optimizing data requests for the clinical research data...
TRANSCRIPT
Optimizing Data Requests for the Clinical Research Data Warehouse
November3,2016
TimHolper,MS,MADirectorofDataWarehouse&BusinessIntelligence,CRIJulieJohnson,MPH,RNSeniorHealthcareBusinessAnalyst,CRI
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Disclaimer and Permissions
• ThiseducaKonalacKvityisbeingpresentedwithouttheprovisionofcommercialsupportandwithoutbiasorconflictofinterestfromtheplannersandpresenters.
• Thepurposeofthesematerialsistoprovideinsightintothevarietyofdatasources,aswellastherecommendedprocessforobtainingdatafromtheAnalyKcsCorewithinUCMC
• ThesematerialsarenotmeanttoprovideanexhausKveanalysisof
alldatasourcesandmeansforrequesKngdatawithintheinsKtuKon
• ItisnotpermissibletosharethesematerialsoutsideUCMC
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Agenda • WhatisaDataWarehouse?• WhatistheCRDW?• CRDWI/O• DataWarehousingChallenges• QualityofDataSources• OtherDataWarehousesatUCMC• HowdoIrequestdata?• AnalyKcsCoreandACReS• CRDWRequestTriage• DataRequestChallenges• RegulatoryCompliance• Semi-SelfServiceTools• QuesKons?
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What Is a Data Warehouse?
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What Is a Data Warehouse?
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What Is a Data Warehouse? • BillInmon,generallyconsideredtobethefatherofmodern
datawarehousing,describesadatawarehouseas:‘Asubjectoriented,nonvola3le,integrated,3mevariantcollec3onofdatainsupportofmanagement'sdecisions’
• Whatdoesthismean?
• ADataWarehouseisarepositoryofveryspecificandverystaKcdataorganizedinways(usuallybyKmeandbysomeothersetofa`ributes),updatedonaregularbasis,sothatthedatacanbeusedbyfolkstomakedecisions
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What Is a Data Warehouse?
• SubjectOriented:Dataorganizedintologicalsubjectareas
• Integrated:RemovalofinconsistenciesregardingnamingconvenKonandvaluerepresentaKons
• Nonvola3le:Datastoredinread-onlyformatandupdateddaily
• TimeVariant:Datanotinreal-Kme
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What Is a Data Warehouse?
• Forthepurposesofthisdiscussion,aDataWarehouseisacentralrepositoryforstoringandextracKngthemostcommonly-requesteddataelementsrequiredtoansweraquesKon
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Data Warehouse Is a Physical Database
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Data Warehousing Is a Process Thefastesthardwareandthelatestandgreatestsohwareisonlyasmallpartofthatprocess
Withoutgoodprocessesinplaceformanagingdata,requesKngdata,governanceofdata,consistentstandards,andqualitycontrol,thebesthardwareandsohwarewilldoonething:serveupbaddatafaster
Morethanjusthardwareand
sohware
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What Is the CRDW? • CRDWisarepositoryofUniversityofChicagomedicaldatadaKngbackto2006.TheCRDWteambringstogetherdatafromdisparatesources,includingEpicelectronicmedicalrecords,Centricitybilling,CancerRegistry,REDCap,andLabvantagetocreatecohesivedatasetsforresearch.
• CRDWisaninterfacefor‘seamlessly’integraKngdisparatedatasources
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What Is IN the CRDW? • Themostcommonly-requesteddataelementsrequiredtoanswerresearchquesKonsarethefollowing:
• PaKentDemographicInfo• EncounterInfo(Encountertype,AdmitDate,Discharge
Date,etc.)• Diagnoses:ICD9/10• Procedures:ICD9/10,CPT• FlowSheets:Vitals,Respiratory,PhysicalAssessment,etc.• MedicaKons:OutpaKentandMAR• Labs• ADT:(Admission,Discharge,Transfer)
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What About Notes? • Notesareavailableinbulkandwecanprovideaspartofadatarequest
• However,definingacohortordoingaddiKonalfiltering/logicusinginfofromwithinthenoteisdifficult,Kme-consuming,andexpensiveusingNaturalLanguageProcessing
• Notimpossible,justcost-prohibiKve
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Source Systems- Internal to UCMC
• UCMsourcedataliveswithinanumberofdisparatesourcesystemsaroundtheUniversityofChicagoMedicalCenter
• Atasufficientlyhighlevel,datasourcesbreakdownintothefollowingareas:
• Clinical• Billing• Other
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Source Systems- Internal to UCMC
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Source Systems- Internal to UCMC
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Source Systems- External to UCMC • External‘PubliclyAvailable’DataSources,whicharetypicallypopulaKon-
based,ratherthanspecifictoUCMC• Examplesincludethefollowing:
– Medicare/MedicaidData-MarketScan– SSA’sNaKonalDeathRegistry
• Inmostcases,wecanpointyouintherightdirecKon• ChallengesofmatchingdatafromoutsideoftheinsKtuKon• Example:NaKonalDeathRegistry:
– Onamonthlycadence,wepullSSA’sNaKonalDeathRegistry(NDR)fileintotheCRDWandmatchrecordsw/paKentsinEpic
– WhilethiscanonlybeaprobabilisKcmatch,usingacombinaKonofSSN,DOB,LastName,andFirstIniKal,weseefrom110k-150k+paKentsinEpicwhichlackaccuratemortalitydata.
