CER Hub Platform for Comparative Effectiveness Research

The CER Hub: An Informatics Platform for
Conducting Comparative Effectiveness
Research with Comprehensive Electronic
Medical Record Data

Brian Hazlehurst, PhD
Kaiser Permanente Northwest
Center for Health Research

© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH

Outline
 Why do we need the CER Hub?
 The CER Hub extends and makes available an
automated medical record classifier (MediClass)
 The development of projects using the CER Hub
 The current CER Hub members and projects
under way


CER requires LOTS of data
 Diverse populations, many topic areas
 Increasing adoption of EMR systems provides an
emerging opportunity for developing large
databases
 KP covers ~9M lives @ 4 encounters/yr, roughly
100,000 encounters per day captured in the EMR
 A vast amount of this data is captured in unstructured (non-
coded) text


Example clinical encounter record segments
addressing family and personal hx for cancer
Clinical note segment written Relevant ICD9 dx code applied
Med Hx:Asthma-Azmacort, Ventolin, rarely prednisone
1 Surg Hx:neg None
Family Hx:Fa-aodm, pgf colon ca, mgm bone marrow ca
------------------------------------------------------------------------ ------------------------------------------
Last Mammogram: 1 yr ago. None
Previous Paps have been normal
2 There is a strong family hx of breast cancer.(M, MGM,Aunt)
------------------------------------------------------------------------ ------------------------------------------
RN noted S OB comma asked that I see pt. 174.9 CA FEMALE BREAST,
She has invasive ductal CA of the breast, and is getting INFILTRATING DUCTAL
3 chemo.
Has today become more acutely SOB.
------------------------------------------------------------------------ ------------------------------------------
ROS: neg for exertional chest pain or pressure, shortness V16.0 FAMILY HX
breath, changes inbowel habits. MALIGNANCY GI TRACT
4 Fam Hx: + early MI, colon cancer-- sister in her 50s


Coverage of the RAND QA measures by
standardized CODED data
1
0.9
0.8 Claims
% of QA measures

0.7
Claims+Lab
0.6
Claims+Proced
0.5
Claims+Vitals
0.4
Claims+S/S
0.3
0.2 Claims+All
0.1
0
Data Sources

The remainder necessary for comprehensive quality assessment
is found in either the templated- or free-text clinical notes of the
EMR!

The CER HUB
 A web-based platform for collaborative development of
study-specific, standardized, processors of
comprehensive electronic medical record data.
 Site data is extracted locally in industry standard form (HL7 CDA)
 Centrally developed processor of entire medical record creates a
standardized and reusable/extensible resource for CER Hub users
 Sensitive source clinical data (e.g., text progress notes) remain under local
control and is extracted on demand for specific projects
 Standardized (study-specific) datasets that are generated by applying the
processor locally are pooled to answer targeted study questions centrally
and these remain under study-team’s control


Why do we need the CER Hub?
 EMR adoption promises LOTS of data, but the data are
heterogeneous both across and within institutions
 EMR’s are variable (diverse representation of events)
 Clinical practices are variable (diverse priorities and capture of events)
 Patients are variable (diverse conditions and needs)
 Need scalable informatics solutions allowing
assignment of consistent (and specific) meanings to
highly heterogeneous data
 want to remove spurious variation to highlight the “real” variation specific
to a study question


MediClass (Medical Classifier)
 Utilize a standard representation for electronic medical
record data (HL7 Clinical Document Architecture, CDA-
CCD)
 potential to process records of any EMR.
 Process both text and coded data in the EMR
 potential to process any type of data captured in the EMR.
 Allow for modular definition of measures or study variables
(classifications determined by plug-in “knowledge modules”)
 potential to apply any specific measure.
 Capable of local installation and operation
 potential to create shareable, standardized research data

MediClass System
CDA Medical CDA
EMR Record(XML) Parser EMR Integration
System
(data CDA Medical Record
warehouse) ( Java object model)

