A Novel Use of Electronic Health Reco

To Inform Patient and Clinician Treat

Choices

John Crilly, PhD, MPH, MSW

US Department Department of Veterans Veterans Affairs Affairs, VISN 16 MIRECC, New O

O

Tulane University, Department of Psychiatry

March 20, 2012
Acknowledgements

• Diane Niemann, VISN

2 CIO and Team

• Jim McCain

, VA Innovations Innovations Team

• W. Ashley Byrd and Team, ICF• Joe Linza, CWI

• VISN

2 Center of Excellence for Suicide Prev

Canandaigua VAMC

• VISN 16 MIRECC

, New Orleans
Objectives

• Describe the process of developing an early

innovation project project in the VA

• Examine how a comparative effectiveness r

mod l e can be b h roug t to the f l ront ine of ca

• Discuss consideration and challenges of the 

relatively “real‐time” data

• Describe how this tool can be expanded for 

promoting patient‐centered outcomes
Early VA Innovation Innovation Project Project

• VA Innovations Program: 2008• First Stage: Greenfield Incubation

Mechanism to encourage innovation from the field

• Competitive formal submission of projects (over 20

submitted, 75 awarded) awarded)

• Now: Brief ideas, voting
Early VA Innovation Innovation Project Project

• Crash‐course in government contracting• Learning “Scope of Work” language• Experimenting in Wiki and a “sandbox”• Guidance and help from talented Innovations staff

Carley, Bill Cerniuk, Jim McCain

• Actual Start: 1 year after funding funding notification notification
Our Objectives

• There is no mechanism for individual patien

examine examine the outcomes outcomes of other patients patients lik

using the treatment they are considering.

Currently:• Results from small‐sample studies and cliniNo direct model to build from, but 2 existing u

approaches
Conceptual Model

1. Patient Self‐Report online sites:

e.g.: www.patientslikeme.com

• Patients self‐report treatment outcomes

Problem: search capability limited (gender, age, diagnosis, 

• Important search fields not available:

– Co‐morbidity ‐Family History– Smoking status ‐Concurrent Medication– Weight factors ‐Mental health i– Geographical region ‐Race/ethnicity

Etc..........
Conceptual Model

2. Comparative Effectiveness Research (CER)• CER compares treatments head‐to‐head for effecti• Problems:

EfficiencyRelevanceReplication and updatingDistant from frontline of care
SOLUTION

• Electronic Health Records (EHR) in local healthcare 

provide the missing elements:

“Grouping” powerRegional reach allows g gp eo ra hical homog 

enei

Common methods of data collection

• VHA’s VistA EHR
Formulation of Project

• EHR systems have problems of their own regarding 

access and integ y rit (data errors, missing data, etc

• Must determine the best data fields to use for bot

treatments and outcomes.

• “Medications” as the set of treatments

Easily quantifiableWell‐developed data fieldsInformatics‐friendly

• Project name: “Outcomes‐Based Prescribing” (OB
Formulation of Project

First step: Which VistA data fields are good?• Fileman queries to determine data fields to analyz• Evaluated integrity/useability of data fields with SA• Final list of “useable” fields as indicators for either 

indirect clinical outcomes.
Formulation of Project

Second step: Develop our Models: Conceptual Use

The scenario:

1. Patient receives a new diagnosis or needs to start on a new 

2. Patient reviews information on disease, identifies and beco

with potential treatments, receives input from provider

3. Patient and provider view data on how other similar patien

on the different different treatment treatment options options under consideration consideration

4. Patient and provider view additional data on cost, generic a

FDA warnings, and FDA adverse events

5. Patient and provider jointly reach treatment decision
General Software Roadmap

1. Data Retrieval and Definition:

Use near real‐time dataLocate index patient record and choose medication to eChoose comparison cohort

2. Collate Outcomes Data:

Retrieve outcomes data

3. Analysis and reporting:

Algorithms calculate expected outcomes based on pre‐parametersResults reported to computer screen
Actual Development Process

Project goal: Develop a working prototype• Developers: Developers: we partnered partnered with:

CWI for project project project oversight: oversight: oversight: Joe LinzaICF International International for software software development: development: Ashley Byrd 

• Background Background Database Database Design: Design: we worked with VIS

Dianne Nieman’s Nieman’s team to construct construct the working working da

da

Vi Ast

.
Actual Development Process

• System Architecture: Open‐source coding

Source code built using Maven.Presentation layer

• uses JSF 2.0 to display data via a web browser without refr• accesses accesses display display data through through calls to backing backing beans which 

access objects (DAO) to manipulate Hibernate persistence 

Persistence layer

• abstract layer separating direct database calls from busine

• Java objects are mapped to database tables via Hibernate 

files.

• Once mapped, persistent objects allow access to database 

their various method calls.
Actual Development Process

• Data Source: Near real‐time data from VistA

VISN 2 programmers programmers developed developed call routines routines u

data fields identified at the start

Dataset intended to be updated on a regular b

weekly)

Query routines must be run during off‐hours (

n

weekends)

NOTE: Initial work for data field testing was done wit

patient data. OBP program was developed using p

data.
Actual Development Process

Dt M a aap
Actual Development Process

Log-i S ncreen
Actual Development Process

H P omeage
Actual Development Process

P ti t L k Patient Look-up Page
Actual Development Process

M di ti l d M di ti L k Medication class and Medication Look-up Pag
Actual Development Process

R f G O t tP Reference Group Output Page
Actual Development Process

Ot S P u comes Summary Page

Novel Use?

Definition of novel : “New or unusual unusual in an int

int

way”

• EHRs do not have decision‐assistance tools for pati

can be used by both patients and clinicians.

• EHRs initiate input from outside sources, primarily 

based. OBP uses clinical data from the EHR as well 

sources

• EHRs do not clinically apply patient experience. OB

direct clinical application of data from real patients 

healthcare system.

• Few applications within EHRs can be directly portab

OBP is directly directly portable portable for use by patients patients on their 

of the PHR.

• KEY: Uses social media framework
Observations From Project

• Some large EHR systems (e.g., Epic) have tools to b

type application, but the vast majority do not

• The vast majority of EHRs have data which can driv

type system

• OBP is simple, non‐intrusive, and portable to EHR/P

environments
Next Steps

• OBP has potential to be a stand‐alone module, inte

an EHR, and as a feature of patient portals or PHRs

• Currently writing R‐18 (AHRQ) to expand prototyp

working application adding the following features:

Assess its use on non‐VistA EHR systemsDevelop versions for mobile platformsAdd ability for patient commentary regarding thtreatment experiences
Next Steps

• Expand the algorithm capability:

Use additional additional variables variables (life expectancy, expectancy, side e

e

mix, drug‐drug interactions, etc.)

Build in "Prescription Optimization”Allow users to adjust the weighting of the

model's objective function

Allow users to change the weighting of the termDetermine optimal groups of users and second

effects (e.g., impact on suicide rates)
Acknowledgements

• Diane Niemann, VISN

2 CIO and Team

• Jim McCain

, VA Innovations Innovations Team

• W. Ashley Byrd and Team, ICF• Joe Linza, CWI

• VISN

2 Center of Excellence for Suicide Prev

Canandaigua VAMC

• VISN 16 MIRECC

, New Orleans
THANK YOU!

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