With the upcoming move to ICD-10 Procedure Codes across the world, information flow will reach many new recipients to improve the world's health conditions!

1. Designing for Innovation: Interventional Informatics
and the Healthcare Information Age
Philip R.O. Payne, PhD, FACMI
Professor and Chair, College of Medicine, Department of Biomedical Informatics
Professor, College of Public Health, Division of Health Services Management and Policy
Director, Translational Data Analytics @ Ohio State
Associate Director for Data Sciences, Center for Clinical and Translational Science
Co-Director, Bioinformatics Shared Resource, Comprehensive Cancer Center

2. COI/Disclosures
 Federal Funding: NCI, NLM, NCATS
 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 Minnesota, Northwestern University
 International Partnerships: Soochow University (China), Fudan University
(China), Clinical Alemana (Chile), Universidad de Chile (Chile)
 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)
 Corporate: Signet Accel LLC (Founder), Signet Innovations LLC (Advisor),
Futurety, Illumina, Aver Informatics, Philips Healthcare, Epic, IBM

3. A Roadmap for Today’s Talk
Setting
“The Stage”
Current
Opportunities
for Innovation
What’s
Next…
 Healthcare
transformation
 HIT and data
landscape
 Informatics as the
intervention
 Data analytics and
decision science
 Interactive decision
support
 Knowledge-based
healthcare
 Data “liquidity”
 Creating an evidence
generating medicine
system
 BMI and data
analytics at OSU

4. Setting
“The Stage”

5. A Unique Confluence of Trends and Capabilities That
Will Define the Future of Healthcare
Healthcare
Transformation
Evolving HIT
and Data
Landscape
Design
Thinking
Changing culture,
incentives, and
business model(s)
Advent of the “HIT
and Big Data Age”
Systems Approach
to Innovation in
Complex
Environments

6. Healthcare
Transformation
 We are beginning to address
fundamental challenges facing
the US healthcare system:
 Misalignment of economic
incentives
 Intrinsic inefficiencies
 Transactional focus
 Access
 Workforce
 How to fix a fragmented system?
 Delivery
 Technology
 Research vs. Practice
 What is the role of informatics
and data analytics in terms of
catalyzing solutions to driving
problems in the health and life
sciences? Source: http://theincidentaleconomist.com

7. Evolving HIT and Data Landscape
Characteristics Before The Printing
Press
After The Printing
Press
Cost High
Printed materials only
available to the
extremely wealthy
Low
Printed materials
become cost effective
for general public
Ubiquity Low
Copies of printed
materials had to be
transcribed by hand,
limiting number of
instances
High
Mass production of
printed materials leads
to broad dissemination
and access
Reproducibility Low
Errors of transcription
and omission very
common
High
Systematic printing
processes ensure
fidelity of materials
The Advent of the Printing Press and the 1st Information Age
Characteristics Before HIT and Big
Data
After HIT and Big
Data
Cost High
Data sets generated
and/or curated on a
need basis
Low
Data production and
storage costs
decreasing in excess of
Moores Law
Ubiquity Low
Proprietary data
situated in vendor or
project-specific
repositories and
formats
High
Data becoming a
renewable resource
enabled by diverse re-
use scenarios
Reproducibility Low
Errors of transcription
and omission very
common
High
Linked public data
enables the creation of
“commons” model
Growth in HIT and Big Data in the Healthcare Information Age

8. Evolving HIT and Data Landscape (2):
Re-engineering Medicine Through Data Analytics

9. Rethinking the Role of Informatics and Data
Analytics: Informatics as the Intervention
Source: http://www.yourgenome.org
Effect on System
Safety and Tractability
Impact on
Targeted Problem
Comparison to
Existing Practices
Long Term Effects
on System
Critical Advantages:
 Cost
 Time
 IP/Financial “Up Side”
Average Cost = 5-6B
Duration = 15-20y
Average Cost = 200-250k
Duration = 6m-1y

10. 10
Design Thinking: A Systems Approach to Complex
Problems with Technology

11. Current
Opportunities
for Innovation

12. A Survey of Current Opportunities for Innovation:
Intersection of Healthcare Transformation, HIT, Big Data
and Design Thinking
 Creating a learning healthcare system through the
implementation of an Evidence Generating Medicine
(EGM) paradigm
 Enabling adaptive therapies at the point-of-care
 Supporting patient-centered decision making in non-
traditional settings or contexts

13. Creating an Evidence Generating
Medicine (EGM) Paradigm
Payne, Philip RO, and Peter J. Embi, eds. Translational Informatics: Realizing the Promise of Knowledge-Driven Healthcare. Springer, 2014.

