Supporting Researchers with Electronic Patient Data … · 05/12/2019 · translational research is difficult – Repurpose transactions for research – Use one or more tools –

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Thomas R. Campion, Jr., Ph.D.Associate Professor of Research in Healthcare Policy & ResearchDirector, Research Informatics

Supporting Researchers with Electronic Patient Data

12.5.19

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• Problem• Approach

– Research Computing at Weill Cornell Medicine– Architecture for Research Computing in Health (ARCH)

• Discussion

Overview

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• Obtaining electronic health record (EHR) data for clinical and translational research is difficult– Repurpose transactions for research– Use one or more tools– Understand strengths and limitations– Obtain approval

• Optimal approaches are unknown• Research Informatics can help investigators

– Obtain EHR data– Collect novel measures– Integrate data

Problem

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Approach: Research Computing at WCM

Information Technology Infrastructure

Scientific Computing

Research Informatics

Research Administrative

Computing

Conduct Administration

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Architecture for Research Computing in Health (ARCH)

Electronic Data

Capture (EDC)

EDC integrated with EHR

Research Data

Repositories

College-wide

Cohort Discovery

Multi-institutional

Data Sharing

EHR Interventions

EHR Reporting

Consent

WCM-NYP-CUMC Data Sharing

Biobank and Ancillary Omics

Data Core

Data Integration Clinical Translation Compliance & PlanningScientific Computing

Retrospective Prospective

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Data Integration

Electronic Data

Capture (EDC)


College-wide

Cohort Discovery

Multi-institutional

Data Sharing


Research Data

Repositories

EHR Reporting

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Data Integration

Electronic Data

Capture (EDC)


College-wide

Cohort Discovery

Multi-institutional

Data Sharing


Research Data

Repositories

EHR Reporting

8


Data Integration

Electronic Data

Capture (EDC)


College-wide

Cohort Discovery

Multi-institutional

Data Sharing


Research Data

Repositories

EHR Reporting

Data Core


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Data Core


Data Integration

Model

Grumbach K, Lucey CR, Johnston SC. JAMA. 2014 Mar 19;311(11):1109-10

Retrospective

Electronic Data

Capture (EDC)


College-wide

Cohort Discovery

Multi-institutional

Data Sharing


Research Data

Repositories

EHR Reporting


10

Data Core


Data Integration

Model


Retrospective

Electronic Data

Capture (EDC)


College-wide

Cohort Discovery

Multi-institutional

Data Sharing

EHR Interventions

Clinical Translation


Research Data

Repositories

EHR Reporting


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Data Core


Data Integration

Model


Retrospective

Electronic Data

Capture (EDC)


College-wide

Cohort Discovery

Multi-institutional

Data Sharing

EHR Interventions

Clinical Translation


Research Data

Repositories

EHR Reporting


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Retrospective ProspectiveRetrospective

Research Data

Repositories

Electronic Data

Capture (EDC)

EHR Reporting

College-wide

Cohort Discovery

Multi-institutional

Data Sharing

Big Data Analytics

EHR Interventions


SUPERNLP

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Research Data

Repositories

Electronic Data

Capture (EDC)

EHR Reporting

College-wide

Cohort Discovery

Multi-institutional

Data Sharing

Big Data Analytics

EHR Interventions


SUPER

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Research Data

Repositories

Electronic Data

Capture (EDC)

EHR Reporting

College-wide

Cohort Discovery

Multi-institutional

Data Sharing

Big Data Analytics

EHR Interventions


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Step 1: obtain raw EHR data describing diagnoses assigned in patients encounters

ARCH: EHR Reporting

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Step 2: match raw EHR data describing diagnoses to ICD-9 codes

ARCH: EHR Reporting

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Step 3: transform raw EHR data describing ICD-9 codes into dichotomous scientific variable

ARCH: EHR Reporting

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Step 4: repeat process to define additional diagnosis dichotomous variables

ARCH: EHR Reporting

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Step 4: repeat process to transform raw EHR data for medications and other domains (raw data for medications and other domains not pictured)

ARCH: EHR Reporting

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Research Data

Repositories

Electronic Data

Capture (EDC)

EHR Reporting

College-wide

Cohort Discovery

Multi-institutional

Data Sharing

Big Data Analytics

EHR Interventions


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ARCH: i2b2

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ARCH: i2b2

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ARCH: i2b2

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ARCH: i2b2

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ARCH: i2b2

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ARCH: i2b2

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ARCH: i2b2

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ARCH: i2b2

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ARCH: i2b2

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ARCH: i2b2

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ARCH: i2b2

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Research Data

Repositories

Electronic Data

Capture (EDC)

