epic as platform for clinical decision support. implications for qi and research
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www.CenterForUrbanHealth.org
Epic Research Advisory Council MeetingApril 1, 2009
Epic as a Platform Launching Decision Support Tools:
Implications for Research and Population QI
Yiscah Bracha, MSMinneapolis Medical Research Foundation
Robert Grundmeier, MDThe Children’s Hospital of Philadelphia
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Improving Asthma Care in an Integrated Safety Net through a Commercially Available Electronic Medical
Record
Prime contractor: Denver Health and Hospital Association. Subcontractor: Minneapolis Medical Research Foundation. Project site: Hennepin County Medical Center, Mpls MN
AHRQ Contract No. HHSA290200600020, Task Order No. 5
Staff and contractors – Minneapolis Medical Research FoundationGail Brottman, MD (Chief, Pediatric Pulmonology, HCMC)Kevin Larsen, MD (Chief Medical Informatics Officer, HCMC)Yiscah Bracha, MS (Research Director, Center for Urban Health)Cherylee Sherry, MPH (Project Manager, Pediatric Research & Advocacy HCMC )Touch Thouk (Administrative Manager, Center for Urban Health)Angeline Carlson, PhD (Principle, Data Intelligence Inc.)
Staff – Denver Health and Hospital AssociationSherry Eisert, PhD (Director, Health Services Research)Michael (Josh) Durfee (Research Projects Coordinator, Health Services Research)
Contributors of Ideas, Information & Effort:Michael Barbouche (University of Wisconsin Medical Foundation); Robert Grundmeier, MD (Children’s Hospital of Philadelphia); Michael Kahn, MD, PhD (Denver Children’s Hospital)Donald Uden, PharmD (University of Minnesota), Faith Dohman, RN (Hennepin Faculty Associates), Susan Ross, RN (Minnesota Department of Health)
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The Problem:
• Docs practice within an information avalanche
• Impossible for any human to keep up
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What is a busy physician to do?
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Be confident.
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Be concerned.
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Be oblivious.
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Medical informatics to the rescue!
• Provide decision support during care, while doc viewing pt electronic record
• Developmental generation beyond: URL to PDF of guidelines on screen
(clinicians don’t go there) Automatic reminders & alerts (clinicians
suffer from “alert fatigue”)
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Task-based decision support
• Types of support Which diagnostic tests to use How to initiate therapy How to adjust therapy over time
• Electronic data: EHR data automatically populate
decision algorithms Results from algorithms automatically
populate patient data record
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ASTHMA REGISTRY
Asthma info for Populations
Clarity
Information forPopulations
Epic
Information onIndividual Patients
Electronic decision
support for:
Asthma
Decision Support & Epic: Current model
interface engine
interface engine
Electronic decision
support for:
Which diagnostic imaging tech?
IMAGING REGISTRY
Imaging info for Populations
www.CenterForUrbanHealth.org
ASTHMA REGISTRY
Asthma info for Populations
Clarity
Information forPopulations
Epic
Information onIndividual Patients
Electronic decision
support for:
Asthma
Current model and data warehousing:
interface engine
interface engine
Electronic decision
support for:
Which diagnostic imaging tech?
IMAGING REGISTRY
Imaging info for Populations
ASTHMA REGISTRY
Asthma info for Populations
www.CenterForUrbanHealth.org
Clarity
Information forPopulations
Epic
Information onIndividual Patients
Electronic decision
support for:
Asthma
Current model and data warehousing:
interface engine
interface engine
Electronic decision
support for:
Which diagnostic imaging tech?
IMAGING REGISTRYImaging info for Populations
ASTHMA REGISTRY
Asthma info for Populations
www.CenterForUrbanHealth.org
Building the Warehouse:
IMAGING REGISTRYImaging info for Populations
ASTHMA REGISTRY
Asthma info for Populations
www.CenterForUrbanHealth.org
The Warehouse Dream:
ASTHMA REGISTRY
Asthma info for Populations
HIV REGISTRY
HIV info for Populations
STENT REGISTRY
Stent info for Populations
DIABETES REGISTRY
Diabetes info for Populations
IMAGING REGISTRY
Imaging info for Populations
COLONOSCOPY REGISTRY
Colonoscopy infoPopulations
www.CenterForUrbanHealth.org
ASTHMA REGISTRY
Asthma info for Populations
HIV REGISTRY
HIV info for Populations
STENT REGISTRY
Stent info for Populations
DIABETES REGISTRY
Diabetes info for Populations
IMAGING REGISTRY
Imaging info for Populations
COLONOSCOPY REGISTRY
Colonoscopy infoPopulations
The analytic dream:
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Our analytic reality:
ASTHMA REGISTRY
Asthma info for Populations
HIV REGISTRYHIV info for Populations
DIABETES
REGISTRY
Diabetes info fo
r
Populations
IMAG
ING
REG
ISTRY
Imaging info for
Populations
WHY?
www.CenterForUrbanHealth.org
Clarity
Information forPopulations
Epic
Information onIndividual Patients
Electronic decision
support for:
Asthma
ASTHMA REGISTRY
Asthma info for Populations
The problem with the current model:
Electronic decision
support for:
Which diagnostic imaging tech?
