towards na*onal scale clinical data warehousing: experiences … › conferences2019 ›...
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TowardsNa*onalScaleClinicalDataWarehousing:ExperiencesandPerspec*vesonDataReusefortheLearningHealthcareSystem
LuisMarcoRuiz,PhD1,2
1NorwegianCentreforIntegratedCareandTelemedicine
2MedizinischenHochschuleHannover
©LuisMarco-Ruiz2018
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Background
• ComputerScienJst,MScAppliedStaJsJcs,PhDHealthScience(MedicalInformaJcs)
• Since2013workingfortheNorwegianCentreforE-healthResearch(NSE)
• PostdoctoralresearchfellowandadvisorforclinicalinformaJonstandardizaJon
• SemanJcInteroperabilitySpecialistattheHannoverMedicalSchool(HiGHmedproject)
©LuisMarco-Ruiz2018
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Agenda• TheLearningHealthcareSystem• ConceptualizaJonofclinicaldatawarehousing(DW)• ComputaJonalmodelsinvolved• InformaJonrepresentaJonforclinicalDW• KnowledgerepresentaJonforclinicalDW• ExperiencesinNorwayandGermany• OrganizaJonandchallenges• Acknowledgements
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Agenda• TheLearningHealthcareSystem• ConceptualizaJonofclinicaldatawarehousing(DW)• ComputaJonalmodelsinvolved• InformaJonrepresentaJonforclinicalDW• KnowledgerepresentaJonforclinicalDW• ExperiencesinNorwayandGermany• OrganizaJonandchallenges• Acknowledgements
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TheLearningHealthcareSystem
5
Evidence Based Medicine paradigm is facing many challenges (Djulbegovic 2009, Greenhalgh 2014). Among others, it is needed to decrease the 17 years that currently elapse since knowledge is generated until it is exploited at the bedside (Balas 2000, Friedman 2010). The IOM proposed in 2007 the Learning healthcare system as a paradigm to overcome these challenges. The LHS heavily relies on the use of technologies for (IOM 2007, ESF 2016): • Implementing that knowledge to exploit latest evidence at several levels (clinicians, patients,
citizens, populations).
• Providing communication channels and tools across the participants in care processes. • Facilitating secondary use of data to generate new knowledge.
->data reuse networks and Clinical Data Warehousing (DW)
Budrionis A, Bellika JG. The Learning Healthcare System: Where are we now? A systematic review. J Biomed Inform 2016;64:87–92. doi:10.1016/j.jbi.2016.09.018. ©LuisMarco-Ruiz2018
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Agenda• TheLearningHealthcareSystem• ConceptualizaJonofclinicaldatawarehousing(DW)• ComputaJonalmodelsinvolved• InformaJonrepresentaJonforclinicalDW• KnowledgerepresentaJonforclinicalDW• ExperiencesinNorwayandGermany• OrganizaJonandchallenges• Acknowledgements
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ClinicalDataWarehousing• IntherecentyearsthereuseofclinicaldatahasreceivedlotofaYenJonforimplemenJngtheLHS.
• ThishasledtothebirthofmanydatareusenetworksandtheClinicalDataWarehousing(DW)discipline.
• ClinicalDWhassignificantdifferenceswithrespecttotradiJonalenterpriseDW.• WhileinenterpriseDWthefocusisondealingwithlargeamountsofdata,clinicalDWfocusesonpreservingthemeaningofdata.
DATA
CONTEXT
CLINICALDATA
CONTEXT
EnterpriseDW ClinicalDW
UsefulInformaJon
UsefulInformaJon
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GeneraJngknowledgefromdata
8(AdaptedfromShethetal.2013)
DataAnalyJcs
SemanJcs(concepts)
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Agenda• TheLearningHealthcareSystem• ConceptualizaJonofclinicaldatawarehousing(DW)• ComputaJonalmodelsinvolved• InformaJonrepresentaJonforclinicalDW• KnowledgerepresentaJonforclinicalDW• ExperiencesinNorwayandGermany• OrganizaJonandchallenges• Acknowledgements
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ComputaJonalmodelsinvolved
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RepresenJnginformaJonandknowledge
• ThemeaningofinformaJonwhenaccessingitacrossdifferentinsJtuJonsmustbepreserved.
• Datausers(e.g.researchers,healthindicators,etc.)needtobeabletounderstandthemeaningoftheinformaJonaccessed.
• DatausersmaybelongtodifferentorganizaJons.
• AlackofprecisionincommunicaJngthemeaningofadatasetmayresultininaccurateorerroneousconclusionsinqualitystudiesirrespecJvelyofthealgorithmused.