Concept CDA w/Free
Identifier Text Concepts
Unified
Medical
Concept
Language Coded Data CDA w/Free Text
Identification
System Concept Concepts& Structured
(UMLS) Data Concepts
Knowledge Mapper

Module Event Classifier
Clinical Event
Classification
Rulebase CDA w/Free Text Classification
Concepts& Structured
Data Concepts & Event
Classification

Results
JAMIA Sep-Oct, 2005 Repository

Summary of MediClass study results
Project Title Funder Description Key Results
Automated Assessment of
Use of electronic medical
Asthma Incidence and Sensitivity, 62-95%;
CDC records for surveillance of
Prevalence (manuscript in Specificity, 90-100%
asthma in an HMO
preparation)
Development of Sensitivity(1), 62-92%;
Automating Assessment of
comprehensive automated Specificity(1), 75-93%.
Asthma Care Quality (in AHRQ
assessment of outpatient Sensitivity(2), 35-69%
press, AJMC)
asthma care quality Specificity(2), 69-95%
Vaccination Safety Datalink: Detection of possible
Adverse Vaccine Event vaccine-related adverse Sensitivity, 75-81%;
CDC
Detection (Hazlehurst et al, events in large-linked Specificity, 97-98%;
2009 -- Vaccine) databases
Identifying Family and Identification of
Personal History of Cancer in breast/ovarian and other
Sensitivity, 62-98%;
the EMR (Hazlehurst et al, NCI cancer family and
Specificity, 97-99%
2005 HMORN research conf personal history in
poster) progress notes
HMO Interventions in Assessing compliance Sensitivity, 64-100%;
Tobacco (Hazlehurst et al, NCI with the 5A’s guideline in Specificity, 82-100%
2005 – AJPM) four HMO’s (four HMO’s)


The CER HUB
 A web-based platform for collaborative
development of study-specific, standardized,
processors of electronic clinical datasets.

 A web site with functions related to building,
testing, sharing, study-specific processors of
heterogeneous clinical data.


The CER HUB: Building
 A set of tools for collaborative development of
study-specific data processors.
 Operationalizing study variables in terms of concepts
identified in clinical records
 These variables may involve concepts identified in text and/or
structured data elements of clinical records.
 Eg., “persistent asthma” can be operationalized in terms of
sequences of asthma medication fills, exacerbation visits, and
clinician assessment in the progress note.


The CER HUB: Testing
 Developed processors can be run on the HUB
against (de-identified) test datasets to evaluate
the data processor.
 Allows for rapid development of knowledge
modules through iterative test-refine cycles.
 Creates validation metrics that provide a “profile”
about the data processor that is retained in the
library.


The CER HUB: Sharing
 A web site hosting virtual communities of
researchers with shared interests (i.e., organized
around a shared study topic).
 A library of study-specific data processors are
available for download as applications
addressing a range of research questions.
 Researchers who join the consortium build out
the library over time through their activities using
the HUB for their research.

The CER HUB workflow
1) Develop 2) Develop and validate 3) Configure the
a study protocol a standardized data processor for your site
processor

(define study measures (operationalize (defines site-specific
and populations of study measures based parameters
interest) on concepts in data) for the processor)

De-identified Download
Pop def
samples processor
1a) Data extraction 4) Apply processor 5) Pool standardized,
to local data sharable data for
analysis

(extract data in a
standard format)

1a) Data Extraction (EMR Integration)
Study
Protocol Clinical
Study pop
and data Research Data
elements Dictionary
HL7
EMRAdapter CDA
(Schema Schema
EMR Site specific Site Specific Mapper)
schema CRD
Warehouse schema
(CSV or XML) Schema
Schema mapping
EMR-to-CDA
mapping
De-identify
and upload
to Hub
E M R D a ta s e t
S ite S p e c ific
(C S V , X M L )

EMR
W a re h o u s e
EM R D a ta
(RD B M S )

