Information ExtractionGroup Health

David Carrell, PhDGroup Health Research InstituteJune 29, 2010

BA

Ph

il. &

Re

lig.

BA

Ph

il. &

Re

lig.

1979

NNCNNCM

A P

ol.

Sci

ence

MA

Pol

. S

cien

ce

1983

SUNYSUNY U of WashingtonU of Washington

PhD

Pol

. S

cien

ceP

hD P

ol.

Sci

ence

1990

Pew

Hea

lth P

olic

yP

ew H

ealth

Pol

icy

1992

UCSFUCSF U of WashU of Wash

Res

earc

h, I

TR

esea

rch,

IT

1993

Group HealthGroup Health

IT,

Res

earc

hIT

, R

esea

rch

2005

David’s background ...

Group Health Research Institute (GHRI)

● Group Health (www.ghc.org)

●Founded 1947, Seattle, WA

●Integrated delivery system (“HMO”)

●~600K patients in WA (some OR, ID)

●Comprehensive EMR & patient portal (2004+)

● GHRI (www.grouphealthresearch.org)

●Founded 1983

●300 staff (50 investigators)

●2009: >250 active grants ($39M)

Group Health Research Institute (GHRI)

● Applied research

●Epidemiology, health systems, clinical trials, economics ...

●Limited bio-informatics expertise

● Collaborative

●HMO-Research Network, Cancer-RN, ... MH-RN

●Federated data systems

● NLP vision

●NLP expertise through collaboration

●Bring NLP to the text—locally ... other network sites

HMO Research Network• Large data repositories • Common EMR platforms

GHRI & Research Consortia

• Virtual Data Warehouse (VDW)

GHRI & Virtual Data Warehouse (VDW)

DiagnosesMRN

provideradate

enctypedxpdx

diagproviderorigdx

Tumor

MRN

dxdatestaging vars…tumor vars…

treatment vars…etc.

EncountersMRN

provideradate

enctypeddate

encounter_subtypefacility_code

discharge_dispositiondischarge_status

DRGadmitting_source

department

Demographics

MRNbirth_date

genderrace1-5hispanic

Pharmacy

rxdaterxsuprxamtrxmd

MRNndc

Vital SignsMRN

measure_datehtwtbmi

days_diffdiastolicsystolicposition

CensusMRNblock

blockgpcountystatetractzip

education vars...income var...race vars...

Procedures

MRNprovider

adateenctype

pxcodetype

performingproviderpxcntorigpx

NDCndc

GenericNameBrandName

Provider

SpecialtyProvider

EnrollmentMRN

enr_startenr_end

ins_medicareins_medicaid

ins_commercialins_privatepay

ins_otherdrugcov

• Structured data (legacy + Epic/EMR)

• Minimum 1990+

• Integrated care delivery (some claims)

• Diagnoses, procedures, pharmacy, tumor, vitals, census/geocode, etc.

HMO Research Network

GHRI & Virtual Data Warehouse (VDW)

GHRI & NLP Adoption

DiagnosesMRN

provideradate

enctypedxpdx

diagproviderorigdx

Tumor

MRN

dxdatestaging vars…tumor vars…

treatment vars…etc.

EncountersMRN

provideradate

enctypeddate

encounter_subtypefacility_code

discharge_dispositiondischarge_status

DRGadmitting_source

department

Demographics

MRNbirth_date

genderrace1-5hispanic

Pharmacy

rxdaterxsuprxamtrxmd

MRNndc

Vital SignsMRN

measure_datehtwtbmi

days_diffdiastolicsystolicposition

CensusMRNblock

blockgpcountystatetractzip

education vars...income var...race vars...

Procedures

MRNprovider

adateenctype

pxcodetype

performingproviderpxcntorigpx

NDCndc

GenericNameBrandName

Provider

SpecialtyProvider

EnrollmentMRN

enr_startenr_end

ins_medicareins_medicaid

ins_commercialins_privatepay

ins_otherdrugcov

Structured Information from TextPathology

MRNaccession_number

collection_date

coding_datethesaurus_version

ImagingMRN

image_numberimage_date

providercoding_date

thesaurus_version

PathologyConcepts

accession_number

concept_codeconcept_type

negated

ImagingConcepts

image_number

concept_codeconcept_type

negated

Clinical NotesConcepts

MRNprovider

adateenctype

concept_codeconcept_type

negated

HMO Research Network

GHRI & NLP Adoption

• caBIG TBPT adoption proposal, Jun 2006 • caTIES for pathology & radiology text, ~2007• Chart note text, May 2007• GWAS (eMERGE) proposal, Aug 2007• GATE experimentation, Feb 2008• Strategic planning conference, Dec 2008• ARRA Challenge Grant, Apr 2009• UIMA/cTAKES adoption, Aug 2009• Proposals... e.g.,HMORN multi-site, Jan 2010

GHRI & NLP Adoption

GHRI & NLP Adoption

● How to bring NLP capacity to clinical text?

