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Enabling Large-Scale Analysis of Electronic Health Records in Europe through standardization: SNOMED in Action Peter Rijnbeek Associate Professor Health Data Science Department of Medical Informatics Erasmus MC Rotterdam The Netherlands

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Page 1: Enabling Large-Scale Analysis of Electronic Health Records ......Enabling Large-Scale Analysis of Electronic Health Records in Europe through standardization: SNOMED in Action

Enabling Large-Scale Analysis of Electronic Health Records in Europe through standardization: SNOMED in Action

Peter Rijnbeek

Associate Professor Health Data ScienceDepartment of Medical Informatics

Erasmus MC RotterdamThe Netherlands

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2

Disclosure belangen sprekerSymposium Verbinden & Innoveren met SNOMED

13 februari 2020

Geen (potentiële) belangenverstrengeling None

Voor bijeenkomst mogelijk relevante relaties None

• Sponsoring of onderzoeksgeld

• Honorarium of andere (financiële) vergoeding

• Aandeelhouder

• Andere relatie, namelijk: ..

• Innovative Medicines Initiative (IMI)• Janssen Research & Development Grant

• None

• None

• None

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THE JOURNEY TO LARGE-SCALE ANALYTICS

• Introduction to the use of a Common Data Model and Standardized Vocabularies

• The Observational Health Data Sciences and Informatics (OHDSI) initiative

• The European Health Data and Evidence (EHDEN) Project

• SNOMED in Action: examples of large scale studies

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HEALTH DATA ORIGINATES FROM PATIENT JOURNEYS

Conditions

Drugs

Procedures

Measurements

Person time

Dis

eas

e

Trea

tmen

t

Ou

tco

me

0Baseline time Follow-up time

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EACH OBSERVATIONAL DATABASE IS JUST AN (INCOMPLETE) COMPILATION OF

PATIENT JOURNEYS

Person 1

Conditions

Drugs

Procedures

Measurements

Person time

Dis

ease

Trea

tmen

t

Ou

tco

me

0Baseline time Follow-up time

Person 2

Conditions

Drugs

Procedures

Measurements

Person time

Dis

ease

Trea

tmen

t

Ou

tco

me

0Baseline time Follow-up time

Person 3

Conditions

Drugs

Procedures

Measurements

Person time

Dis

ease

Trea

tmen

t

Ou

tco

me

0Baseline time Follow-up time

Person N

Conditions

Drugs

Procedures

Measurements

Person timeD

isea

se

Trea

tmen

t

Ou

tco

me

0Baseline time Follow-up time

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QUESTIONS ASKED ACROSS THE PATIENT JOURNEY

Conditions

Drugs

Procedures

Measurements

Person time

Dis

ease

Trea

tmen

t

Ou

tco

me

0Baseline time Follow-up time

Which treatment did patients choose after diagnosis?

Which patients chose which treatments?

How many patients experienced the outcome after treatment?

What is the probability I will experience the outcome?

Does treatment cause outcome?

Does one treatment cause the outcome more than an alternative?

What is the probability I will develop the disease?

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GENERATING RELIABLE EVIDENCE

How can we do this at a large scale, i.e. on many data sources in Europe for many research questions?

How can we make sure these results are reliable?

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MINIMUM REQUIREMENTS TO ACHIEVE REPRODUCIBILITY

Patient-level data in source

system/schema

Reliable evidence

B

D

F

H

J

KM

OP

Q

R

S TU

V

W

I

C

E

L

N

XY

G

AZ

• Complete documented specification that fully describes all data manipulations and statistical procedures

• Original source data, no staged intermediaries• Full analysis code that executes end-to-end (from source to

results) without manual intervention

Desired attribute

Question Researcher Data Analysis Result

Reproducible Identical Different Identical Identical = Identical

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THE CHALLENGES OF REAL-WORLD DATA

Analytical method

Link to data

Data interoperability Standardised analytics Data network Strong community

What will it require?

The data…

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GENERATING RELIABLE EVIDENCE USING THE OMOP CDM

Patient-level data in source

system/schema

Reliable evidence

B

D

F

H

J

KM

OP

Q

R

S TU

V

W

I

C

E

L

N

XY

G

AZ

B

D

F

H

J

K

M

I

C

E

L

G

APatient-

level data in CDM

A Common Data Model will enable standardised analytics to generate reliable

evidence.

