia Foundation 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 Minnesota, Northwestern University, Stonybrook University, University of Kentucky

International Partnerships: Soochow University (China), Fudan University (China), Clinica Alemana (Chile), Universidad de Chile (Chile)

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

Editorial Boards: Journal of the American Medical Informatics Association, Journal of Biomedical Informatics, eGEMS, BMC Medical Informatics and Decision Making (Section Editor, Healthcare Information Systems)

Study Sections: NLM (BLIRC), NCATS (formerly NCRR) Corporate: Signet Accel LLC (Founder), Signet Innovations LLC (Advisor),

Futurety, Illumina, Aver Informatics, Philips Healthcare (China), Epic, IBM

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Today’s Discussion

Emerging trends in Healthcare and Informatics

Approaching informatics as an applied science

Current research and development

The future of our field and data-driven health

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

1. Speed and Agility Can and Will Become Critical

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2. Health Data Will Become Ubiquitous and Extend Beyond the Clinic and Hospital

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3. Data-driven Approaches to Population Health Will Create True Value for Patients, Providers, and Payers

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4. Disruption Will Come From Outside of the Health and Life Science Communities

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5. Open Innovation Will Reshape Healthcare Delivery, Workforce Development, and Economics

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6. Biomedical and Health Informatics Sub-disciplines and Pre-fixes Will Become Obsolete

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"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)

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BMI (1): A Historical Perspective on the Evolution of Biomedical Informatics 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 Evolving N

omenclature…

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BMI (2): What Will We Focus on Next?

Translational bioinformatics in silico Hypothesis Discovery Evidence Generating Medicine Cognitive and Predictive Analytics Workflow and Human Factors Implementation Science Workforce Development

Making Sense of High-Throughput Data

Delivering Knowledge-Based Healthcare

Informatics as an Intervention

Training The Future Health and Biomedical

Workforce

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BMI (2): What Will We Focus on Next?

Translational bioinformatics in silico Hypothesis Discovery Evidence Generating Medicine Cognitive and Predictive Analytics Workflow and Human Factors Implementation Science Workforce Development

Making Sense of High-Throughput Data

Delivering Knowledge-Based Healthcare

Informatics as an Intervention

Training The Future Health and Biomedical

WorkforceApplications of Informatics to Pragmatic Problems

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How Do We Differentiate Applied Biomedical Informatics? Design Thinking and Innovation

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

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Three Example Applied Biomedical Informatics Projects Underway at OSU

1) TOKEN: high throughput in silico hypothesis discovery for translational medicine

2) TimeCAT: instrumenting the healthcare environment to support HIT optimization

3) CIELO: support an open innovation model for collaborative and team science

Accelerating Hypothesis Generation, Testing, and Translation Using Existing Data Resources

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TOKEN (1): An in silico Hypothesis Discovery Pipeline for Adaptive and Combination Therapies

Translational Ontology-anchored Knowledge Discovery Engine (TOKEN)

in silico hypothesis discovery platform TOKEN platform initial verified and

validated in the context of the NCI-funded Chronic Lymphocytic Leukemia Research Consortium (CLL-RC)

Current efforts have focused on extending the the TOKEN framework to create a novel scoring metric to identify and prioritize combination therapies in terms of their shared drug concepts and proximity to disease-related concepts Leveraging melanoma as an

experimental contextPayne PR, Borlawsky TB, Lele O, et al. The TOKEn project: knowledge synthesis for in silico science. J Am Med Inform Assoc 2011;18(Suppl 1):i125–31.

TOKEN (2): Leveraging Constructive Induction (CI) for Discovery “Big Data” Science

TOKEN (3): Applying the TOKEN “Pipeline” to Discovery Rational Combination Therapies for Melanoma

Regan, K., Raje, S., Saravanamuthu, C., & Payne, P. R. (2014). Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma. Studies in health technology and informatics, 216, 663-667.

TOKEN (4): Exemplary Results

Top ten ranked non-BRAF inhibitor drugs hypothesized for use in combination with BRAF inhibitor drugs.

DCS = Drug Combination ScoreLog-2 transformed values shown

Optimizing Clinical Workflow and Human-Computer Interaction Through Time Motion Studies

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TimeCAT (1): Realizing HIT Value Through Data-Driven Clinical Workflow Optimization Fundamental Goals

Increase patient safety Improve productivity and efficiency Evaluate quality of care Manage clinician workload and resource allocations

Challenges: Common definitions and measures for Time Motion Studies (TMS) Lack of consistent definitions and understanding of what constitutes clinical workflow

analysis

Lopetegui MA, Yen PY, Lai AM, Jeffries J, Embi PJ, Payne PR. Time Motion Studies in Healthcare: What are we talking about? J Biomed Inform. 2014 Jun;49:292-9. PMC4058370

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Task 1

Task 2

Task 3

Aim: Identify critical areas requiring normalization and gain awareness of the state of the art in TMS.

Aim: Make a time capture tool available to TMS researchers, and use it as means of testing, distributing proposed methods, and collect user’s feedback.

Aim: Propose standards regarding human observers’ training, develop a realistic and appropriate inter-observer reliability assessment score, unify approaches for multitasking scenarios and capturing interruptions.