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Output from CRDW: 2 Flavors • StaKcDataSet:1xdataextracKon
– Example:FindingacohortofpaKentsthatmatchonaspecificsetofdemographic,clinical,andbillingvariables.
• DynamicDataSet(AKAAffiliatedDataMart)– Example:BringingtogetheraregistryofpaKentsmaintainedinREDCapw/clinicaldatafromEpic/Clarity,billingdatafromCentricity,andcancerdatafromtheCancerRegistry
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Output from CRDW
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Data Warehousing Challenges: Source Matters • SomequesKonsposedtotheCRDWteam,despiteappearingstraighoorwardonthe
surface,provechallengingtooperaKonalizebasedonhowwebringtogetherdisparatedatasources.
• Example:ExaminingPaKentintubaKonusingclinicaldatavs.billingdata
– PaKentisintubatedandresearcherwantstoknowtheclinicaldate/KmeofintubaKonbybillingcode(i.e.CPT).
– Usingbillingdata,wehavemulKpleservicedatesandcanputtheintubaKonprocedurewithinarangeofservicedates,butwecannotnecessarilypinpointtheexactKmestampfortheintubaKonprocedurebasedonbillingcodealone.
– IfwejumpovertotheclinicaldatainEpic,wehaveexactKmestamps,butwecannotnecessarilyalignthoserecordsw/billingCPTcodes.
• Takeaway:ParametersusedtodefineapaKentcohortcanbeverydifferentfromthe
datathatactuallyshowsupinthedeliverable
• Onewaytothinkofitisin2steps:
– Definingthecohort– GeneraKngthedataset
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Data Warehousing Challenges: Crumpled-Up Cocktail Napkin
• ClientssomeKmescometousw/theirowndata
• Dependingonhowwellthedatahasbeencurated,itcanbedifficulttoalignw/CRDWdata
• IfyouarecollecKngregistrydatalocally,pleaseconsiderthefollowingsuggesKons:
1. Don’t
2. ConsiderpurngyourdataintoREDCap
3. Ifyoumust/insistonstoringdatalocally,pleaseconsiderthingslikethefollowing:
• Howdataisstored(i.e.Datesina‘date’fieldw/appropriatedatatypeinexcel)• LimiKngscopeofindividualsw/permissiontoeditdata• AddiKonalfieldswhichcouldprovidemoreaccuracywheneventually
triangulaKngdatawithinyourregistryw/datafromCRDW(i.e.billing/clinicalencounternumber).
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Data Warehousing Challenges: Discrete vs. Non-Discrete Data
• ResearcherputsdataintoEpicinadiscretefield
• Importanttoworkw/therightteamstomakesurethedatagoingintoEpicissetuptoeventuallymakeitswayovertoClaritywhereCRDWteamtakesover.Morecommonw/customizablefields(i.e.SmartPhrases,DocFlowsheets)
• Ifdiscretedataisenteredintodiscretefields,buteventuallymakesitswayovertoClaritywithinabloboftext,muchmoredifficultforCRDWteamtoextract/parse
• Example:ResearcherwantstodefinecohortbasedonextracKngpaKent’sHarveyBradshawIndexfromaprogressnote
– AlthoughHarveyBradshawIndexisadiscretevalue,itmaynotbestoredwithinadiscretefield
– Difficulttoextractw/oNLP
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Quality of Data Sources • Weeklyreportsfollowtrendinginprimarydatasources
• Datashouldbestableandshowaconsistentpa`ernofvariaKonoverKme
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Quality of Data Sources • Weeklyreportsfollowtrendinginprimarydatasources
• Datashouldbestableandshowaconsistentpa`ernofvariaKonoverKme
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Other Data Warehouses at UCMC
• Nomise:FacilityBillingdata(2012–present)– RunbyManagedCare
• UCPG:ProfessionalBillingdata(2012–present)
– RunbyUCPG
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Why So Many Data Warehouses?
