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Compares two approaches for bringing up-to-date electronic decision support to the point of care, for docs using electronic health records systems. One approach taken by "intelligent designers", the other emerging from collective actions of "adaptive agents". Presented at MN HSR conference, Mar 09.
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www.CenterForUrbanHealth.org
MN HSR ConferenceMarch 3, 2009
Bringing Clinical Guidelines to the Point of Care with HIT Intelligent Designers & Adaptive Agents
ComparedYiscah Bracha, MS
Minneapolis Medical Research Foundation
Gail Brottman, MDHennepin County Medical Center
Kevin Larsen, MDHennepin County Medical Center
Robert GrundmeierThe Children’s Hospital of Philadelphia
Angeline Carlson, PhDData Intelligence, Inc.
www.CenterForUrbanHealth.org
The issue:
• Medical care delivered ≠ medical care recommended in evidence-based guidelines
• Is this a “problem”? Health policy: It is a problem. Docs not
following guidelines, pts don’t get best care.
Docs: Construction as “problem” depends on reason for differences
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Reasons identified empirically:
1. Information overload Too many guidelines Unaware of specific recommendation Need info in the moment of delivering care
2. Limited resources for implementation. Docs don’t have time or staff No reimbursement
3. Recommendations not useful or relevant
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Reason 1: Info overload
• Assumptions: Docs want to use guidelines, but don’t know what they are
• Source of the problem: Limits to human cognitive capacity
• Solution to the problem: Use information technology to enhance human cognitive capacity
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Reason 2. Limited resources
• Assumptions: Docs want to use guidelines, know what they are, but cannot implement them with existing resources (e.g. time, staff)
• Source of the problem: Inadequate material resources
• Solution to the problem: Change reimbursement systems.
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Reason 3. Not useful or relevant
• Assumptions: Contested!• Epistemic legitimacy: Biomedicine vs.
epidemiology• Source of authority: Front-line
clinicians vs. university-based researchers
• Credible “evidence”: Clinical practice vs. controlled experiments
• Problem? Contested!
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Using HIT as soln to info overload:
HIT Tool: Experience:
PDF of guideline on screen
Clinicians don’t access it
Pop-up reminders and alerts
“Alert fatigue”.
Electronic clinical decision support tools for:
Diagnostic tests to use Tools exist. Varied effect on doc behavior. Pt outcomes unknownInitiating therapy
Modifying therapy over time
“The final frontier”
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A Vision of the Future
• Individual practice sites will have up-to-date electronic decision support tools.
• Tools based on guidelines’ recommendations
• Recommendations based on evidence• Tools integrated into EHR systems.• Tool updates disseminated
electronically
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(assumptions behind the vision)
• Material resources are adequate• Contests over legitimacy & authority
resolved.
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Current HIT work leading to vision:
• Intelligent Designers Practiced by subgroup in academic medical
informatics community Supported by govt grants & contracts to
universities Current efforts guided by vision of future
• Adaptive Agents Practiced by vendors & their healthcare
customers Supported by market forces Current efforts guided by immediate needs
www.CenterForUrbanHealth.org
Intelligent Designers
• Situated at: Source of guidelines• Looking towards: Universe of practitioners• Concept: Convert guidelines into executable
code; disseminate code to practice sites.• Implementation: Standards (to help local
implementation) adopted by: Guideline developers EHR systems Guideline coders.
• Information channels: Academic conferences & peer-reviewed journals
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Adaptive Agents
Situated at: Practitioner sites Tool Developers
Looking towards:
Universe of potential tools
Universe of potential sites
Development concept:
Find tools that meet local needs
Develop tools that meet local
needs
Implementation concept:
Use whatever is available
Relationships w. EHR vendors
Communication
mechanisms:Healthcare product marketplace
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Properties of developed tools:
Properties: Single
tool
Current Efforts By:
Intelligent Designers Adaptive agents
ContentLanguage – can
represent guidelines as executable code
Support - clinical decision making &
administrative documentation.
ScopeBroad – all guidelines thru entire lifecycle
Narrow – single clinical condition or issue
Development effort
Extensive. Modest
Local Install Effort
ExtensiveDepends on local
environment
Local Use Effort
Intended to be minor Depends on installation
HIT Asthma Project:An Adaptive Agent Example
Project supported by the Agency for Health Research and Quality.
Contract No. HHSA290200600020Task Order No. 5
The findings and conclusions are the responsibility of the authors, not the AHRQ.
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HIT Asthma Tool Supports:
• Assessment & documentation: Asthma severity for untreated patients Asthma control for treated patients
• Selection of age-specific therapy: Initial therapy for untreated patients Modified therapy for treated patients
• Production of: Asthma progress note for patient’s chart List of selected meds & instructions for use Patient-friendly Asthma Action Plan.