D a ta s e t
Dataset Adapter

CD A
XML
Publish (Runtime
Engine)
MediClass
Application

2) Data Processor Development and
Validation F ro m S te p 1 a n d 1 a

Study Protocol w/ Uploaded de -
operationalized identified data
measures

Chart
Define/
abstraction Mediclass
refine
using Processes Job
concepts
Manual Job
and rules
Coder tool
• Direct Inspection of
Development classification results
data • Comparison to Manual
Validation Data Coding (development )
• Comparison to Gold No
Gold
Standard (validation)
Std
Aggregate Done.
manual Processor is
coding using ready for
Gold download
Standard Satisfactory
Maker Performance ?
Yes
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH Processor building tools
Processor Validation Tools

CER HUB
Study Protocol

Population and data Knowledge module Application specific
element selection for and Configuration for extraction filter for study
encounter -based specific application specific (and sharable )
extraction events

Study variables
operationalized in
terms of temporally
located events

MediClass EventsDataset
EMRAdapter Post-Processor
Application Processor

CDA
CDA w/ MediClass EventsDataset Study
Data Warehouse
(XML) Classifications (Flat file ) Measures
(XML)

Data Extraction Event Identification Study Analyses

Local Site Local Site DCC

Outline
 Why do we need the CER Hub?
 The CER Hub extends and makes available an
automated medical record classifier (MediClass)
 The development of projects using the CER Hub
 The CER Hub project: members and studies
under way


The CER HUB Project
 A consortium of researchers from 6 health
systems
 KPNW, KPGA, KPHI,
 VA PugetSound,
 Baylor HealthCare System,
 OCHIN (consortium of FQHCs mostly on west coast)
 Developing and using the CER HUB to address
effectiveness questions in asthma control
therapy and smoking cessation services

CER HUB Project Specific Aims
1. Develop, operate, and evaluate a centralized CER service on the Internet
that provides automated tools, methods, and support for generating
standardized datasets to answer CER questions.

2. Utilize the CER HUB to develop and validate an EMR-based measure of
“asthma control" in accord with established national guidelines, and
evaluate effectiveness of treatment intensification options on asthma
control.

3. Utilize the CER HUB to assess implementation of the US Preventive
Services Task Force evidence-based tobacco treatment guideline (the
5A’s) in the six participating organizations, and evaluate the comparative
effectiveness of smoking cessation services on quitting in whole
populations of patients in the course of real-world, routine clinical care.

Asthma Control
Impairment Risk
 Asthma-related steroid use –
 Asthma symptoms (wheezing, chest orders or dispensings consistent
cough, chest tightness, or shortness of with 2 or more courses in past 12
breath) > two days per week; months;
 Experiencing night-time awakening one or  ED visits or hospitalizations – 2 or
more in past 12 months;
more per week;
 Progressive loss of pulmonary
 Using reliever medications more than two function over time (by spirometry
days per week; or peak flow testing);
 Symptoms interfere with normal activity;  Medication side effects, such as
dysphonia, thrush, osteoporosis
 Reporting unacceptable control; (for inhaled corticosteroids),
 Low asthma questionnaire score (e.g., ACT nervousness and
tachydysrhythmia (for beta-2-
score < 19); agonists)
 FEV1< 80% predicted and/or PEFR <80%
best;

Compare effectiveness of step-up therapies
for asthma control

Main Asthma study CER question:

For patients on low-dose inhaled corticosteroid therapy whose asthma is
not well controlled (i.e., failed EPR-3 Step 2 therapy), we will investigate
the comparative effectiveness of the following step-up therapies (i.e.,
EPR-3 recommended Step 3 therapies)
(1) addition of a leukotriene modifier
(2) addition of a long-acting beta-agonist
(3) increase to medium-dose inhaled corticosteroids

On the basis of efficacy studies, options 2 and 3 are considered
‘first-line’ options.