●“Cookbooks” (SAS Java programmers)

●“Parachuted” hardware

●Parachuted virtual machine (?)

●Cloud-based processing

● Security issues●Other?

GHRI & NLP Adoption

Challenges of Cloud-based Solutions:

Unfamiliar technologies

Responsibility sharing (e.g., security)

Patient privacy

Institutional risk

De-identification

Graduated adoption?

GHRI & NLP Adoption

SHARP Cloud Security Workshop

Spring 2011

Educational focus

Challenges of processing clinical text in a novel security space (virtual firewall?)

Security best practices

IRB engagement

Graduated adoption strategies

SHARP -- Exploring deployment strategies

NLP Challenge Grant

Natural Language Processing for Cancer Research Network Surveillance Studies

• Aim 1:Deploy open-source NLP softwareDevelop ETL connective tissueBuild “human capital” (Java, NLP)

• Aim 2:NLP algorithm boot camp: Recurrent breast cancer diagnoses>3000 existing gold standard cases (human reviewed)

• Approach:Local deployment/programming supportHigh-level NLP/bioinformatics expertise via external collaboration

• Participants:GHRI (Carrell, Buist, Chubak), Mayo Clinic/Harvard (Savova), Pittsburgh (Chapman), Vanderbilt (Xu).

04/21/23 16

Epic/Clarity Chart NotesEpic/Clarity Chart Notes

Radiology Reports

Radiology Reports

Pathology Reports

Pathology Reports

UIMA/cTAKES NLP

UIMA/cTAKES NLP

RawRaw

RichRich

Document Manager

Document Manager

Document_Identifier Concept_Code

Radiology_Report_000001 2877143

Radiology_Report_000001 8600231

Radiology_Report_000001 3134988

Radiology_Report_000001 5287109

NormalizedNormalized

NLP SQL Server Database

NLP SQL Server Database

NLP Challenge Grant – Aim 1

17

DocumentType

Available Documents

Percent NLPConcept-Coded

Chart Notes 20M 25%

Radiology 4M 33%

Pathology 1.2M 2%

Chart NotesChart Notes

RadiologyRadiology

PathPath

NLP Challenge Grant – Aim 1

NLP Challenge Grant – Aim 2

NLP Challenge Grant – Aim 2

Rec Br Ca?

AE1 AE2 AE3

Progress Notes

AE1 AE2

Oncology Notes

AE1 AE2 AE3

Radiology Reports

AE1

Pathology Reports

NLP Challenge Grant – Aim 2

eMERGE consortium

• Vanderbilt, Mayo, Northwestern, Marshfield, Group Health

• Can EMRs from multiple institutions provide comparable phenotype data for GWAS?

• 14 phenotypes

• Group Health

•structured data

•Adoption of NLP algorithms developed by others

•“Low-tech” NLP

• Text explorer, Assisted chart abstraction

Clinical Text ExplorerSelect text source (chart notes,

radiology, pathology, etc.) Search: recurrent NEAR breast NEAR cancer.

Date range

Sample spec’s N documents,

N patients found

Search terms highlighted

Assisted Chart Abstraction

A-ZFull-text

Indexes

Chart notes• 550K pts• 17M notes• 0.8B lines

SQL Server

Chart notes• 550K pts• 17M notes• 0.8B lines

• Pre-processed

A-ZID A-Z

Date

CohortLists

DataWarehouse

A-ZEtc.

• Point-and-click• Outside EMR

AssistedChart

AbstractionGUI

NLPConceptCodes

Data

• Text capture

Assisted Chart Abstraction

Identify Cohort

Selection criteria applied to the patient

Selection criteria applied to the notes

Pt Dx/Px/Rx

Pt Visits

Pt Demog

Note Date

Note By

Note Type

Note Text

Assign note

priority

Assisted Chart

Abstraction

Traditional chart abstraction Assisted chart abstraction

Data

Assisted Chart Abstraction

2903(100%)

Initial cohort identification: 137,019 (100%)

671(23%)

Inclusion criteria

(demog., dx, px, etc.):

70,119 (51%)

122(4%)

Pre-processed

text:

284 (0.2 %)

228(8%)Electronic

text:

28,186 (21%)

Chart NotesPatientsStage

• Text: “CATARACT”• Note: Op/Ophthal exam• Near: Cataract procedure

Assisted Chart Abstraction

Potential SHARP synergy ...

National Cancer Institute FOA:Tools for Electronic Data Extraction

• Funding:NCI Contract for software development

• Aim:Enhance/automate existing SEER cancer case identification (largely manual abstraction of EHR/paper charts)

• Approach:Assess, propose, test, modify, develop, deploy technologies that leverage NLP to automate some aspects of SEER workflow

• Participants:IMS, Inc., SEER sites (4), Group Health, Harvard

SHARP – NLP research lab

Questions – Discussion