cer hub an informatics platform for conducting compartive effectiveness with emr hazlehurst
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© 2012, KAISER PERMANENTE CENTER FOR HEALTH 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
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
Example clinical encounter record segments addressing family and personal hx for cancer
Med Hx:Asthma-Azmacort, Ventolin, rarely prednisone Surg Hx:negFamily Hx:Fa-aodm, pgf colon ca, mgm bone marrow ca ------------------------------------------------------------------------Last Mammogram: 1 yr ago. Previous Paps have been normal There is a strong family hx of breast cancer.(M, MGM,Aunt) ------------------------------------------------------------------------RN noted S OB comma asked that I see pt. She has invasive ductal CA of the breast, and is getting chemo. Has today become more acutely SOB. ------------------------------------------------------------------------ROS: neg for exertional chest pain or pressure, shortness breath, changes inbowel habits.Fam Hx: + early MI, colon cancer-- sister in her 50s
None
------------------------------------------None
------------------------------------------174.9 CA FEMALE BREAST, INFILTRATING DUCTAL
------------------------------------------V16.0 FAMILY HX MALIGNANCY GI TRACT
Clinical note segment written Relevant ICD9 dx code applied
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© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
Coverage of the RAND QA measures by standardized CODED data
The remainder necessary for comprehensive quality assessment is found in either the templated- or free-text clinical notes of the EMR!
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Data Sources
% o
f Q
A m
easu
res Claims
Claims+Lab
Claims+Proced
Claims+Vitals
Claims+S/S
Claims+All
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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
© 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
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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
EMR System(data
warehouse)
Event ClassifierClinical Event Classification
Rulebase
Results Repository
CDA w/Free Text Concepts& Structured
Data Concepts & Event Classification
CDA Medical Record(XML)
CDA Parser
CDA Medical Record( Java object model)
Coded Data Concept Mapper
Unified Medical
Language System (UMLS)
CDA w/Free Text Concepts
CDA w/Free Text Concepts& Structured
Data Concepts
Concept Identifier
ConceptIdentification
EMR Integration
Classification
JAMIA Sep-Oct, 2005
KnowledgeModule
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
Summary of MediClass study resultsProject Title Funder Description Key Results
Automated Assessment of Asthma Incidence and Prevalence (manuscript in preparation)
CDCUse of electronic medical records for surveillance of
asthma in an HMO
Sensitivity, 62-95%;Specificity, 90-100%
Automating Assessment of Asthma Care Quality (in press, AJMC)
AHRQ
Development of comprehensive automated assessment of outpatient
asthma care quality
Sensitivity(1), 62-92%;Specificity(1), 75-93%.Sensitivity(2), 35-69%Specificity(2), 69-95%
Vaccination Safety Datalink: Adverse Vaccine Event Detection (Hazlehurst et al, 2009 -- Vaccine)
CDC
Detection of possible vaccine-related adverse
events in large-linked databases
Sensitivity, 75-81%;Specificity, 97-98%;
Identifying Family and Personal History of Cancer in the EMR (Hazlehurst et al, 2005 HMORN research conf poster)
NCI
Identification of breast/ovarian and other
cancer family and personal history in
progress notes
Sensitivity, 62-98%;Specificity, 97-99%
HMO Interventions in Tobacco (Hazlehurst et al, 2005 – AJPM)
NCIAssessing compliance
with the 5A’s guideline in four HMO’s
Sensitivity, 64-100%;Specificity, 82-100%
(four HMO’s)
© 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
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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.
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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.
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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.
