Clinical KM 3.0 in a
1.0 EHR World
Tonya Hongsermeier, MD, MBA
CMIO
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
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
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)
Portalization Supports Transparency, Curation, De-Silo-ization of CDS Content
Build-trackers and Stove-piped EHR content editors don’t cut it
Jive: Facebook plus Twitter plus Wikipedia plus
Survey monkey plus Expertise Locator plus…
Jive: Mobile apps to further engage…
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
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

Hongsermeier app store for health

  • 1.
    Clinical KM 3.0in 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 KMInvestments • 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 plusTwitter plus Wikipedia plus Survey monkey plus Expertise Locator plus…
  • 7.
    Jive: Mobile appsto 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 EHRscould “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