14. EGM in Action (1): Instrumenting the EHR to support risk
profiling and patient-centered decision making
Pa ent
Encounter in IHIS
Clinical Database
Collected by EHR
Informa on
Warehouse
SPHERE Data Mart
Pa ent Survey
Tools
Stored To
Exported To
Materialized As
Op onal Source of
Addi onal Data
BPA w/Link to Project-
Specific Web
Applica on
Triggers
Invokes
SPHERE Web Applica on
Risk Profiling Algorithm
Pa ent-Specific Risk Profile
Data Concilia on
Visualiza on of Interac ve Risk
Report @ point-of-care
Supplies Encounter Data
Request Historic Data
Delivers
Updates on interac on
Selected Publications:
• Foraker RE, Shoben AB, Lopetegui MA, Lai AM, Payne PR, Kelley M, Roth C, Tindle H, Schreiner A,
Jackson RD. Assessment of Life’s Simple 7TM in the Primary Care Setting: The Stroke Prevention in
Healthcare Delivery EnviRonmEnts (SPHERE) Study. Contemp Clin Trials. 2014
• Roth C, Foraker RE, Lopetegui M, Kelley MM, Payne PR. Facilitating EHR-based Communication and
Understanding in a Learning Healthcare System. Proc AcademyHealth Annual Research Meeting. 2014
• Lopetegui M, Foraker RE, Harper J, Ervin D, Payne PR. Real-time Data-driven Tools for Clinicians: A
Module for Extending Functionalities within the EHR. Proc AcademyHealth Annual Research Meeting.
2014
• Foraker RE, Shoben AB, Lai AM, Payne PR, Kelley MM, Lopetegui MA, Langan M, Tindle H, Jackson RD.
Electronic Health Record-based Assessment of Cardiovascular Health. Proc AHA Annual Meeting.
2015
• Foraker RE, Kite B, Kelley MM, Lai AM, Roth C, Lopetegui MA, Shoben AB, Langan M, Rutledge N, Payne
PR. EHR-based Visualization Tool: Adoption Rates, Satisfaction, and Patient Outcomes. EDM Forum,
eGEMS, 2015.

15. EGM in Action (2): Embedding Decision Support and
Visualization Tools in Existing EHR Workflow
Interactive Risk Visualization
Patient-Centered Decision Making

16. EGM in Action (3): Impacting Decision Making and Clinical
Outcomes in At-Risk Populations
One-year changes in CVH: Intervention clinic (n=160)
One-year changes in CVH: Control clinic (n=109)
 Average age was 74 years (eligible patients ≥ 65)
 Intervention clinic was 35% black (control clinic
19% black)
 Improvements seen in the intervention clinic – but
not control clinic – for diabetes and body mass
index
Pragmatic RCT Design
(Clinic-Based Randomization)

17. From Predictive Analytics to Decision Support
Selected Publications:
• Embi PJ, Payne PR. Evidence Generating Medicine: Redefining the Research-Practice
Relationship to Complete the Evidence Cycle. Med Care. 2013 Aug; 51(8 Suppl 3):S87-91.
• Abrams Z, Markowitz J, Carson W, Payne PR. Clinically Actionable MicroRNA Expression
Profiling for Cancer Diagnostics and Therapeutic Planning. AMIA Joint Summits 2015
• Raje S, Kite B, Ramanathan J, Payne PR. Real-time Data Fusion Platforms: The Need of Multi-
dimentional Data-driven Research in Biomedical Informatics. MedINFO, 2015.