IMAGING REGISTRY
Imaging info for Populations
interface engine
interface engine
www.CenterForUrbanHealth.org
Clarity
Information forPopulations
Epic
Information onIndividual Patients
Electronic decision
support for:
Asthma
ASTHMA REGISTRY
Asthma info for Populations
Electronic decision
support for:
Which diagnostic imaging tech?
IMAGING REGISTRY
Imaging info for Populations
Hard to build….
interface engine
Hard to maintain….
interface engine
www.CenterForUrbanHealth.org
Clarity
Information forPopulations
Epic
Information onIndividual PatientsElectronic decision
support for:
Asthma
ASTHMA REGISTRY
Asthma info for
Populations
So much effort focused on interface*:
Electronic decision support for:
Which diagnostic imaging tech?
IMAGING REGISTRY
Imaging info for
Populations
interface engine
interface engine
* Building complex decision support within Epic is just as much effort as interface
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No resources for data mgmt:
ASTHMA REGISTRY
Asthma info for Populations
HIV REGISTRYHIV info for Populations
DIABETES
REGISTRY
Diabetes info fo
r
Populations
IMAG
ING
REG
ISTRY
Imaging info for
Populations
www.CenterForUrbanHealth.org
A low-cost alternative to current model:
1. Support provided by external applet2. Applet invoked through Intranet
hyperlink3. Hyperlink opens applet in new
window4. Applet provides decision support5. Applet saves data to its own
database6. User returns to Epic
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Example: HIT Asthma Project
• Java Applet, called e-AAP, provides administrative & clinical decision support*
• Invoked from Epic: Create new Asthma Action Plan (procedure order) View existing Asthma Action Plan
from Order Results from asthma detail in problem list
• Invocation sends @ 20 encrypted live Epic data elements to applet through URL
• New asthma data saved to SQL server database
* Support based on NAEPP-3 Guidelines, August 2007
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e-AAP Clinical Decision Support:
• For new asthma patients Assess severity Initiate treatment given age & severity
• For currently treated asthma patients Assess control Modify treatment given age, control,
current treatment, pt adherence Step-based treatment Supports user in calculating current step
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e-AAP Administrative Support:
• Document severity and/or control• Generate printable asthma action plan
Patient-specific Patient-friendly (tested for literacy) Records all JHACO-required data
• Document production of AAP• Generate:
Template of asthma progress note that user can paste into patient’s chart
List of selected meds & instructions for use
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Launched as procedure order in Epic:
Highlighted data transferred in URL.
Clicking here launches applet..
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Live data passed to Applet in URL:
Web link defined by print group
Launches web browser with “garbled” pt data in URL
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Linking code required:
Cache routine: Delivers hyperlink
Cache global:Provides configuration
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1st screen user sees:
User automatically logged in; audit trail initiated; patient data transferred directly from Epic.
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Applet generates data. Where do they go?
• Individual patient data: Saved to underlying database Applet generates asthma progress note
that doc can paste into record
• Population data: All Applet data stored to underlying
registry Clarity data extracted & merged with
Applet data in registry
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Clarity
Information forPopulations
Epic
Information forIndividual Patients
ASTHMA APPLET
Asthma info for Individual patients
ASTHMA REGISTRY
Asthma info for Populations
Patient & user context thru
URL
Individual pt data:
Saved to SQL server database (registry)
www.CenterForUrbanHealth.org
Clarity
Information forPopulations
Epic
Information forIndividual Patients
ASTHMA APPLET
Asthma info for Individual patients
ASTHMA REGISTRY
Asthma info for Populations
Patient & user context thru
URL
Population data:
Relevant data extracted from Clarity, merged with Applet data in registry
www.CenterForUrbanHealth.org
Clarity
Information forPopulations
Epic
Information forIndividual Patients
ASTHMA APPLET
Asthma info for Individual patients
ASTHMA REGISTRY
Asthma info for Populations
Patient & user context
Decision Support & Epic: Alternative model
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How are applet data retrieved?
• Individual patient data: Previously created e-AAPs easily found &
reviewed from Applet. If user pasted asthma progress note,
available in EHR.