AVOIDAMBIGUITY!MAXIMIZEDATAQUALITY!
Dataaccessisnecessarybutnotenough…
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• InordertopreservethemeaningofinformaJonacrossdisparateorganizaJons,representaJonmechanisms(languages)forboththeinformaJonandthemeaningareneeded.
• ClinicalInformaJonstandardsareusedtorepresentinformaJonstructures.
• DescripJonLogicsareusedtodefineontologiesthatareformal(mathemaJcal)representaJonsofthesemanJcsconveyedbyclinicalinformaJonmodels.
RepresenJnginformaJonandknowledge
Informa*onrepresenta*on Knowledgerepresenta*on
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Expressivityneedsofphenotypingalgorithms
(*)ConwayM,BergRL,CarrellD,DennyJC,KhoAN,KulloIJ,etal.AnalyzingtheHeterogeneityandComplexityofElectronicHealthRecordOrientedPhenotypingAlgorithms.AMIAAnnuSympProc2011;2011:274–83.
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Agenda• TheLearningHealthcareSystem• ConceptualizaJonofclinicaldatawarehousing(DW)• ComputaJonalmodelsinvolved• InformaJonrepresentaJonforclinicalDW• KnowledgerepresentaJonforclinicalDW• ExperiencesinNorwayandGermany• OrganizaJonandchallenges• Acknowledgements
©LuisMarco-Ruiz2018
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InformaJonrepresentaJonforclinicalDW
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BuildingclinicalinformaJonmodelsforDW
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BuildingclinicalinformaJonmodelsforDW• OpenEHRhasbeenselectedasstandardforrepresenJngclinicalinformaJoninEHRsby3outof4healthregions.
• In2013theNaJonalICTHealthTrustcreatedtheNa*onalEditorialGroupforArchetypes(NRUA)forcoordinaJngthedevelopmentofthenaJonalrepositoryofclinicalmodels(archetypes).
• InNovember2015severalprojectsstartedexploringthemappingofSNOMED-CTtowardsthemostcommonlyusedElectronicMedicalRecord(EMR)funcJonaliJes.
• AmongthesuggesJonsofthedirectorateistheelicitaJonofclinicalinformaJonmodels(archetypes)andterminologyassetsforbuildingthepaJentsummaryforconJnuityofcare(SNOMED-CTvaluesets).
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BuildingclinicalinformaJonmodelsforDW
CKMClinicalModelSelecJon Extension/AdapJon
• Archetypereusemustbeattemptedcheckingnationalandinternationalrepositories.
• Often,CKMarchetypesneedtobeextendedtoaccommodatedatareuserequirements.
• Thesetofarchetypeschosenmustguaranteethehighestlevelofreusability.
• Archetypesshouldnotbeinfluencedbyaparticularreusescenario.
• KeepanyneworextendedArchetypesunconstrainedasmuchaspossible.
Setofarchetypes
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Marco-RuizL,MonerD,MaldonadoJA,KolstrupN,BellikaJG.Archetype-baseddatawarehouseenvironmenttoenablethereuseofelectronichealthrecorddata.IntJMedInf2015;84:702–14.doi:10.1016/j.ijmedinf.2015.05.016. ©LuisMarco-Ruiz2018
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Agenda• TheLearningHealthcareSystem• ConceptualizaJonofclinicaldatawarehousing(DW)• ComputaJonalmodelsinvolved• InformaJonrepresentaJonforclinicalDW• RepresentaJonsofsemanJcsinclinicalDW• ExperiencesinNorwayandGermany• OrganizaJonandchallenges• Acknowledgements
©LuisMarco-Ruiz2018
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RepresentaJonofsemanJcsinclinicalDW
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BuildingclinicalinformaJonmodelsforDW
22©LuisMarco-Ruiz2018
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Heartdisease(disorder)SCTID:56265001
Familyhistorywithexplicitcontext(situaJon)SCTID:57177007
SCTconcept
…
SituaJonwithexplicitcontext243796009
Disease(disorder)64572001
AcuteHeartFailure AcuteMiocardiJs AtrialFibrillaJon ….
:(57177007|Familyhistorywithexplicitcontext(situaJon)|:246090004|Associatedfinding|=56265001|Heartdisease|)
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Marco-RuizL,PedrinaciC,MaldonadoJA,PanzieraL,ChenR,BellikaJG.PublicaJon,discoveryandinteroperabilityofClinicalDecisionSupportSystems:ALinkedDataapproach.JBiomedInform2016;62:243–64.doi:10.1016/j.jbi.2016.07.011.