18. Bridging Molecules and Populations At The
Point-of-Care: Predictive Cancer Therapeutics
• Design:
 Cluster-based case
based reasoning
engine
 Interactive
visualization
 Used for
Identification of
adaptive therapy
strategies in
sarcoma based
upon SNP-based
“signatures”
• Observational study:
 Usability
 Perceived utility
(adoption)
 Impact on
physician decision
making

19. Taking Decision Support Into the Field: Mobile
Computing and Sports Medicine
• Design:
 Statistical risk profiling of
surgical treatment plans
(RR)
 Mobile application
 Used for patient-centered
decision making by
athletes, mediated by
athletic trainers (“in the
field”)
• Observational study:
 Usability
 Perceived utility
(adoption)
 Impact on patient
decision making
Selected Publications:
• Embi PJ, Hebert C, Gordillo G, Kelleher K, Payne PR. Knowledge Management and Informatics Considerations for Comparative Effectiveness
Research: A Case-driven Exploration. Medical Care. 2013; 51(8):S38-S44.
• Roth C, Foraker RE, Payne PR. Bringing Public Health into the Primary Care Clinic through an EHR-based Application: Lessons Learned for Public
Health and Informatics. 2014 Public Health Informatics Conference. Atlanta, GA. 2014
• Payne PR. Advancing User Experience Research to Facilitate and Enable Patient Centered Research: Current State and Future Directions. eGEMs
(Generating Evidence & Methods to Improve Patient Outcomes). 2013; 1(1):10.

20. What’s
Next…

21. The Traditional Healthcare Model and the
Role of Patients and Populations
Adapted from: Payne, Philip RO, and Peter J. Embi, eds. Translational Informatics: Realizing the Promise of Knowledge-Driven Healthcare. Springer, 2014.

22. The Alternative Model: Revisiting EGM in the
Context of The Learning Healthcare Ecosystem
Adapted from: Payne, Philip RO, and Peter J. Embi, eds. Translational Informatics: Realizing the Promise of Knowledge-Driven Healthcare. Springer, 2014.

23. What Needs to Be Done to Realize This Vision?
1) Creation of oversight and “trust fabrics” across levels or responsibility and
engagement
 Evidence and policy generators
 Providers and healthcare organizations
 Patients and their communities
2) Understanding value propositions so as to ensure appropriate levels of
engagement
 Creating incentives
 Removing barriers
3) Establishing linkages between stakeholder participation in the healthcare
system and outcome measurement
 Roles and responsibilities
 Data “liquidity”
4) Ensuring that HIT architectures and Applied Biomedical Informatics
practice adapt and adopt to these strategies

24. Intersection of Data Governance, Analytics and
Healthcare Research or Operations using EGM Paradigm
Operational
Analytics
(Understanding
Operations and
Business
Environment)
Research
Analytics
(Identifying and
Quantifying
Novel Models
and Findings)
Business Intelligence (BI)
(Tracking and Evaluation)
Data, Information, and
Knowledge Infrastructure
(Warehousing, Registries, Analysis
Platforms)
Integration
Critical Dimensions of this Model:
• BI uses known models/measures to
present data in a way that can support
business operations
• Operational analytics investigates
emergent environmental and/or
competitive phenomena internally and
externally that serve to inform strategic
decision making
• Research analytics identifies and
quantifies the relative impact of novel
models and findings
All three areas need to be coordinated
by a cross-cutting governance and
decision making model, representing
the needs of all stakeholder groups
Cross-CuttingGovernance&DecisionMaking
Practice Innovation
RapidCycleTranslation

25. Survival Guide for BMI in HIT and Data Era
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) 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”)

26. BMI and Analytics in 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?

27. TDA@OhioState: A Interdisciplinary Home
for Translational Data Analytics
Institute for Translational Data Analytics:
• Physical and virtual hub
• Shared services
• Solution factory
Active Community of Data Analytics
Education, Research, and Practice:
• Engaged faculty teams
• Trainees and curricula at all levels
• Public-private and public-public
partnerships
• Advocacy
International Recognition for Delivering
Data Analytics Solutions
Demonstrable Local, Regional, National,
and International Impact
Community • Solutions • Impact

