leveraging clinical data for research: the miracum ... · restrict to easily available data re-use...
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Leveraging Clinical Data for Research: The MIRACUM Consortium in the German Medical Informatics Initiative
04.12.2019 Heidelberg University Office Kyoto Joint Lecture (Kyoto, Japan)
Prof. Dr. Thomas GanslandtHeinrich-Lanz-Center for Digital Health (HLZ)
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Hot Topic: Digital Health
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Goal: Learning Health System
Dissemination
Data generation
Knowledgegeneration
Research
Diagnostics
Therapy
Outcomes
Clinical CareTranslation
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Translation
DataEvidence
Secundary use
Data quality
Governance & Data protection
Digital participation
Machine learning
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Secondary Use of Routine Clinical Data
ResearchLifecycle
Hypothesisgeneration
Feasibilityanalysis
Recruitment
Datacapture
Dataanalysis
Longtermarchiving
Clinical IT
Collaborating Hospitals
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The German Medical Informatics Initiative (MII)
Foster re-use ofroutine clinical data
Demonstrate utilitythrough clinical use cases
Strengthen MedicalInformatics as a discipline
160 M€ fundingby BMBF
Long-term perspective
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MII Consortia & Coverage of the MIRACUM Consortium
Image source: http://www.medizininformatik-initiative.de/en/node/5
Greifswald
Dresden
Medical Informaticsin Research And Care in University Medicine
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Competition vs. Collaboration in the MII
Competitive grant application
◼ 4 distinct consortia
◼ individual IT architectures anddata models
◼ individual clinical use cases
Funder expects an overall solution
◼ data sharing needs to work acrosssite & consortium boundaries
◼ rollout to nonacademic sites
Challenges to address together
◼ how can we implement harmonizeddata structures & encodings?
◼ how can we achieve broad consentby patients?
◼ how can we align governancepolicies and data use contracts?
◼ how can we securely implementshared health data analyses?
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Cross-Consortial Governance & Collaboration Structures of the MII
Task forces
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MII Modular Core Dataset
Oncology Pathology findings
Imaging findings
PDMS/Biosignals
Biomaterial
Genetic tests Structure data
Billing codes
Cost data
Exte
nsi
on
mo
du
les
Diagnoses
Procedures
Lab findings
Medication
PersonDemographics Case data
Bas
ic m
od
ule
s
…… …
…… …
Data structuresbased onHL7 FHIR Standard
Semanticannotationbased on
internationalterminologies
Collaborativetools for
requirementsspecification
and datamodelling
Open governanceprocesses
and balloting
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How to implement the MII Core Dataset (shown for lab findings)
Diagnoses Demographics
Case dataProcedures
E.g. project to determinecomparability of lab findings
In MII
HL7 FHIR
Data structure
LISDorneri/med
PDMSPhilips
ICCA
Laboratory results
Terminology
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Collaborative Solutions for Data Protection & Governance
Clinical IT
Data usecontract
Anonymous orpseudonymous
dataset
Pseudonymization
Identifyingdata
Medicaldata
Data protectionofficer
Patient consent
Data usepolicy
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MIRACUM Use Cases
Patient Recruitmentfor Clinical Trials
Predictive Toolfor Asthma/COPD
and Neurooncology
MolecularTumor Board
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MIRACUM Use Case 1:Patient recruitment for clinical trials
Recruitment
Exclusion
Screening ofCandidates
Research
Clinical care
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MIRACUM Use Case 2:Predictive tools for asthma/COPD & neurooncology
Application ofML-models
Endotyping
Use case
PredictionInfrastructure
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MIRACUM Use Case 3:Molecular Tumorboard
Treatment
Bio-informatics
Visualization
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MIRACUM Open Source Data Integration Center Architecture
Consent-Manage-
ment
Local ID-Manage-
ment
ID-/Consent-Management
Sourcesystem 1
Comm-Server
Sourcesystem 2
Clinicaldata
repository
Routinebusiness
intelligence
Clinical Module
ClinicalDecisionSupport
Enrichment
Researchqueries
Research data
longtermarchive
Research Module
Researchdata
repository
Harmoni-zation
Federation
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MII Demonstrator Study: Harvesting Low-hanging Fruit
Long-term development vs. Needto show short-term results
◼ 4 years planned to implement DICs
◼ funder and general public should getcontinuous updates
Achieve "quick win" with Demonstrator
◼ choose reproductive questions
◼ restrict to easily available data
◼ re-use established software
Implementation Strategy
◼ goal: analysis of comorbidities and rare disease geovisualization
◼ data: diagnoses, demographics, case-related data
◼ datasource: "§21" billing dataset
◼ platform: i2b2, SQL queries
◼ privacy: only aggregated localanalyses, raw data stays at sites
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Demonstrator Study: Iterative approach, harvesting low-hanging fruit
20Locations
19Approvals
1,8Mill. patients
3,2Mill. cases
(09/2018 - 03/2019)
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MII Demonstrator Study - Results:Charlson Comorbidity Index vs. Discharge Reason
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MII Demonstrator Study - Results:Charlson Comorbidity Categories vs. Principal Diagnosis
Fraction of caseswith the comorbidity16. Certain conditions originating
in the perinatal period
15. Pregnancy, childbirth and the puerperium
09. Diseases of the circulatory system
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Conclusions & Outlook
Collaborative approaches towardssecondary use of clinical data work
◼ MIRACUM is sucessfully implementingbased on int'l terminologies, opensource and iterative approach
◼ successful cross-consortial worktowards interoperable structures
◼ alignment with international initiatives(e.g. OMOP/OHDSI, EHDEN, SPHN)
Decisive phase for the MII
◼ in the second half of the fundingperiod, DIC infrastructure needsto be put to visible use
Intensified cross-consortial efforts
◼ new shared Use cases⚫ CORD: on Rare Disases
⚫ POLAR: on Polypharmacy
◼ bid for grant in National researchdata infrastructure (NFDI)