CER Hub Study Protocol - Asthma Care
Possible Asthma Definition
“Possible Asthmatic" is defined by patient having received at least one
ICD-9-CM diagnosis code at any visit during study period of 493.xx

Study Inclusion Protocol
We will include all patients, 12 years and older on 1/1/2006, identified as
possible asthmatic (see definition above) during observation period
(1/1/2006-12/31/2010) and also assess outcomes (2011-2012)

Persistent Asthma Definition
We will focus on patients whose asthma is “persistent” using the
developed data processor that will consider medication usage (orders and
dispenses), visits (inpatient, outpatient, and ED), and clinical judgement
(clinician assessment that the patient has persistent asthma as
documented in the progress note).


CER Hub Possible Asthma Population

TOTAL
DISTINCT
Study Sites 2006 2007 2008 2009 2010 PATIENTS
Baylor 3166 6138 9836 6504 4850 30494
KPSE 9858 10224 10357 10917 5266 26756
KPHI 12637 12182 12227 12756 12324 33349
KPNW 21342 22495 23677 24741 24731 64764
OCHIN 1997 3870 6204 11260 15306 26922
VA-PS 1377 1668 1880 1972 2235 4667
TOTAL
DISTINCT
PATIENTS 50377 56577 64181 68150 64712 186952


Asthma Investigators
 Rich Mularski, MD (KPNW)
 Michael Schatz, MD (KPSC)
 Jerry Krishnan, MD, PhD (U of Chicago)
 David Au, MD (VAPS)
 Mark Millard, MD (Baylor)
 Bob Davis, MD (KPGA)


The 5 A’s of Smoking Cessation
5A step Operational Example in free-text
definition section of EMR

Ask Identify tobacco user “Patient smokes 1 ppd”
status at every visit
Advise Advise all tobacco “It is important for you
users to quit to quit smoking now”
Assess Determine patient’s “Patient not interested
willingness to in quitting smoking”
make a quit
attempt
Assist Aid the patient in “Started patient on
quitting Zyban”
Arrange Schedule follow-up “Follow-up in 2 weeks
contact, in person for quit progress”
or via telephone


CER Hub Study Protocol-Smoking Cessation
Smoker Definition
"Current smoker" is defined on an annual basis as having received at
least one of the following during a calendar year:
1) ICD-9-CM diagnosis code indicating “tobacco abuse” at any visit
2) An update of their social history to indicate "current smoker“
Additional measures are defined for “Quitter” (someone who recently
quit) and “Former Smoker” (someone who has stayed quit).

Study Inclusion Protocol
Unique patients, 12 years and older, identified as:
1) current smoker and
2) having received primary care (one or more primary care visits)
All such patients will be included in the study and will be flagged as to
their status according to these measures in each of the study years
(2006 – 2010) and outcomes assessed (2011 – 2012).


CER Hub Smoker Population

TOTAL
DISTINCT
Study Sites 2006 2007 2008 2009 2010 PATIENTS
Baylor 2707 12338 22741 22162 24025 58616
KPSE 17385 16067 15188 13722 6534 37868
KPHI 19160 17849 17164 18406 20896 51847
KPNW 47202 47786 46375 50944 50630 120328
OCHIN 8489 14726 23769 39946 56340 78736
VA-PS 10944 12334 13570 15052 15860 30535
TOTAL DISTINCT
PATIENTS 105887 121100 138807 160232 174285 377930


Smoking Cessation Investigators

 Victor Stevens, PhD (KPNW)
 Rebecca Williams, PhD (KPHI)
 Nancy Rigotti, MD (Harvard)
 Leif Solberg, MD (Health Partners)
 Andrew Williams, PhD (KPHI)
 Andrew Massica, MD (Baylor)


Informatics Investigators
 Brian Hazlehurst, PhD (KPNW)
 Yan Xiao, PhD (Baylor)
 Jon Puro (OCHIN)
 Paul Nichol, MD (VAPS)
 MaryAnn McBurnie, PhD (KPNW)


www.cerhub.org


CER Hub Platform for Comparative Effectiveness Research

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