28. Bridging Disciplines and Methods
Translational Data Analytics
The application of data analytics theories and methods to generate solutions for
real world problems
Theories and Methods
Real World
Applications
Implementation
and
Dissemination
Basic Science Applied Science Practice
Foundational data analytics strength at Ohio State
• Computational methods – machine learning
• Modeling and representation of complex data sets
• Data engineering – methods to collect, manage and transmit complex, heterogeneous data
• Sensor networks and data

29. Leveraging and
Integrating Rich Data
Assets  Over 600 faculty working in
data analytics domains
 Vibrant local and virtual
communities of data
analytics researchers,
educators and practitioners
 Among the top 15
universities for funding and
publishing in the data
analytics and decisions
science
 Data analytics education
programming across 15
colleges, including first-of-
its-kind interdisciplinary
bachelor of science
 $52.8 million state-of-the-
art translational data
analytics facility, currently in
design

30. TDA@OhioState: Initial Focus Areas
Precision AgricultureFoundations
Systems Health & Wellness Digital Humanities

31. Phase 2: Thematic Cluster
Formation and
Augmentation
Phase 1: “Bridging” Hires and
Existing Talent Activation
Phase 3: Internal
Talent Development
and Alignment
TDA@OhioState: Growing Our Faculty
 $150M investment over 5 years
 60-70 new tenure track faculty

32. TDA@OhioState: Solutions “Factory”
Design
Evaluate environment and
requirements
Define use cases and
evaluation plans
Identify funding and/or
supporting resources
Establish project management
framework(s)
Build
Design and implement
prototype solutions
Define evaluation plans and
process/outcome measures
Align technical resources and
infrastructure
Scale
Implement and report on
solution in use case defined
contexts, using evaluation plans
Deliver solution(s) to
stakeholders (internal and
external)
• Fisher College of
Business Professional
Services
• Industry Liaison Office
• Proposal
Development Center
• Ohio Super Computer
Center
• Statistical Consulting
Service (analytical
methods)
• TDA@OSU Shared
Resources/Cores
• TDA@OSU Software
Development Team
• Statistical Consulting
Service (evaluation)
• Office of Technology
Commercialization
and Knowledge
Transfer
Cross-Cutting TDA@OSU Project Management Team
LifecyclePhaseand
Objectives
Leveraged
Resources

33. Partner Engagement
Identification of
“Real World” Needs
Engagement of
Faculty, Trainees,
and Staff
Generation of
Response Research
Solutions/Products
New Resources
Value Proposition Problem Definition
Team Mobilization
Translation
A Focus on Creating Responsive Research Products While
Advancing Foundational Science
 “De-risking”
technologies
 Generation of
market-based
“traction”
 Rapid-cycle
technology
transfer
 Incubation of
startups or
direct
licensing to
existing
companies
 Optimization
of institutional
“up side”

34. A Few Final
Thoughts…

35. Two Final Thoughts (1): Behaving Like A High
Performance System Requires Difficult Change
 Three characteristics of a
high performance system:
1) Leverage data to identify
problems and opportunities
2) Design reproducible
solutions
3) Implement those solutions
Mastering the art of
designing and implementing
solutions is the greatest
challenge facing the field of
BMI and Data Analytics!

36. Two Final Thoughts (2): Is It Time For
Interventional Informatics?
Technology as a diagnostic or therapeutic agent in pursuit of the triple aim…

37. Acknowledgements
Collaborators:
 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
 Marjorie Kelley, MS
 Tasneem Motiwala, PhD
 Zach Abrams
 Kelly Regan
 Andrew Greaves
 Tara Borlawsky-Payne, MA
 Marcelo Lopetegui, MD, MS
Funding:
 NCI: R01CA134232, R01CA107106,
P01CA081534, P50CA140158,
P30CA016058
 NCATS: U54RR024384
 NLM: R01LM009533, T15LM011270
 AHRQ: R01HS019908
 Hairy Cell Leukemia Research
Foundation
 Academy Health – EDM Forum
Laboratory for Knowledge
Based Applications and
Systems Engineering (KBASE):

38. “Information liberation + new incentives = rocket fuel for
innovation”
– Aneesh Chopra (The Advisory Board Company)
Philip R.O. Payne, PhD, FACMI
philip.payne@osumc.edu
@prpayne5
www.slideshare.net/prpayne5
"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)
“No Outcome, No Income”
– Eric Topol