LinkedKnowledgeBase
LODCloud
sawsdl:modelReference
sawsdl:modelReference
SOAP RESTful
SOAP
sawsdl:modelReference sawsdl:modelReference
sawsdl:modelReference
TimeGene
Ontology
...SNOMED-CT ICDATC
BindingClinicalInformaJonModelstodomainontologies
©LuisMarco-Ruiz2018
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0
50
100
150
200
250
300
1 2 3 4
Processing*me
Datasize
OWL2EL++
OWL2DL
OverdoseofformalsemanJcs
• ComputaJonalrestricJons• HumanrestricJons
Marco-RuizL,NicolaisenK,PedersenR,MakhlyshevaA,BakkevollPA.Ontology-basedterminologiesforhealthcare-ImpactassessmentandtransiJonalconsequencesforimplementaJon.vol.1.1sted.Tromsø:NorwegianCentreforE-healthResearch;2017.
©LuisMarco-Ruiz2018
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Agenda• TheLearningHealthcareSystem• ConceptualizaJonofclinicaldatawarehousing(DW)• ComputaJonalmodelsinvolved• InformaJonrepresentaJonforclinicalDW• KnowledgerepresentaJonforclinicalDW• ExperiencesinNorwayandGermany• OrganizaJonandchallenges• Acknowledgements
©LuisMarco-Ruiz2018
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NaJonwideclinicaldatawarehousinginiJaJves
LHS-toolbox(Norway)&HiGHmed(Germany)
©LuisMarco-Ruiz2018
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ClinicalDWinNorway:theLHS-Toolbox
TheLHS-toolboxprojectisfundedbytheNorwegianResearchCouncilwith12,000,000NOK.TheprojectisbasedontheevoluJonoftheSNOWsystemfordistributed
computaJons.
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LHSandSNOW
sdsdsds
©JohanGustavBellika2017
DeployedSnowsystemnodes•AcommonsystemforextracJonofdatafromlabsandhealthinsJtuJons•AcommonsystemforpresentaJonofhealthdata•AcommonsystemforcomputaJonofdistributedhealthdata
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LHS-toolbox(SnowandEmnet)
GP1
Lab1
GPn
SNOW
Labn …
…
EMNET
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ExtracJonandcaching
Integratedview
Transformation into openEHR extracts
GP1
Lab1
SNOW
TRANSFORMSTAGE
openEHRGP1
ExtracJonandcaching
Integratedview
SNOW TRANSFORM
STAGE
openEHRGP2
Archetypes
Archetypes
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VirtualopenEHR-basedview
32
AQ
L openEHRpersistenceplatform
Extractserver
OpenEHRcompliantextract
OpenEHRcompliantextract
OpenEHRcompliantextract
OtheropenEHRsources
CDS
Clinicalresearch
Publichealth
Surveillance
Adapter(groupsdifferentarchetypesintotheemplatesandsubmitsthemtotheDW
usingit’sAPI)
Transform
OpenEHRcompliantextract
OpenEHRcompliantextract
VirtualopenEHRrepository
OpenEHRcompliantextract
©LuisMarco-Ruiz2018
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HiGHmedSummary
©HiGHmed2018
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ClinicalDWinGermany(HiGHmed)
TheHiGHmedprojectisfundedbytheFederalMinistryofEducaJonandResearch
(BMBF)with30,000,000.Euro.ThisfundingschemewasdesignedtofundiniJaJvesthatwillfosterthedigitalizaJoninthefieldofmedicine
34HaarbrandtB,SchreiweisB,ReyS,SaxU,ScheithauerS,RienhoffO,etal.HiGHmed–AnOpenPlasormApproachtoEnhanceCareandResearchacrossInsJtuJonalBoundaries.MethodsInfMed2018;57:e66–81.doi:10.3414/ME18-02-0002. ©HiGHmed2018
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PrivateandNetworkingPartners
Ada Health
AcademicPartners
AssociatePartnersHiGHmed-Partners
©HiGHmed2018
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HiGHmed
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Requirements• HealthInforma*onExchange:TogainaholisJcviewonpaJents’diseaseprogresses,informaJonhastofollowpaJentsacrossorganizaJonalboundaries.
• Cross-EnterpriseDataAnalysis:TosupportallkindsofanalyJcsbasedonstructuredandunstructureddatafromclinicalcare,researchandtrialdatabases,andqualityregistriesinadistributedenvironment,wewanttosupportcomplexqueriesonagranularpa*ent-recorddatalevel.
• Cross-EnterpriseKnowledgeManagement:TherearesJllbarrierstopracJce-basedevidenceandevidence-basedpracJceinhealthcare.Therefore,weneedtoiteraJvelyworkontheestablishmentofalearninghealthcaresystem,inwhichthelatestresultsofresearchanddataanalysisarereadilyavailabletocaregiversatthebedside,and,inturn,healthoutcomesofpaJentshelpresearcherstogainnewknowledge.
