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 InformaticsProfessor, College of Public Health, Division of Health Services Management and Policy

Director, Translational Data Analytics @ Ohio StateAssociate 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

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

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

Setting “The Stage”

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

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

Evolving HIT and Data Landscape

Characteristics Before The Printing Press

After The Printing Press

Cost HighPrinted materials only available to the extremely wealthy

LowPrinted materials become cost effective for general public

Ubiquity LowCopies of printed materials had to be transcribed by hand, limiting number of instances

HighMass production of printed materials leads to broad dissemination and access

Reproducibility LowErrors of transcription and omission very common

HighSystematic 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 HighData sets generated and/or curated on a need basis

LowData production and storage costs decreasing in excess of Moores Law

Ubiquity LowProprietary data situated in vendor or project-specific repositories and formats

HighData becoming a renewable resource enabled by diverse re-use scenarios

Reproducibility LowErrors of transcription and omission very common

HighLinked public data enables the creation of “commons” model

Growth in HIT and Big Data in the Healthcare Information Age

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

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 Effectson System

Critical Advantages: Cost Time IP/Financial “Up Side”

Average Cost = 5-6BDuration = 15-20y

Average Cost = 200-250kDuration = 6m-1y

10

Design Thinking: A Systems Approach to Complex Problems with Technology

Current Opportunities for Innovation

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

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.

EGM in Action (1): Instrumenting the EHR to support risk profiling and patient-centered decision making

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.

EGM in Action (2): Embedding Decision Support and Visualization Tools in Existing EHR Workflow

Interactive Risk Visualization

Patient-Centered Decision Making

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)

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.

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

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.

What’s Next…

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.

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.

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

Intersection of Data Governance, Analytics and Healthcare Research or Operations using EGM Paradigm

OperationalAnalytics

(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 groupsC

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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”)

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?

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

Bridging Disciplines and MethodsTranslational Data AnalyticsThe 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

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

TDA@OhioState: Initial Focus Areas

Precision AgricultureFoundations

Systems Health & Wellness Digital Humanities

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

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

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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”

A Few Final Thoughts…

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!

Two Final Thoughts (2): Is It Time For Interventional Informatics?

Technology as a diagnostic or therapeutic agent in pursuit of the triple aim…

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

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):

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

Philip R.O. Payne, PhD, FACMIphilip.payne@osumc.edu@prpayne5www.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