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HOW A COMMON DATA MODEL + COMMON ANALYTICS CAN SUPPORT REPRODUCIBILITY

Patient-level data in source

system/schema

Reliable evidence

B

D

F

H

J

K

M

I

C

E

L

G

A

• Use of common data model splits the journey into two segments: 1) data standardization, 2) analysis execution

• ETL specification and source code can be developed and evaluated separately from analysis design

• CDM creates opportunity for re-use of data step and analysis step

Desired attribute

Question Researcher Data Analysis Result

Reproducible Identical Different Identical Identical = Identical

Patient-level data

in CDM

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OBSERVATIONAL HEALTH DATA SCIENCES AND INFORMATICS

Mission: To improve health by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care

A multi-stakeholder, interdisciplinary, international collaborative with a coordinating center at Columbia University

http://ohdsi.org

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OHDSI’S GLOBAL RESEARCH COMMUNITY

• >200 collaborators from 25 different countries

• Experts in informatics, statistics, epidemiology, clinical sciences

• Active participation from academia, government, industry, providers

• Currently records on about 500 million unique patients in >100 databases

http://ohdsi.org/who-we-are/collaborators/

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SECOND ANNUAL OHDSI SYMPOSIUM, MARCH 29TH 2019

- Provides a platform to stimulate community building: 250 participants from 27 countries- Demonstrates the OHDSI approach to Reliable and Reproducible

Evidence Generation: 35 posters, 8 software demos- Educates and trains the community: 5 full day tutorials

www.ohdsi-europe.org

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ASIAN PACIFIC OHDSI COMMUNITY

South Korea, China, Taiwan, Japan, Australia is mapping data to the OMOP-CDM at scale

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DEEP INFORMATION MODEL: OMOP CDM VERSION 6

Concept

Concept_relationship

Concept_ancestor

Vocabulary

Source_to_concept_map

Relationship

Concept_synonym

Drug_strength

Standardized vocabularies

Domain

Concept_classDose_era

Condition_era

Drug_era

Results Schema

Cohort_definition

Cohort

Standardized derived elements

Stan

dar

diz

ed

clin

ical

dat

a

Drug_exposure

Condition_occurrence

Procedure_occurrence

Visit_occurrence

Measurement

Observation_period

Payer_plan_period

Provider

Location

Cost

Device_exposure

Observation

Note

Standardized health system data

Fact_relationship

Specimen

Standardized health economics

CDM_source

Standardized metadata

Metadata

Person

Survey_conduct

Location_history

Note_NLP

Visit_detailCare_site

https://github.com/OHDSI/CommonDataModel/wiki

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Single Concept Reference Table

20

Vocabulary ID

All vocabularies stacked up in one

table

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Ancestry Relationships: Higher-Level Relationships

Atrial fibrillation

Fibrillation Atrial arrhythmia

Supraventricular

arrhythmia

Cardiac arrhythmia

Heart disease

Disease of the

cardiovascular system

Controlled

atrial

fibrillation

Persistent atrial

fibrillation

Chronic atrial fibrillation

Paroxysmal atrial

fibrillation

Rapid atrial

fibrillation

Permanent atrial

fibrillation

Concept Relationships

Concepts

Ancestry Relationships

Ancestor

Descendant

5 levels of separation

2 levels of separation

21

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SNOMED-CT

Source codes

ICD10CM

Low-level concepts

Higher-level classifications

OxmisRead

SNOMED-CT

ICD9CM

Top-level classification

SNOMED-CT

MedDRA

MedDRA

MedDRA

Low-level terms

Preferred terms

High-level terms

MedDRA High-level group terms

MedDRA System organ class

ICD10 Ciel MeSHSNOMED

Use of SNOMED in the Standardized Vocabularies

22

• SNOMED is the standard in several domains, e.g. conditions, procedure.

• Powerful Polyhierarchical Structure.

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SNOMED CHALLENGES

• We want to use SNOMED across the world: how to deal with countries that do not (yet) have a license?

• We will require SNOMED extensions to accommodate differences in granularity or classification differences.

• We have to making mappings from many source coding systems to SNOMED in Europe, for example mapping ICPC1 to SNOMED

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Enabling Large-Scale Analysis of Electronic Health Records in

Europe

The EHDEN Project

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Values

EHDEN: VISION AND MISSION

VisionThe European Health Data & Evidence Network (EHDEN) aspires to be the trusted

observational research ecosystem to enable better health decisions, outcomes and care

MissionOur mission is to provide a new paradigm for the discovery and analysis of health data in

Europe, by building a large-scale, federated network of data sources standardised to a common data model

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EHDEN CONSORTIUM

Start date: 1 Nov 2018End date: 30 Apr 2024Duration: 66 months

Non-for-profit organisations

Small to medium-sized companies

EFPIA & Associated partners

Universities, public bodies and research organisations

Almost €29 million

Academic coordinator

EFPIA Lead

22 partners

Innovative Medicines Initiative Project

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EHDEN IS ABOUT ...