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HIT Asthma Tool: Properties
• Coding language: Java applet• Installation mechanism: Hyperlink
opens applet on delivery site’s Intranet.• System tool data exchange:
Encrypted data in URL of hyperlink• Tool system data exchange:
Individual patient record: Doc opens applet in read-only mode to get asthma-specific hx.
Aggregate records: Merge data extracted from EHR & data generated by applet
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EHR Data Repository
Information forPopulations
Local EHR
Information forIndividual Patients
HIT Asthma Data Model for EHR
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EHR Data Repository
Information forPopulations
Local EHR
Information forIndividual Patients
ASTHMA APPLET
Asthma info for Individual patients
ASTHMA REGISTRY
Asthma info for Populations
Asthma Summary
Read-only invocation
tool
Patient & user context
HIT Asthma Data Exchange Model
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EHR Data Repository
Information forPopulations
Local EHR
Information forIndividual Patients
ASTHMA APPLET
Asthma info for Individual patients
HIV APPLET
HIV info for Individual Patients
ASTHMA REGISTRY
Asthma info for Populations
HIV REGISTRY
HIV info for Populations
Asthma Summary
Read-only invocation
tool
Read-only invocation
tool
HIVSummary
Patient & user context
Patient & user context
Evolutionary Emergence:
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EHR Data Repository
Information forPopulations
Local EHR
Information forIndividual Patients
ASTHMA CIG*
Asthma info for Individual patients
HIV CIG*
HIV info for Individual Patients
ASTHMA REGISTRY
Asthma info for Populations
HIV REGISTRY
HIV info for Populations
Intelligent Design
interface engine
interface engine
* Computer-Interpretable Guideline
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Both designers & adaptive agents:
• Face challenges: Converting natural language narrative
into computer-executable code Implementing uniform code into widely
disparate local systems
• Respond to challenges: Designers: Design global solutions Adaptive agents: Locally adapt
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Proposed Solutions vs. Adaptations:
Intelligent Designers Adaptive Agents
PerspectiveGuideline developers & researchers
Healthcare delivery sites & vendors meeting their needs
ViewDisparate local delivery sites
Sites products; Product developers sites
Goal
Convert narratives to CIGs, disseminate to local sites electronically
Meet immediate needs
Implementation challenge
Proposed Solution: Standards
Adaptation: Whatever works
Conversion challenge
Proposed Solution: Standards
Adaptation: Contest or accept
epistemic assumptions.
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Conclusions: Role of HIT
• HIT can encourage guideline-based care Addresses information overload Does not address:
Material resources Contested assumptions
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Predicted response to challenges:
• Dispersed, creative, adaptive efforts will continue: If regulatory & reimbursement systems create needs Successful adaptations will spread
• Global design efforts will continue If grant funding continues
Convergence towards standard representation language No convergence towards standard implementation
• Standards: May assist locally adaptive agents• Vision will manifest (if at all) by evolution, not
design Loosely coupled data exchange No one in control Messy, but will bring more guidelines to more points of
care.
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Thank YouThank You
For more information:Yiscah Bracha.
shate001@umn.edu
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Live data transfer & registry
Registry & user entry
3. Peak flow, triggers, weight.
Live data transfer from EHR
Lookup in registry
2. Identify patient
Live data transfer from EHR
Log in by user & system check.
1. Authenticate user
2. Verify Level Of
Severity
Choose next step.
2. Validate meds in record
3. Identify current asthma meds
1. Assess Control
Age 0-4 Age 5-11 Age 12+
1. Classify Severity
Age 0-4 Age 5-11 Age 12+
4a. Current meds map to
recognized plan
4b. Current meds do not map to
recognized plan
4. Choose next step.
Existing treatment recordNo existing
treatment record
Not validated
Validated.Asthma not
well controlled
5. User chooses what to do next.
1. Choose Treatment Plan
1. Next Visit Info
2. View/Print Asthma Action Plan.
Selected medsVisit Summary
2. Choose daily controllers
3. Choose quick relievers
4. Choose burst meds (for exacerbations)
Validated.Asthma
well controlled
New values of variables and PDF of AAP to registry
New values of variables and PDF of AAP to registry
Saves.
4a. Existing record of severity
(From EHR or registry, if exists)(From registry, if exists)
4. Classify severity or assess control.
4b. No existing record of severity
Return user to EHR system
Return user to operating system
Clo
sure
HIT Asthma.Model workflow for an outpatient asthma visit
Consistent with recommendations in 2007 NEAPP Guidelines.
EHR-compatible versionDesktop version
Recommended