epic as a platform to launch clinical decision support tools

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

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)

The Problem:

• Docs practice within an information avalanche

• Impossible for any human to keep up

What is a busy physician to do?

Be confident.

Be concerned.

Be oblivious.

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”)

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

ASTHMA REGISTRY

Asthma info for Populations

Clarity

Information forPopulations

Information onIndividual Patients

Electronic decision

support for:

Asthma

Decision Support & Epic: Current model

interface engine

Electronic decision

support for:

Which diagnostic imaging tech?

IMAGING REGISTRY

Imaging info for Populations

ASTHMA REGISTRY

Clarity

Electronic decision

support for:

Asthma

Current model and data warehousing:

interface engine

Electronic decision

support for:

IMAGING REGISTRY

ASTHMA REGISTRY

Clarity

Electronic decision

support for:

Asthma

Current model and data warehousing:

interface engine

Electronic decision

support for:

IMAGING REGISTRYImaging info for Populations

ASTHMA REGISTRY

Building the Warehouse:

IMAGING REGISTRYImaging info for Populations

ASTHMA REGISTRY

The Warehouse Dream:

ASTHMA REGISTRY

HIV REGISTRY

HIV info for Populations

STENT REGISTRY

Stent info for Populations

DIABETES REGISTRY

Diabetes info for Populations

IMAGING REGISTRY

COLONOSCOPY REGISTRY

Colonoscopy infoPopulations

ASTHMA REGISTRY

HIV REGISTRY

STENT REGISTRY

DIABETES REGISTRY

IMAGING REGISTRY

The analytic dream:

Our analytic reality:

ASTHMA REGISTRY

HIV REGISTRYHIV info for Populations

DIABETES

REGISTRY

Diabetes info fo

Populations

Imaging info for

Populations

Clarity

Electronic decision

support for:

Asthma

ASTHMA REGISTRY

The problem with the current model:

Electronic decision

support for:

IMAGING REGISTRY

interface engine

Clarity

Electronic decision

support for:

Asthma

ASTHMA REGISTRY

Electronic decision

support for:

IMAGING REGISTRY

Hard to build….

interface engine

Hard to maintain….

interface engine

Clarity

Information onIndividual PatientsElectronic decision

support for:

Asthma

ASTHMA REGISTRY

Asthma info for

Populations

So much effort focused on interface*:

Electronic decision support for:

IMAGING REGISTRY

Imaging info for

Populations

interface engine

* Building complex decision support within Epic is just as much effort as interface

No resources for data mgmt:

ASTHMA REGISTRY

HIV REGISTRYHIV info for Populations

DIABETES

REGISTRY

Diabetes info fo

Populations

Imaging info for

Populations

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

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

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

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

Launched as procedure order in Epic:

Highlighted data transferred in URL.

Clicking here launches applet..

Live data passed to Applet in URL:

Web link defined by print group

Launches web browser with “garbled” pt data in URL

Linking code required:

Cache routine: Delivers hyperlink

Cache global:Provides configuration

1st screen user sees:

User automatically logged in; audit trail initiated; patient data transferred directly from Epic.

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

Clarity

Information forIndividual Patients

ASTHMA APPLET

Asthma info for Individual patients

ASTHMA REGISTRY

Patient & user context thru

Individual pt data:

Saved to SQL server database (registry)

Clarity

ASTHMA APPLET

ASTHMA REGISTRY

Population data:

Relevant data extracted from Clarity, merged with Applet data in registry

Clarity

ASTHMA APPLET

ASTHMA REGISTRY

Patient & user context

Decision Support & Epic: Alternative model

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

To view individual pt data:

To see previous AAPs, user clicks here

Pop-up appears:

User clicks on desired PDF

Hyperlink opens applet:

Clarity

ASTHMA APPLET

ASTHMA REGISTRY

To view population data:

Clarity

ASTHMA APPLET

Imaging Applet

Imaging info for Individual Patients

ASTHMA REGISTRY

Imaging Registry

Multiple such registries possible:

Clarity

ASTHMA APPLET

Imaging Applet

Imaging info for Individual Patients

ASTHMA REGISTRY

Imaging Registry

ASTHMA REGISTRY

Alternate model and data warehousing:

EHR Data Repository

Local EHR

ASTHMA APPLET

HIV APPLET

HIV info for Individual Patients

IMAGIN

Imagin

Populatio

ASTHMA REGISTRY

Alternate model and data warehousing:

Imaging Applet

EHR Data Repository

Local EHR

ASTHMA APPLET

No major interface to build or maintain!

interface engine

ASTHMA REGISTRY

Imaging Registry

ASTHMA REGISTRY

HIV REGISTRY

STENT REGISTRY

DIABETES REGISTRY

IMAGING REGISTRY

Maybe we can!

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

(kevin.larsen@hcmed.org)

Invoking applet from Epic:Dr. Robert Grundmeier

(grundmeier@email.chop.edu)

Asthma details about applet:Dr. Gail Brottman

(brott004@umn.edu)

Project direction & all other Qs:Yiscah Bracha, MS

(shate001@umn.edu)

Extra Slides

• URL to live application demo• Why not build in Epic

Logical complexity Volume of clinical material

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

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

e-AAP: High-level process flow:

Disease mgmt component

Disease management component:

Treatment decisions in guidelines:

Treatment data in Applet:

epic as a platform to launch clinical decision support tools

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