EHR Reporting

College-wide

Cohort Discovery

Multi-institutional

Data Sharing

Big Data Analytics

EHR Interventions


SUPER

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ARCH: SUPER REDCap

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3434

ARCH: SUPER REDCap

3535

ARCH: SUPER REDCap

3636

ARCH: SUPER REDCap

3737

ARCH: SUPER REDCap

3838

ARCH: SUPER REDCap

3939

ARCH: SUPER REDCap

4040

ARCH: SUPER REDCap

4141

ARCH: SUPER REDCap

4242

ARCH: SUPER REDCap

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ARCH: SUPER REDCap on FHIR

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Electronic Data

Capture (EDC)


College-wide

Cohort Discovery

Multi-institutional

Data Sharing

EHR Reporting

Research Data

Repositories

ARCH: Architecture for Research Computing in Health

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Electronic Data

Capture (EDC)


College-wide

Cohort Discovery

Multi-institutional

Data Sharing

EHR Reporting

Research Data

Repositories


46


Electronic Data

Capture (EDC)


College-wide

Cohort Discovery

EHR Reporting

Research Data

Repositories


Multi-institutional

Data Sharing

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Electronic Data

Capture (EDC)


College-wide

Cohort Discovery

EHR Reporting

Research Data

Repositories


Multi-institutional

Data Sharing

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Electronic Data

Capture (EDC)


College-wide

Cohort Discovery

EHR Reporting

Biobank

Research Data

Repositories


Multi-institutional

Data Sharing

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Electronic Data

Capture (EDC)


College-wide

Cohort Discovery

EHR Reporting

Biobank

Research Data

Repositories


Multi-institutional

Data Sharing

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ARCH: Research Data Repository

Data

RDR

Other

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SUPERData

Tools

RDR

Other

ARCH: Research Data Repository

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SUPERData

Tools

RDR

Other

ARCH: Research Data Repository• Discovery

– Hypothesis generation– Patient counts

• Collection– Capture of novel

measures– Annotation of existing

data

• Analysis– Scientific variable

definition– Hypothesis testing

Workflows

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SUPERData

Tools

RDR

Other

ARCH: Research Data Repository• Discovery

– Hypothesis generation– Patient counts

• Collection– Capture of novel

measures– Annotation of existing

data

• Analysis– Scientific variable

definition– Hypothesis testing

Workflows

Study 1Study 2Study n: future

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• All of Us Research Program (Kaushal)• Anesthesiology (Turnbull)• Center for Advanced Digestive Care (Shah)• Clinical and Translational Neuroscience Unit (Kamel)• Dalio Institute (Min)• Health Informatics (Pathak/Zhang)• Leukemia Program (Roboz)• Myeloproliferative Neoplasms (Scandura)• Neurological Surgery (Hoffman)• Ophthalmology (D’Amico)• Pediatric Epilepsy (Grinspan)• Pediatric Pulmonology (Ono)• Pulmonary and Critical Care (Schenck)• Urology (Hu)• Trauma and Surgical Critical Care (Winchell)

ARCH: Research Data Repositories

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ARCH RDRs: Data Catalog

http://datacatalog.weill.cornell.edu

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• Research Informatics can help investigators– Obtain EHR data– Collect novel research data– Integrate data

• ARCH matches investigators with right tools and services with respect to– Study design– Data sources– Cost

Discussion

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AcknowledgmentsFunding

– Clinical and Translational Science Center (UL1 TR000457)

– Joint Clinical Trials Office

ARCH Leadership– Curtis Cole, MD– John Leonard, MD– Mark Weiner, MD

ITS Research Informatics– Sajjad Abedian– Prakash Adekkanattu– Marcos Davila– Xiaobo Fuld– David Kraemer– Steven Flores– Joseph Kabariti– Ryan McGregor– Sean Pompea– Julian Schwartz– Evan Sholle– Scott Turner– Jacob Weiser

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• Access and inquiries– ARCH inquiries: [email protected]– i2b2 access: [email protected]

• Web resources– ARCH: http://arch.weill.cornell.edu– TRAC: https://webapps.nyp.org/trac– Data Catalog: http://datacatalog.weill.cornell.edu

• Tom Campion– [email protected]– 646-962-2345– http://vivo.med.cornell.edu/display/cwid-thc2015

Questions

Documents

Supporting Researchers with Electronic Patient Data … · 05/12/2019 · translational research is difficult – Repurpose transactions for research – Use one or more tools –