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BasicPrinciples• Pa*entsfirst:InaddiJontofine-graineduseandaccesscontrol,paJentswillbeabletoviewandobtaintheirhealthdatathroughuserfriendlytools.
• DataSafetyandPrivacy:DatasafetyandprivacyandthepaJents’rightofself-determinaJonarehighestprioriJes.Dataaccessisregulatedunambiguouslyinatransparentandtraceableprocess.
• ClinicallyledDataModelling:HealthcareprofessionalsandresearchersalikeneedtobeacJvelyengagedtoestablishsemanJcinteroperability.Thiscreatesanewclinico-technicalrole,theso-calledDataSteward,responsibleforthemanagementandfitnessofdataelementswithintheclinicians.
• Scalability:TechnicalsoluJonsmustbeopJmizedtohandlehighvolumesofcomplex,constantlychanginginformaJonandclinicalworkflows.
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TechnicalConcept• EveryHiGHmedSitehasestablishedaDataIntegra*onCenteraccordingtocommonHiGHmedstandards
• Establishsharedservicesanddatamodels
• UseIHEXDSCross-EnterpriseDocumentSharingtoestablishvendor-neutralarchivesandtosupportcon*nuityofcareprocesses
• WorkingonjointsemanJcsthroughopenEHR;withcommonArchetypesandAQLquerylanguageinterface
• ImplementFHIRinterfacestoenhancedataexchangecapabiliJes(vendorneutral)
©HiGHmed2018
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HiGHmedTechnicalInfrastructure
AuthenticationService
CrossCommunityAccess(XCA,XCPD)
FHIRTerminologyService PseudonymizationServiceKnowledgeService
XCAGateway
ManagementServiceClinicalKnowledgeManager
MedicalDataIntegrationCenterMHH
XDSAffinityDomain
openEHR/IHEXDSDataRepositories
SourceLa
yer
LocalInfrastructure&DataSources
HANNOVERMEDICALSCHOOL
Integrat
ionLa
yer
HIS
Registries
MPIDocumentRegistry
Audit
Norm
alizat
ionLa
yer
Federat
ionLa
yer
PEHR
MHHCDWH ETLTooling(MSSSIS)
SiteSpecificDataAnalyticsInterfaces
Registry Trial
EHR
Cross-ConsortiaService OMICSServices
NLPEngine
...AP
Is XDR FHIR AQL
Apps SMICS
VirtualOncoCenter
CardioAnalytics
CohortExplorer
Data
Marts
DataConverter(CDISC,FHIR,v2)
RIS
...
OMICsDataIntegrationCenterDKFZ/UKHD
XDR
openEHRenabledOMICsDB
SourceLa
yer
LocalInfrastructure&DataSources
DKFZ/UKHD
Integrat
ionLa
yer
OTP
D:cipher ...
Norm
alizat
ionLa
yer
Federat
ionLa
yer
DKFZDataWarehouse
API
Apps Metadata
Catalogue
SiteSpecificDataAnalyticsInterfaces
SAP
XDR FHIR AQL
SAP
Audit
VirtualOncoCenter
XCAGatewayXCAGateway
MedicalDataIntegrationCenterUMG
XDSAffinityDomain
openEHR/IHEXDSDataRepositories
SourceLa
yer
LocalInfrastructure&DataSources
UMGöttingen
Integrat
ionLa
yer
HIS
Registries
MPIDocumentRegistry
Audit
Norm
alizat
ionLa
yer
Federat
ionLa
yer
PEHR
ETLTooling(MSSSIS)
SiteSpecificDataAnalyticsInterfaces
Registry Trial
EHR
NLPEngine
APIs XDR FHIR AQL
Apps SMICS
VirtualOncoCenter
CardioAnalytics
CohortExplorer
Data
Marts
DataConverter(CDISC,FHIR,v2)
RIS
...
XCAGateway
MedicalDataIntegrationCenterUKHD
XDSAffinityDomain
openEHR/IHEXDSDataRepositories
SourceLa
yer
LocalInfrastructure&DataSources
UKHeidelberg
Integrat
ionLa
yer
HIS
Registries
MPIDocumentRegistry
Audit
Norm
alizat
ionLa
yer
Federat
ionLa
yer
PEHR
ETLTooling(MSSSIS)
SiteSpecificDataAnalyticsInterfaces
Registry Trial
EHR
NLPEngine
APIs XDR FHIR AQL
Apps SMICS
VirtualOncoCenter
CardioAnalytics
CohortExplorer
Data
Marts
DataConverter(CDISC,FHIR,v2)
RIS
...