• Population data: Extracted from underlying registry Registry populated with Applet data and
relevant data from Clarity
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To view individual pt data:
To see previous AAPs, user clicks here
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Pop-up appears:
User clicks on desired PDF
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Hyperlink opens applet:
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Clarity
Information forPopulations
Epic
Information forIndividual Patients
ASTHMA APPLET
Asthma info for Individual patients
ASTHMA REGISTRY
Asthma info for Populations
Patient & user context thru
URL
To view population data:
www.CenterForUrbanHealth.org
Clarity
Information forPopulations
Epic
Information forIndividual Patients
ASTHMA APPLET
Asthma info for Individual patients
Imaging Applet
Imaging info for Individual Patients
ASTHMA REGISTRY
Asthma info for Populations
Imaging Registry
Imaging info for Populations
Patient & user context
Patient & user context
Multiple such registries possible:
www.CenterForUrbanHealth.org
Clarity
Information forPopulations
Epic
Information forIndividual Patients
ASTHMA APPLET
Asthma info for Individual patients
Imaging Applet
Imaging info for Individual Patients
ASTHMA REGISTRY
Asthma info for Populations
Imaging Registry
HIV info for Populations
Patient & user context
Patient & user context
ASTHMA REGISTRY
Asthma info for Populations
Alternate model and data warehousing:
www.CenterForUrbanHealth.org
EHR Data Repository
Information forPopulations
Local EHR
Information forIndividual Patients
ASTHMA APPLET
Asthma info for Individual patients
HIV APPLET
HIV info for Individual Patients
Patient & user context
Patient & user context
IMAGIN
G
REGIS
TRY
Imagin
g inf
o fo
r
Populatio
ns
ASTHMA REGISTRY
Asthma info for Populations
Alternate model and data warehousing:
Imaging Applet
HIV info for Individual Patients
www.CenterForUrbanHealth.org
Imaging Applet
HIV info for Individual Patients
EHR Data Repository
Information forPopulations
Local EHR
Information forIndividual Patients
ASTHMA APPLET
Asthma info for Individual patients
Patient & user context
Patient & user context
No major interface to build or maintain!
interface engine
interface engine
ASTHMA REGISTRY
Asthma info for Populations
Imaging Registry
Imaging info for Populations
www.CenterForUrbanHealth.org
ASTHMA REGISTRY
Asthma info for Populations
HIV REGISTRY
HIV info for Populations
STENT REGISTRY
Stent info for Populations
DIABETES REGISTRY
Diabetes info for Populations
IMAGING REGISTRY
Imaging info for Populations
COLONOSCOPY REGISTRY
Colonoscopy infoPopulations
Maybe we can!
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Questions?Questions?
Project supported by the Agency for Health Research and Quality.
Contract No. HHSA290200600020, Task Order No. 5
The findings and conclusions are the responsibility of the authors, not the AHRQ.
Clinical informatics at HCMC:Dr. Kevin Larsen
Invoking applet from Epic:Dr. Robert Grundmeier
Asthma details about applet:Dr. Gail Brottman
Project direction & all other Qs:Yiscah Bracha, MS
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Extra Slides
• URL to live application demo• Why not build in Epic
Logical complexity Volume of clinical material
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Why not build it in Epic?
• Logical complexity Existence of relevant data Validity of relevant data User choices Patient age, adherence
• Volume of up-to-date clinical info required @37k med combos in guidelines Guidelines contain @ 2% of all possibilities
www.CenterForUrbanHealth.orgLive data transfer from EHR
Lookup in registry
2. Identify patient
2. Verify Level Of
Severity
Choose next step.
3. Identify current asthma meds
1. Assess Control
Age 0-4 Age 5-11 Age 12+
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)
HIT Asthma.Model workflow for an outpatient asthma visit
Consistent with recommendations in 2007 NEAPP Guidelines.
EHR-compatible versionDesktop version
4. Classify severity or assess control.
3. Validate meds in record
4a. Current meds map to
recognized plan
4b. Current meds do not map to
recognized plan
4. Choose next step.
Live data transfer from EHR
Log in by user & system check.
1. Authenticate user
Live data transfer & registry
Registry & user entry
3. Peak flow, triggers, weight.
4a. Existing record of severity
(From EHR or registry, if exists)(From registry, if exists)
4b. No existing record of severity
Return user to EHR system
Return user to operating system
Clo
sure
New values of variables and PDF of AAP to registry
New values of variables and PDF of AAP to registry
Saves.
1. Classify Severity
Age 0-4 Age 5-11 Age 12+
Existing treatment record
Not validated
Validated.
No existing treatment record
BEGIN
END
e-AAP: High-level process flow:
Disease mgmt component
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Disease management component:
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Treatment decisions in guidelines:
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Treatment data in Applet:
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