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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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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