This document discusses challenges with using electronic health records (EHRs) to support clinical knowledge management (KM) and continuous learning. It outlines several investments made by Lahey Health to address these challenges, including using an external collaboration platform to manage clinical decision support (CDS) content outside of the EHR. The document argues that EHRs are not designed for collaboration or learning, and that clinical KM 3.0 approaches are needed to help health systems and users continuously self-improve.

1. Clinical KM 3.0 in a
1.0 EHR World
Tonya Hongsermeier, MD, MBA
CMIO

2. MU, ACO, PCMH, Safety, Readmission Prevention 
Imperative to be Competent at Self-Improvement
Care
Context
Guided Data Review
Guided Decisions
& Orders
Guided Execution
of Decisions & Orders
Guided
Assessments
&Monitoring
of Interventions
Learning
Context
Identify
Gap in
Knowledge
or Care:
CDS Target
Implement
CDS
Knowledge
Measure Effectiveness
Of CDS Knowledge
Research&Discovery
Update/Acquire
Knowledge
Curate Assets
Knowledge
Data
2
But: EHRs are not designed
as Collaboration-ware or Learning-Ware

3. Self-Improving Organizations Know:
Committee, Department,
Researcher, or Other
Proposes to Implement Content
Guideline is Defined and Validated
Functional Knowledge Specification
For Encoding is
Designed and Validated
Ongoing Revisions or
Eventual Sunset
Of Encoded Guideline
GOVERNANCE: Who decides what clinical problems to tackle
with CDS, How to drive change adoption
Specification is
Engineered into Production Generating
a Technical Specification
PARTICIPATION: How to enable Subject Matter Experts
engagement in CDS design
COMMODITIZATON: Not to build what you can buy
STEWARDSHIP: How to allocate resources to steward the
CDS knowledge
TRACEABILITY: To invest in technical tools to support
build, dependency management, visualization,
maintenance
MEASURE EFFECTIVENESS: To focus on measurable targets
and invest in continuous program evaluation
3

4. Lahey Health KM Investments
• Governance and stewardship aligning systems
with business drivers
• Same regulations that force EHR adoption make
them unusable
• Focused on reconciling paradox of
standardization and personalization of care
• 3rd party content (usual suspects)
• Externalization of CDS content from EHR to
support curation
• Collaboration Platform (JIVE)

5. Portalization Supports Transparency, Curation, De-Silo-ization of CDS Content
Build-trackers and Stove-piped EHR content editors don’t cut it

6. Jive: Facebook plus Twitter plus Wikipedia plus
Survey monkey plus Expertise Locator plus…

7. Jive: Mobile apps to further engage…

8. KM Platforms = Convergence
Content Management Social Interaction Management
Process/Transaction Management
•Email
•IM
•Corporate Twitter
•Portals/Virtual Rooms
•Teleconferencing
•Desktop Sharing
•Idea Capture
•Expertise Locators
•Social Q&A
•CMS
•curation,versioning,auditing
•Wikis, Blogs, RSS
•Database Management
•Document Management
•Clouds
•Semantics
•Tagging
•Taxonomies/Folksonomies
•Search
•User Profiles/Contacts
•Rules Engines
•Workflow Engines
•Task Management
•Scheduling/Tracking
Leaders:
•EMC
•Microsoft
•IBM
•Oracle
•OpenText
Leaders:
•Telligent
•Jive
•Atlassian
•SocialText
•NewsGator

9. Imagine if EHRs could “Learn”
how to help Users/Health Systems Self-Improve
how to anticipate user workflows and information needs
Amount of data
ProductivityofSearch
Databases
Web 1.0
1990 - 2000
The World Wide Web
PC Era
1980 - 1990
The Desktop Keyword search
Directories
2000 - 2010
Web 2.0
The Social Web
Files & Folders
Tagging
Natural
language
search
2010 - 2020
Web 3.0
The Semantic Web
Automated Content
Analysis
2010 - 2020
Web 3.0
User Modeling
User profiling
Health System Profiling
** From: Making Sense of the Semantic Web, BY Nova Spivack
The MetaWeb
Web 4.0
We are about here, can’t find
pt. data or knowledge in the
swamp
Intelligent Agents
Connected Intelligence
EHR vendors today impose enormous costs
of conversion and curation of Data, Knowledge, Behavior