HIMSS Detroit 2015 presentation by Timothy Pletcher and Joshua Rubin.

1. Overview of National
Learning Health Community
& Learning Health for
Michigan Landscapes
Joshua Rubin & Timothy Pletcher
October 27th, 2015

2. Disclosure of Conflicts of
Interest-Pletcher
• There are no personal conflicts
• Dr. Pletcher has an adjunct faculty
appointment at the University of Michigan
Medical School Department of Learning
Health Sciences
• Dr. Pletcher also serves as the Executive
Director for the Michigan Health
Information Network Shared Service
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3. Objectives
1. Understand the national framework for how the Learning Health Community is
evolving abroad, in the U.S., and within Michigan
2. Become familiar with the LHS vision and the multi-stakeholder consensus LHS
Core Values
3. Learn about other stakeholders spanning the health arena who are working
toward collaboratively realizing this shared vision; discover how to join them by
participating in the Learning Health Community movement at a national level or
participate in Learning Health for Michigan (LH4M) effort
4. Gain insight into how the research and discovery networks are poised to
integrate with traditional health care delivery data sharing infrastructure
5. Achieve awareness of the new technology and policy environments and
approaches such as PopMedNetTM being used to enable distributed data
sharing, as well as rapid learning leveraging the power of analytics
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4. Acknowledgements
This material is based on the work and content
provided by:
Charles P. Friedman, PhD
Josiah Macy, Jr. Professor
Chair, Department of Learning Health Sciences
&
Allen Flynn, Research Investigator
Department of Learning Health Sciences
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5. How Learning Happens :
“Virtuous Cycles” of Study
and Change
Assemble
Experience Data
Take
Action
Interpret
Results
Analyze
Data
Tailored Messages
to Decision-Makers
A Problem of
Interest
Decision to Study
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6. Learning Health for Michigan
LH4M
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7. Members of the Platform Team
• Allen Flynn, University of
Michigan
• Chuck Friedman, U-M Medical
School
• Johmarx Patton, U-M Medical
School
• Jodyn Platt, U-M School of Public
Health
• Tim Pletcher, MiHIN
• Peter Polverini, U-M School of
Dentistry
• Josh Rubin, U-M Medical School
Learning Health for Michigan
CHRT Staff:
Leah Corneail
Babette Levy
Ezinne Ndukwe
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8. How to Learn Routinely: A Single
Platform Supports Multiple
Simultaneous “Virtuous Cycles”
Different
Problems
Rapid Cycle
Slower Cycle
PLATFORM
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9. In Other Words…
• Without a platform, each
learning cycle develops its
own, sub-optimal methods
for learning; no economy of
scale
• With a platform, all cycles
share & benefit from a
common infrastructure;
costs are distributed
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10. What is our preference?
Is it?
Or
And is it?
Or
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11. So What’s in a Complete Platform?
Mechanisms for
managing
communities of
interest
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12. Platform Development
Domain
Story
Collection
Use
cases
Standard
“widgets”
Refinement/
Testing
Synthesis
Collection of use cases
(across domains)
Domain
Story
Collection
Use
cases
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13. The Afferent and Efferent
Sides of the Learning Cycle
A Problem of
Interest
Afferent
(BD2K)
Efferent
(K2P)
Learning = BD2K + K2P
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14. The LHS and Big Data
• The LHS is bigger than Big Data
• Big Data addresses only the blue side of
the learning cycle
• The LHS infrastructure must support
complete learning cycles
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15. The LHS Must Do This
Assemble
Relevant Data
Take Action to Change
Practice
Interpret
Results
Analyze
Data
Deliver Tailored
Message
A Problem of
Interest
Decision to Study
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16. Not This
Assemble
Relevant Data
Take Action to Change
Practice
Interpret
Results
Analyze
Data
Deliver Tailored
Message
A Problem of
Interest
Decision to Study
Journals?
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17. Record
decisions
Communicate
advice
Store
knowledge
Formulate
advice
Icon: Brain by Eovaro Atli Birgisson, The Noun Project, 2015.Flynn, 2015
LHS components
to organize,
manage and
provide access to
what is learned,
i.e., to knowledge.
At scale, the Brain is a
Digital Library of Learning.
There can be one such
library, or many.
The LHS Needs a Brain to
Drive the Efferent Side