• Differentpurposes• Ifyouhavearequestthatisspecifictoqualityorspecifictoresearch,thetargetedrepositoryisopKmizedforansweringspecifictypesofquesKons
• Samedata,differentpackaging
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HowdoIrequestdata?
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History of Data Requests at UCM
• Noteasytoobtaindatawhenneeded
– Typicallywasdrivenbywhoyouknew
• Inconsistencyinwaydatawasrequested
– SamerequestsfilledbymulKpleteams• Discrepancyinoutcomes
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Request
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analystanalyst
analyst
analyst
analyst
analyst
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analyst
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Analytics Core
• TheEnterpriseInformaKcsandAnalyKcsProgramleadershiphascharteredtheAnalyKcsCoretoimprovethepathtodatafortheorganizaKonbyfosteringcommunicaKon,collaboraKon,andbestpracKcesamongtheorganizaKon’sanalyKcsleaders
• AnalyKcsCoreiscomprisedofanalyKcsleadsfrombothtechnicalandbusinessteamsspanningUCMandBSD
• AnalyKcsCoremeetsweeklytoreviewalldatarequestssubmi`edviaACReS(AnalyKcsCoreRequestSystem)toreduceduplicaKonofresources,improvedataquality,andprovideaplaoormforcommunicaKonandtransparency
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Current UCM Analytics Ecosystem • AnalyKcsCore
– CBIS• Clarity• EpicreporKngworkbench• EpicProgramming
– CenterforQuality(CFQ)• AdvancedAnalyKcsandDataScience• ClinicalEffecKvenessanalyKcs• HealthCareDeliveryScienceandInnovaKon
– CenterforResearchInformaKcs(CRI)– Finance
• ManagedCareandFinancialPlanning• BudgetResourceAnalysis&FinancialPlanning
– PaKentCareServices– ProceduralServices– UCPGdecisionsupport– MarkeKngAnalyKcs– StrategicPlanning
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How Do I Request Data? ACReS
• AnalyKcsCoreRequestSystem
• h`ps://cri-app02.bsd.uchicago.edu/ACReS
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Step 1: Log into ACReS Website • h`ps://cri-app02.bsd.uchicago.edu/ACReS• AnalyKcsCoreconnectstheorganizaKontodatabeyondtheCenterforQuality
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Step 2: Begin a New Request
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Step 3: Complete the Form
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Step 4: Submit the Form
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Step 5: Post Request Submission
• YouwillreceiveaconfirmaKonemailsummary• Checkbacktoseestatusupdates(youwillreceiveupdatesviaemail,andthroughtheACReStool)
• YourrequestwillbeassignedtothemostqualifiedanalyKcsteambytheendofthenextbusinessday
• TheanalyKcsteamassignedtoyourrequestwillnowbeyourprimarycontactforthisrequest
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Regulatory Compliance for Data Requests QIvs.ResearchHumanSubjectResearch• HumansubjectsresearchisdefinedinCFR§46.102as
1. Research:asystemaKcinvesKgaKon…designedtodeveloporcontributetogeneralizableknowledge.
2. Humansubject:livingindividualaboutwhomaninvesKgatorconducKngresearchobtainsa. DatathroughintervenKonorinteracKonwiththeindividual,orb. IdenKfiableprivateinformaKon.
