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)

Hot Topic: Digital Health

Goal: Learning Health System

Dissemination

Data generation

Knowledgegeneration

Research

Diagnostics

Therapy

Outcomes

Clinical CareTranslation

Translation

DataEvidence

Secundary use

Data quality

Governance & Data protection

Digital participation

Machine learning

Secondary Use of Routine Clinical Data

ResearchLifecycle

Hypothesisgeneration

Feasibilityanalysis

Recruitment

Datacapture

Dataanalysis

Longtermarchiving

Clinical IT

Collaborating Hospitals

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

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

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?

Cross-Consortial Governance & Collaboration Structures of the MII

Task forces

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

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

Collaborative Solutions for Data Protection & Governance

Clinical IT

Data usecontract

Anonymous orpseudonymous

dataset

Pseudonymization

Identifyingdata

Medicaldata

Data protectionofficer

Patient consent

Data usepolicy

MIRACUM Use Cases

Patient Recruitmentfor Clinical Trials

Predictive Toolfor Asthma/COPD

and Neurooncology

MolecularTumor Board

MIRACUM Use Case 1:Patient recruitment for clinical trials

Recruitment

Exclusion

Screening ofCandidates

Research

Clinical care

MIRACUM Use Case 2:Predictive tools for asthma/COPD & neurooncology

Application ofML-models

Endotyping

Use case

PredictionInfrastructure

MIRACUM Use Case 3:Molecular Tumorboard

Treatment

Bio-informatics

Visualization

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

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

Demonstrator Study: Iterative approach, harvesting low-hanging fruit

20Locations

19Approvals

1,8Mill. patients

3,2Mill. cases

(09/2018 - 03/2019)

MII Demonstrator Study - Results:Charlson Comorbidity Index vs. Discharge Reason

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

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)