18. Objective:
Design and build a LHS brain
Store
knowledge
Formulate
advice
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19. Specific Functions of a Brain
Basic Brain Functions
Organize knowledge to know what is known
Manage knowledge to know about what is known
Represent and provide knowledge for use
Advanced Brain Functions
Formulate tailored advice
Infer what is NOT YET known
Predict an individual’s immediate knowledge needs
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20. No brain. Slow gain.
• Publications are NOT ready for use. The knowledge they
contain has to be transformed into actionable knowledge.
• Evidence-based guideline development is slow. Guideline
dissemination is inadequate.
• RCT-level evidence is NOT available to guide most health care
decisions so learning from experience is a necessity thus a
capacity to manage experiential knowledge is a necessity.
• Generating up-to-date, individualized, relevant, clear advice
remains a difficult task
• “Inventory principle” - It is difficult to know what is known and
NOT YET known unless knowledge can be assessed in aggregate
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21. A brain contains knowledge
Examples of knowledge are…
Regression Equation
Clinical Calculation
Checklist
Template
Guideline
Predictive Model
Decision Model
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22. Store
knowledge
Formulate
advice
- Now I can learn!
With a brain to contain, organize, and manage knowledge,
our health system can be responsive, adaptive, effective and efficient.

23. What is a Digital Knowledge Object?
Attribution, versioning, and context comes from metadata.
Transactional capabilities afford (i) access and authorization
controls, and (ii) direct interaction with executable code.
The knowledge contained in a DKO can be generally modeled
using terms and relations amongst them – forming its ontology.
The knowledge can be specifically represented in one of more
computable formats – R code, javascript, GEM, etc.
A digital knowledge object takes an instance of
knowledge-in-the-world and adds digital metadata and
transactional capabilities to it.
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24. DKOs can be explicitly related or linked
Step 3
Interrelate
Pieces of
Digital
Knowledge in a
Knowledge
Network
DKO networks afford new capabilities.
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25. Types of Digital Knowledge Pieces
Interactivity
Agency
Logic in Autonomous
Systems
Logic in Apps
Digital Knowledge
Objects (DKOs) with Logic
Static
Websites & PDFs
Icons: App by Garrett Knoll, Website by buzzyrobot, PDF by Laurent Canivent, The Noun Project, 2015.Flynn, 2015
consumed by
contain, link to
or reference
evolution from
PDF to DKO
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26. Digital Knowledge Object Maturity Levels
Static:
A digital document (e.g., PDF file) that a person can
read
Interactive
An “APP” that accepts inputs and provides outputs
Self-describing
A DKO that describes its role and uses in metadata
Semantic
A DKO with explicit links to known terms or concepts
Nodal
A DKO node that has defined relations to other DKOs
passive - narrative
active - transactional
automatically disseminable
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27. Store Knowledge and
Formulate Advice
• Semantically aware queries of DKOs
• Automated queries based on individual features
• Inference over any DKO space to identify what is NOT YET known
• Digital DKO libraries online
- Versioning
- Governance
- Curation
• Knowledge stored and linked in various forms, including
static forms and transactional, coded, computable forms
• A platform to create advice-giving systems of all kinds
• A necessary component of a Learning Health System
Fedora Repositories of Digital Knowledge Objects (DKOs)
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28. Fedora is a digital knowledge repository
• An open source management system for digital content
• Scalable knowledge engineering and management system
• Ready solution that speeds up LHS “brain” development
• Proven system already in use by libraries worldwide
• Fedora’s creators are faculty and staff now at U-M
Precursor A
Create
Digital
Knowledge
Repositories
https://wiki.duraspace.org/display/FF/Fedora+Repository+Home
We are not starting from scratch.
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29. How to create a “brain” for the LHS
Precursor A Precursor B Step 1 Step 2 Step 3
Create
Digital
Knowledge
Repositories
Make Digital
Knowledge as
Explicit and
Transactional
as Possible
Wrap Pieces
of Digital
Knowledge in
Descriptive
Metadata
Associate
Pieces of
Digital
Knowledge
with
Terminologies
& Ontologies
Interrelate
Pieces of
Digital
Knowledge in a
Knowledge
Network
Manage knowledge to know about what is known
Formulate Tailored Advice
Infer what is NOT YET stored
Represent and provide knowledge for use
Predict
knowledge
needs
Organize knowledge to know what is known
Store what is known in a way that it persists and is always accessible
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30. Acknowledgements
The PopMedNet content was made available
courtesy of:
Jeffrey Brown, PhD
Michael Klompas, MD, MPH
MDPHnet Research Team
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31. Connection to the Blue Side
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The PopMedNet™ software
application enables
simple creation, operation, and
governance of distributed
health data networks.
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32. PopMedNet
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33. A Distributed Way to Ask
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34. Learning Health for Michigan
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35. Questions?
For follow up send email attention:
Tim Pletcher, DHA
pletcher@umich.edu
Thank you
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