Aim: Facilitate results aggregation across sites, workflow comparisons and knowledge discovery by solving definitions inconsistencies.

Aim: To move forward in the analysis of TMS produced data: from a simple average time on tasks, to a comprehensive quantitative workflow analysis driven by TMS data.

TimeCAT (2): Creating a Dynamic, Highly Usable, and Reproducible TMS Platform

Lopetegui M, Yen P, Lai AM, Embi PJ, Payne PR. Time Capture Tool (TimeCaT): Development of a Comprehensive Application to Support Data Capture for Time Motion Studies. AMIA Annu Symp Proc. 2012; 596-605. PMC3540552

Lopetegui MA, Bai S Yen PY, Lai A, Embi PJ, Payne PR. Inter-Observer Reliability Assessments in Time Motion Studies: the Foundation for Meaningful Clinical Workflow Analysis. AMIA Annu Symp Proc. 2013. PMC3900222

TimeCAT (3): Enabling Systematic Data Capture, Visualization, and Comparison

Comparing Multiple Observers

Visualizing ResultsComparing Multiple Workflow Models

Creating an Open Innovation Platform for Collaborative and Team-based Science

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CIELO: Enabling Collaborative Data Analytics in Patient-Centered ResearchProject Goals:

1) Provide members of the research community with access to an open-source/-standards compliant commons for data analysis and software sharing

2) Reduce time and cost of research while enhancing the reproducibility and transparency of data analysis

3) Evolve and meet emerging community needs

Blue-Sky (Merriam Webster Dictionary):• Not grounded in the realities of the present• Visionary <blue–sky thinking>

In Collaboration With

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CIELO (1): Extending and Enhancing Existing Technologies

Sharing of Software and

Data Sets

Social Engagement

Discovery and Reuse

Github/Gitlab

Activity FeedsDiscussion Forums

FolksonomySemantic Search

Partitioning of access Bundling code and data Data model harmonization Cross-linkage (URIs/APIs)

Project-level feeds Linkage to metadata

Current ontologies Linkage to social functions

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CIELO (2): Enabling A Data Commons “Life Cycle”

User “Workspaces”

Social Search and Interaction“Bundling” of Data and Code

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1) Extension of CIELO with the ResearchIQ metadata annotation and search engine

2) Integration with pScanner distributed execution tooling (APIs)

3) Use-case driven verification and validation

CIELO (3): Adopting and Adapting Metadata And Distributed Execution Enhancements

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CIELO (4): Creating Value for the Open Science Community

Research Reproducibility Scalable and Elastic Environment for Collaboration Ability to Support Public-Private Partnerships Economies-of-scale

http://cielo.bmi.osumc.edu

http://cielo.bmi.osumc.edu/

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Placing These Projects in a Broader Context…

in silico Discovery of Knowledge

Optimizing Human-Computer Interaction

An Open Innovation Platform for Team

Science

TOKEN

CIELO

Accelerating EvidenceGeneration and Validation

Creating Data Liquidity to Fuel

Future Discovery

TimeCAT

Optimizing the Ability ofHIT to Enable EBM

Virtuous Cycle

Driving Biological and/orClinical Problems

Data Resources

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“No Outcome, No Income” – Eric Topol

Anticipating and Embracing Evolution in Technologies and Information Needs Is Critical…

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Structural Evolution: Where Does a Contemporary Informatics Model Fit in the Emerging Academic Healthcare Enterprise?

Traditional Model Emerging ModelDepartments and Divisions Multi-disciplinary Centers and

InstitutesTuition, 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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Scientific Evolution: What Should Our Scholarly Foci Be?1) 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) Adapting strategies from the private sector Identify and place disproportionate emphasis on “blue oceans” Behave like a start-up (speed, agility, “real artists ship”) Rapid translation of technologies to the “market”

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Educational Evolution: Creating an Informatics Capable Workforce

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

Two Final Thoughts (1): Behaving Like A High Performance System and Evolving Gracefully Requires A Systems Approach Three characteristics of a

high performance system:1) Leverage data to identify

problems and opportunities2) Design reproducible

solutions3) Implement those solutions

Mastering the art of designing and implementing

solutions is the greatest challenge facing the field of

Biomedical Informatics

Two Final Thoughts (2): Now is the Time for Applied Biomedical Informatics Innovation!

Technology as the Primary Driver For Research, Education, And Practice

AcknowledgementsCollaborators: Peter J. Embi, MD, MS

Albert M. Lai, PhD

Randi Foraker, PhD

Kun Huang, PhD

John C. Byrd, MD

William E. Carson, MD

Omkar Lele, MS, MBA

Tara Borlawsky-Payne, MA

Marcelo Lopetegui, MD, MS

Funding: NCI: R01CA134232, R01CA107106,

P01CA081534, P50CA140158, P30CA016058

NCATS: U54RR024384

NLM: R01LM009533, T15LM011270

AHRQ: R01HS019908

Rockefeller Philanthropy Associates

Hairy Cell Leukemia Foundation

Academy Health – EDM Forum

Laboratory for Knowledge Based Applications and

Systems Engineering (KBASE)

http://u.osu.edu/kbase/



Philip R.O. Payne, PhD, [email protected]@prpayne5www.slideshare.net/prpayne5

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applied biomedical informatics: data-driven approaches to discovery and practice

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