overview of national learning health community & learning health for michigan landscapes

Overview of National Learning Health Community

& Learning Health forMichigan LandscapesJoshua Rubin & Timothy Pletcher

October 27th, 2015

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

Learning Health for Michigan2

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


Acknowledgements

This material is based on the work and content provided by:

Charles P. Friedman, PhDJosiah Macy, Jr. ProfessorChair, Department of Learning Health Sciences

&Allen Flynn, Research InvestigatorDepartment of Learning Health Sciences


How Learning Happens : “Virtuous Cycles” of Study and Change

AssembleExperience Data

TakeAction

InterpretResults

AnalyzeData

Tailored Messagesto Decision-Makers

A Problem of Interest

Decision to Study


Learning Health for Michigan

LH4M

6

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


CHRT Staff:Leah CorneailBabette Levy Ezinne Ndukwe

7

How to Learn Routinely: A Single Platform Supports Multiple Simultaneous “Virtuous Cycles”

DifferentProblems

Rapid Cycle

Slower Cycle

PLATFORM


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


What is our preference?Is it?

Or

And is it?

Or


So What’s in a Complete Platform?

Mechanisms for managing

communities of interest


Platform Development

Domain

Story

Collection

Use cases

Standard “widgets”

Refinement/ Testing

Synthesis

Collection of use cases (across domains)

Domain

Story

Collection

Use cases


The Afferent and Efferent Sides of the Learning Cycle


Afferent(BD2K)

Efferent(K2P)

Learning = BD2K + K2P


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


The LHS Must Do This

AssembleRelevant Data

Take Action to Change Practice

InterpretResults

AnalyzeData

Deliver Tailored Message


Decision to Study


Not This

AssembleRelevant Data

Take Action to Change Practice

InterpretResults

AnalyzeData

Deliver Tailored Message


Decision to Study

Journals?


Record decisions

Communicateadvice

Store knowledge

Formulate advice

Icon: Brain by Eovaro Atli Birgisson, The Noun Project, 2015.Flynn, 2015

LHS componentsto organize, manage and

provide access towhat 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

Objective:

Design and build a LHS brain

Store knowledge

Formulate advice


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


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. Guidelinedissemination is inadequate.

• RCT-level evidence is NOT available to guide most health caredecisions 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


A brain contains knowledge

Examples of knowledge are…

Regression Equation

Clinical Calculation

Checklist

Template

Guideline

Predictive Model

Decision Model


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.

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.


DKOs can be explicitly related or linked

Step 3InterrelatePieces of

Digital Knowledge in a

Knowledge Network

DKO networks afford new capabilities.Learning Health for Michigan

24

Types of Digital Knowledge Pieces

Interactivity

Agen

cy Logic in AutonomousSystems

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 toor reference

evolution fromPDF to DKO


Digital Knowledge Object Maturity Levels

Static: A digital document (e.g., PDF file) that a person can

read

InteractiveAn “APP” that accepts inputs and provides outputs

Self-describingA DKO that describes its role and uses in metadata

SemanticA DKO with explicit links to known terms or concepts

NodalA DKO node that has defined relations to other DKOs

passive - narrative

active - transactional

automatically disseminable


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, includingstatic 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)


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

CreateDigital

KnowledgeRepositories

https://wiki.duraspace.org/display/FF/Fedora+Repository+Home

We are not starting from scratch.


How to create a “brain” for the LHS

Precursor A Precursor B Step 1 Step 2 Step 3

CreateDigital

KnowledgeRepositories

Make DigitalKnowledge as

Explicit andTransactional

as Possible

Wrap Piecesof Digital

Knowledge inDescriptiveMetadata

Associate Pieces of

Digital Knowledge

with Terminologies & Ontologies

InterrelatePieces 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


Acknowledgements

The PopMedNet content was made available courtesy of:

Jeffrey Brown, PhDMichael Klompas, MD, MPHMDPHnet Research Team


Connection to the Blue Side


The PopMedNet™ software application enables simple creation, operation, and governance of distributed health data networks.

31

PopMedNet


A Distributed Way to Ask


Questions?

For follow up send email attention:Tim Pletcher, DHA

[email protected]

Thank you


overview of national learning health community & learning health for michigan landscapes

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