XCAGateway
MedicalDataIntegrationCenterNewSite
XDSAffinityDomain
openEHR/IHEXDSDataRepositories
SourceLa
yer
LocalInfrastructure&DataSources
AdditionalSite
Integrat
ionLa
yer
HIS
Registries
MPIDocumentRegistry
Audit
Norm
alizat
ionLa
yer
Federat
ionLa
yer
PEHR
... ETLTooling(...)
SiteSpecificDataAnalyticsInterfaces
Registry Trial
EHR
NLPEngine
APIs XDR FHIR AQL
Apps SMICS
VirtualOncoCenter
CardioAnalytics
CohortExplorer
Data
Marts
DataConverter(CDISC,FHIR,v2)
RIS
...
DataLake DataLake
©HiGHmed2018
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HiGHmedUseCases• UseCase1Oncology:TheoncologyusecasewillhelpustoaddresschallengesinMedicalInformaJcswhenintegraJngomicsdatafromgenomesequencingandradiologyintoclinicalpracJce.New,mobilediagnosJcdevicesareexpectedtochangecurrentmedicalpracJceandresearchbycontribuJngtothelong-termmonitoringofpersonalhealthdataatanunprecedentedlevel.
• UseCase2Cardiology:Withinthecardiologyusecase,wewillsystemaJcallyexploreandaddressITchallengesrelatedtotheintegra*onofdatafromwearableandconnecteddevicesintoourITarchitecture.
• UseCase3Infec*onControl:TheinfecJoncontrolusecasewilldevelopanautomatedearlywarningandclusteranalysissystemtosupportthealgorithmicdetec*onofpathogenclusters.ItwillincludemulJdrug-resistantorganismswithinandacrossuniversityhospitals,theverificaJonofwhetherclustersrepresentoutbreaks,andtheidenJficaJonofpossiblecausesofoutbreaks.
10/03/19 41©HiGHmed2018
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Agenda• TheLearningHealthcareSystem• ConceptualizaJonofclinicaldatawarehousing(DW)• ComputaJonalmodelsinvolved• InformaJonrepresentaJonforclinicalDW• KnowledgerepresentaJonforclinicalDW• ExperiencesinNorwayandGermany• OrganizaJonandchallenges• Acknowledgements
©HiGHmed2018
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LHS-toolboxandHiGHmedsharerequirementsandchallenges
©LuisMarco-Ruiz2018
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TechnicalrequirementsofclinicalDW
• IntegraJonofheterogenousdatasourcesinstructureandmeaning• Commonrepresenta*onformatsareneeded(openEHR,HL7FHIR,IHEXSD)• Seman*cInteroperabilityisrequiredfortheconsistencyandmeaning• Expressivequeries(e.g.Somekindof…)
©LuisMarco-Ruiz2018
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HighlevelorganizaJonofteamsSharedforalltheproject
• 1Enterprisearchitect
• 1Projectmanager
• 2-4developers
• 1semanJcinteroperabilityspecialist
• Others:Datastewards,PhDs,administraJonstaffetc.
Internaltoeachins*tu*on(dataintegra*oncenter)
• Thestaffdependsdependingontheneedsandtheroleinthedatareusenetwork
©LuisMarco-Ruiz2018
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OrganizaJonalchallengesinclinicalDW
• Needformoreflexibilitymanagingprojectfunds.
• ExcessofregulaJoninpublicfundedprojects->burocracyconsumeshugeamountofresources,slowprocurementprocess,slowhiringprocessesetc.
• Thecomplexityofprocurementprocessesmayaffectsmall-mediumsizecompanies.
• CollaboraJonamongdifferenthealthregionsmaybecomplex.
• RecruithighlyskilledITprofessionalsforpublicprojectsisdifficult.WearenotcompeJJvewhencomparedwithprivatecompanies.Thismayaffectqualityofourdevelopments->wetrytoalleviatethisbypartneringwithvendorsthatareinterestedinourusecases/developments.
©LuisMarco-Ruiz2018
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OrganizaJonalchallengesinclinicalDW
• GDPR->currentlytheregulaJonisambiguousandnoclearanswersareprovided ->thisaffectsthewholedatareuseinfrastructure.
• WeneedmoreefficientmechanismstoenablepaJentcontrol.
• Theborderbetweenaresearchstudyandastudymeasuringhealthindicatorsisnotclear
->uncertaintyonhowtoproceed
©LuisMarco-Ruiz2018
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LHS-toolboxteam
HiGHmedteam
Acknowledgements
©LuisMarco-Ruiz2018