QualityImprovement• QualityimprovementissomeKmesdefinedinthefollowingways:
1. “Qualityimprovement(QI)consistsofsystemaKcandconKnuousacKonsthatleadtomeasurableimprovementinhealthcareservicesandthehealthstatusoftargetedpaKentgroups.”1
2. Qualityimprovementis“thecombinedandunceasingeffortsofeveryone-healthcareprofessionals,paKentsandtheirfamilies,researchers,payers,plannersandeducators-tomakethechangesthatwillleadtobe`erpaKentoutcomes(health),be`ersystemperformance(care)andbe`erprofessionaldevelopment”2
3. Thepursuitofthetripleaim:“ImprovingtheU.S.healthcaresystemrequiressimultaneouspursuitofthreeaims:improvingtheexperienceofcare,improvingthehealthofpopulaKons,andreducingpercapitacostsofhealthcare.”3
REFERENCES:(tooutsidesources,ifapplicable)1.U.SDepartmentofHealthandHumanServices,HealthResourcesandServicesAdministraKon.Qualityimprovement.2011.Accessed29October2015ath`p://www.hrsa.gov/quality/toolbox/
508pdfs/qualityimprovement.pdf.2.BataldenP,DavidoffF.Whatis‘‘qualityimprovement’’andhowcanittransformhealthcare?QualSafHealthCare2007;16:2–3
3.BerwickD,NolanTW,WhirngtonJ.TheTripleAim:care,health,andcost.HealthAffairs,27,no.3(2008):759-769.
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Regulatory Compliance for Data Requests
QIvs.Research– QualityImprovementDetermina3on
• NewpolicythatguidestheapprovalprocessfortherequestofanduseofinsKtuKonaldatainqualityimprovementprojectswhenthereisintenttosharethedataoutsidetheOHCA(OrganizedHealthCareArrangement)
• OHCAconsistsofUCM,BSD,andPritzker• Requestand/oruseofPHIissubjecttominimumnecessaryrequirementandmaybesubjecttoapprovalfromPaKentComplianceOffice(appliestoallnon-researchrequestsfordatarelatedtotreatment,payment,opera9ons,andQI)
– IRBReview• Governshumansubjectsresearch• IRBapprovedprotocolrequiredpriortobeginningdatarequest• Requestand/oruseofPHIfollowsminimumnecessaryrequirementandissubjecttoIRBapproval
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Regulatory Compliance for Data Requests
QIvs.Research– QualityImprovementDetermina3on
• ArenotassignedtotheCRDW– IRBApproval
• AreassignedtotheCRDWNeitherQIDetermina8onnorIRBapprovalisaguaranteeofanalyst8me.Thedetermina8onsimplydesignateswhichanaly8csteamisassignedtoreviewyourprojectforformal
acceptance.
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CRDWRequestTriage
Face-to-facemeeKngofrequestorandpointperson(30-60minutes)
Requestorsubmitsdatarequest(online)
Projectmanager,pointperson,andreportwritersmeetweeklyto
reviewandassignrequests(1hr)
SpecificaKonsdocumentandcostesKmate(1-3hours)
Requestorvalidatessampledataset(1-2hours)InternalverificaKonthatdataset
matchesspecificaKonsdocument.Recheckthatqueriesarewri`en
correctly(1-5hours)
IRBcheck-ProjectmanagerandOfficeofClinicalResearch(1hour)
ReportwriterdevelopsSQLcodefordataextracKon(1-20hours)
Internalreview-Pointpersonandreportwriter(1-2hours)
Reportwriterpullssampledataset(30-60minutes)
Reportwriterpullsfinaldataset(30-60minutes)
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CRDW Spec Document
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Data Request Challenges: Most Common Hold-ups
• Regulatory– DisKncKonbetweendatapointsusedforscreeningvs.thoseneededforthe
actualdataset,parKcularlyrelatedtoPHI
• Lackofresponse– Unwillingnesstomeetduringofficehours– TimelinessrespondingtodataclarificaKonemails
• Cohortiden9fica9on– DataisnotavailableinaformatthatisconducivetodataextracKon
• Requestexpecta9ons– Askingustomakingthebinarydecisions– OnepaKentperrow
• Funding– Nobudgettofacilitatetheresearch
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CRI Resources
• OfficeHours– Tuesdays,RoomN161,byappointment10-4– Fridays,RoomN161,byappointment10-12
• IRBguidance
– CRDWspecificIRBlanguage• ITMgrantapplica3onguidance
– Website• Semi-selfservicetools
– I2b2– SEECohorts
• DataStorageSolu3ons– REDCap– Bulkstorage
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CRI Resources
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Semi Self-Service Tools
• i2b2
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Semi Self-Service Tools
• SEE-Cohorts:https://seecohorts.cri.uchicago.edu/
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