EPIC EMR to OMOP CDM to Research Data Mart: An … · 2017-04-06 · ü OMOP CDM is open source. No...

RESEARCH POSTER PRESENTATION DESIGN © 2015

www.PosterPresentations.com

Elicitdatarequirements

Identifyprimarydata

source

Identifyneededsubsetofsourcedatacomponents

Identifycohort

Buildandpopulatedatamart

Testandvalidatedatawiththeuser

Developuser

manuals

In this research data delivery project, we explored a less traveled path of building a clinical “data mart” for a registry study on kidney transplant patients based on our institutional OMOP database.

Background

ProjectGoals

The5ThingsWelearned

References

1. Observational Health Data Sciences and Informatics (OHDSI) Website: https://www.ohdsi.org/2. Huser V, DeFalco FJ, Schuemie M, Ryan PB, Shang N, Velez M, Park RW, Boyce RD, Duke J,

Khare R, Utidjian L, Bailey C. EGEMS (Wash DC). 2016 Nov 30; Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets. 4(1):1239. doi: 10.13063/2327-9214.1239. eCollection 2016.

3. User acceptance testing framework: https://usersnap.com/blog/types-user-acceptance-tests-frameworks/

Acknowledgements² This project is supported by the UCSF Clinical and Translational Science Institute (CTSI), part of

the Clinical and Translational Science Award program funded by the National Center for Advancing Translational Sciences (Grant Number UL1 TR000004) at the National Institutes of Health (NIH).

² We thank UCSF pSCANNER team, PI Dr. Mary Whooley, MD, project manager NirupamaKrishnamurthi, MPH and UCSF IT EIA team that implemented our institution’s instance of OMOP database, for all their support and inspiration to use OMOP CDM for research.

First, we supported the study by providing access to datao Provide ongoing access to the up-to-date clinical data on kidney transplant patients that the study

team can use to answer the research questionsSecond, we learned what it takes and how it could scaleo Learn about building a study data product, based on specific solution choices. o Assess feasibility of generalizing this approach for other studies that rely on EMR data; identify

generalizable components

1,2,3,4,5,6,7,8,9,10,11UniversityofCaliforniaSanFrancisco,CA

Oksana Gologorskaya, MS1, Meyeon Park, MD2, Debbie Huang, MS3, Robert Hink, PhD, MBA4, Vijaykumar Rayanker5, MS, Nelson Lee, MA, MBA6, Hasan Bijli, BS, MBA7, Govardhan Giri, MBA8, Amit Shetty, BS9, Leslie Yuan, MPH10, Mark Pletcher, MD, MPH11

EPICEMRtoOMOPCDMtoResearchDataMart:AnUnmaintainedRoadoraHighway?

STARTHERE:• Researcherneedsaccesstoextensive

up-to-date clinicalinformationonkidneytransplantpatientstosupportlongtermregistrystudy

Solutionchoices,methodsandthequestionswehad

o Delivery format: data mart built from the institutional OMOP data warehouseo When is it appropriate to use OMOP DB as the primary source of EMR data for research?o Data mart implementation process: what is generalizable? What recourses/time it

takes?o What else should the research team get besides access to the data mart? E.g.

documentation (user manual, including data limitations), other resources?

o Primary data source: subset of institutional EMR (Epic) data available in OMOP DBo What about adding other data sources, e.g. pathology data or kidney transplant data?o Deliverables: data mart access, documentation (user manual for data access),

including data limitations

o QA and data validation: User-centered approach: user acceptance testing and data validation procedures

o What are researcher’s expectations about the quality of data?o General best practices and understanding of working with EMR data

o Important questions that came up in the process:o How can we help the researcher use the imperfect data that’s available?o When is it right to build a data mart? What kind of projects and what kind of study

teams can fully benefit from it?

DATAREQUIREMENTS• Mostoftherequireddataareavailable

intheEMRDB,EpicClarity.• Needlabresults,medications,health

conditions,vitals,otherobservations(imagingetc.)pre- andpost-transplant

WAITCustomqueriesgettingthedatascatteredalloverEMRDB,repeatedfordatarefresh,wouldnotscale.

HOWcouldwemeettheseneeds

byspendingLESSEFFORT,andgettingMOREVALUE

inthefuture?

1. Dataisneverperfectbutyoucanstilltrustitifyouunderstandit!Inordertousethedatainthebestway,andtotrustourdata,weneedtounderstanditslimitations.Present/analyzethedataalongwiththelimitations,basedonthelevelofevidencethedataprovides.

2. Studyteam’sinvolvementinthequalitycontrol/validationofthedatawasextremelyeffective.WeadoptedaUserAcceptanceTestingmethodaspartofourdatadeliveryprocess.WedevelopedauseracceptancetestingprocedurefortheresearchdatamartthatmaynowbeusedasamodelforallresearchdatadeliveryprojectsatUCSF

3. SettingexpectationswiththeresearcherisimportantSetexpectationswiththeresearcheraboutthequalityofdata,thecomplexityofthedataandthenecessityoftheirinvolvementintheprocessofdatadelivery

4. Advantages of using OMOP-based vs. Epic CLARITY data sourceü OMOPisaresearch-orienteddatamodel.AlternativetoCLARITYreports,potentiallyfaster

access,easyenoughforskilledanalysttouseindependentlyü OMOPCDMisopensource.NoneedtogotoCLARITYtrainingtolearnthedatamodelü Commondatamodels(CDMs)sharedacrossmanyorganizationsallowthesameanalyticalcode

tobeexecutedonmultipledistributeddatasets.Insomecases,adherencetoaCDMisaprerequisiteforparticipatingonagrant(orresearchnetwork).[2]

5. OMOP data quality issues we found sparked internal OMOP QA initiative

Implementingresearchdatamart– whatcanbestreamlined?

Study-specific, manual work Reusable method Reusable code, tools and deliverables and much faster execution in repeat projects

We believe that building OMOP-based data marts is a very efficient way to deliver data for research because for the next similar project, we can replicate this solution, plug-in a new cohort and be done!

ImplementationHighlights:² ETL/Dataintegrationtool:IBMInfoSphere DataStage² Dataflow:UCSFEPICCLARITYEMR->UCSFOMOPDB->ResearchDatamart² UCSFOMOPversion:v4,beingupgradedtov5² SourceDBplatform:SQLServer² TargetDB:SQLServer² Refreshfrequency:Weekly² datamart accessforstudydataanalyststoquerydirectlyintheDBorfromSAS.

ContactOksana GologorskayaSr. Product Manager, Research Technologyhttp://profiles.ucsf.edu/oksana.gologorskayaClinical & Translational Science Institute (CTSI)University of California, San Francisco (UCSF)550 16th St, 6th Floor, San Francisco, CA 94143-0558