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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 workflow1) Developa study protocol
(define study measuresand populations of interest)
2) Develop and validatea standardized data processor
(operationalizestudy measures basedon concepts in data)
3) Configure the processor for your site
(defines site-specific parameters for the processor)
4) Apply processor to local data
5) Pool standardized,sharable data for analysis
1a) Data extraction
(extract data in a standard format)
De-identifiedsamples
Downloadprocessor
Pop def
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
1a) Data Extraction (EMR Integration)E
MR
Da
taW
are
ho
use
(RD
BM
S)
Study pop and data elements
CD
A
Da
tase
tX
ML
EMR-to-CDA mapping
EMR Adapter
(Runtime Engine)
Site
Sp
ecific
E
MR
Da
tase
t (C
SV
, X
ML
)
Site Specific schema
(CSV or XML)
Dataset Publish
HL7CDA
Schema
EMR Warehouse
Schema
MediClassApplication
Study Protocol
Site specific schema mapping
EMRAdapter(Schema Mapper)
CRD Schema
Clinical Research Data
Dictionary
De-identify and upload
to Hub
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCHProcessor Validation Tools
Aggregate manual
coding using Gold
Standard Maker
Satisfactory Performance?
Define/refine
concepts and rules
MediclassProcesses
Job
GoldStd
Yes
Job
No
Processor building tools
Chart abstraction
using Manual
Coder tool
Validation Data
Done.Processor is
ready for download
Development data
From Step 1 and 1a
Study Protocol w/operationalized
measures
Uploaded de-identified data
· Direct Inspection of classification results
· Comparison to Manual Coding (development)
· Comparison to Gold Standard (validation)
2) Data Processor Development and Validation
EMRAdapter
Data WarehouseCDA
(XML)
MediClass Application
CDAw/ MediClass Classifications
(XML)
Post-Processor
EventsDataset(Flat file)
EventsDatasetProcessor
StudyMeasures
Data Extraction Event Identification Study Analyses
Study variables operationalized in terms of temporally located events
Application specific extraction filter for study specific (and sharable) events
Knowledge module and Configuration for specific application
Population and data element selection for encounter-based extraction
Study Protocol
Local Site Local Site DCC
CER HUB
© 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 CER Hub project: members and studies
under way
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
CER HUB Project Specific Aims1. 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.
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
Asthma Control
Risk Asthma-related steroid use –
orders or dispensings consistent with 2 or more courses in past 12 months;
ED visits or hospitalizations – 2 or more in past 12 months;
Progressive loss of pulmonary function over time (by spirometry or peak flow testing);
Medication side effects, such as dysphonia, thrush, osteoporosis (for inhaled corticosteroids), nervousness and tachydysrhythmia (for beta-2-agonists)
Impairment Asthma symptoms (wheezing, chest
cough, chest tightness, or shortness of breath) > two days per week;
Experiencing night-time awakening one or more per week;
Using reliever medications more than two days per week;
Symptoms interfere with normal activity; Reporting unacceptable control; Low asthma questionnaire score (e.g., ACT
score < 19); FEV1< 80% predicted and/or PEFR <80%
best;
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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.
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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 DefinitionWe 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).
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
CER Hub Possible Asthma Population
Study Sites 2006 2007 2008 2009 2010
TOTAL DISTINCT
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
TOTALDISTINCTPATIENTS 50377 56577 64181 68150 64712 186952
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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)
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
The 5 A’s of Smoking Cessation
5A step Operationaldefinition
Example in free-textsection of EMR
Ask Identify tobacco userstatus at every visit
“Patient smokes 1 ppd”
Advise Advise all tobaccousers to quit
“It is important for youto quit smoking now”
Assess Determine patient’swillingness tomake a quitattempt
“Patient not interestedin quitting smoking”
Assist Aid the patient inquitting
“Started patient onZyban”
Arrange Schedule follow-upcontact, in personor via telephone
“Follow-up in 2 weeksfor quit progress”
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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 visit2) 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 ProtocolUnique 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).
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
CER Hub Smoker Population
Study Sites 2006 2007 2008 2009 2010
TOTAL DISTINCT
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
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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)
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
Informatics Investigators
Brian Hazlehurst, PhD (KPNW) Yan Xiao, PhD (Baylor) Jon Puro (OCHIN) Paul Nichol, MD (VAPS) MaryAnn McBurnie, PhD (KPNW)
© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
www.cerhub.org
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