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CALL PROCESS FOR DATA PARTNERS AND SMALL TO MEDIUM-SIZED ENTERPRISE (SMES)

Tailored for project objectives and sustainability

Data Partners

Supporting SMEs

Open calls

Focusing on SMEs able to support

mapping and sustainability

Open calls

Workshop

Source Data

Evaluation

Share of Mapping Process

Mapping

Audit

MappingCycle

Evaluated via a pre-defined set of criteria

by the Data source prioritisation committee

Harmonization fund

Data sources can choose the SME from

the pool of EHDEN certified SMEs

SMEs are paid via grants from the

harmonisation fund

Payments are milestone based

Mapped data sources are encouraged to be active members of the EHDEN community,

participating in research studies.

Grant Awarding

Training & Certification

SME certification committee prioritizes SMEs for training and

certification

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THE EHDEN ACADEMY

AimTo develop an e-learning environment to train all stakeholders in the project in the use of the tools and processes that are being adopted in EHDEN

CollaborationCourse development on the OMOP Common Data Model and the rich set of OHDSI tools are developed in collaboration with the OHDSI community

InfrastructureThe EHDEN Academy is being developed in Moodle and is hosted in the Amazon AWS cloud

academy.ehden.eu

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SME CERTIFICATION

1) EHDEN Foundation: Introduction to IMI, EHDEN, OHDSI2) OHDSI-IN-A-BOX Virtual Machine3) OMOP CDM and Standardized Vocabularies4) Extract, Transform and Load5) Analytical Infrastructure

More course will be added in the EHDEN Academy in the future.

• Final certification will contain a two days face-to-face meeting at the Erasmus MC in Rotterdam with all SMEs in the current batch. Multiple persons per SME can participate.

• Final assessment will contain a mapping exercise and installation of the Analytical Infrastructure.

Goal: to provide the SME all the skills to perform the data standardisation task to the OMOP-CDM and train them on the installation of the analytical infrastructure

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SME OPEN CALL RESULTS – APRIL 2019

Applicant countries

34 SME profiles made

28 Eligible applications

11 SMEs initially selected

Batch 1

Batch 2

Now open for applications till end of February!

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SME PILOT CALL

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OPEN CALL FOR DATA PARTNERS

• Draft Call Description has been made available on the website for public review since July. Pilot call opens Sept 1st and closes Sept 15th.

• Different types of grants (max 100.000 Euro):• Create new Data Transformation and Analytical Infrastructure• Revise Existing Data Transformation and Analytical Infrastructure• Inspect Completed Data Transformation and Analytical Infrastructure

• Data Partners from EU Member States and H2020 countries can apply through online application portal.

For more information about the future Open Calls see the EHDEN website: www.ehden.eu

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DATA PARTNER PILOT CALL RESULTS

Applicant countries

48 Data partner profiles made

28 Submitted applications

20 Data Sources selected

>170 million Patient Records

Hospital, GP, Registries, etc.

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What type of Evidence do we generate?

OHDSI and EHDEN in Action

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CLASSIFICATION BY SCIENTIFIC TASKS

Instead of defining health data science by its technical activities, e.g. management, processing, analysis, visualization, we should define the field by its scientific tasks:

1. Description -> Clinical Characterisation: What happened to them?

2. Prediction (inference) -> Patient-Level Prediction: What will happen to me?

3. Counterfactual Prediction (causal inference) -> Population-Level Effect Estimation: What are the causal effects?

Real-World Data is very valuable for all these three Health Data Science Tasks!

Hernán MA. A second change to get causal inference right: A classification of data science tasks. Chance. 2019

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Questions asked across the patient journey

• Clinical characterization

– Treatment Utilization: among patients with diabetes, which treatments are taken when

– Natural history: Who has diabetes, and who takes metformin?

– Quality improvement: What proportion of patients

with diabetes experience complications?

• Patient-level prediction

– Precision medicine: Given everything you know about me, now I started using metformin, what is the chance I will get lactic acidosis?

– Disease interception: Given everything you know about me, what is the chance I will develop diabetes?

• Population-level effect estimation

– Safety surveillance: Does metformin cause lactic acidosis?

– Comparative effectiveness: Does metformin cause lactic acidosis more than glyburide?

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Goal of our work

• Develop transparent and fully reproducible analytical pipelines for all three scientific tasks

• Develop processes and tools to disseminate all the generated evidence

• Create an active community that collaboratively moves this field forward

• Train and educate all the stakeholders to maximally leverage the new paradigm Power of a network of

standardised data!

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Type 2 Diabetes Mellitus Hypertension Depression

OPTUM

GE

MDCDCUMC

INPC

MDCR

CPRD

JMDC

CCAE

Clinical Characterization: Population-level heterogeneity across systems, and patient-level heterogeneity within systems

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Population-Level Effect Estimation: Large-Scale Evidence Generation and Evaluation in a Network of Databases (LEGEND)

58 Outcomes, 9 databases

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Comparisons of hypertension treatments

Not all analyses are valid

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LEGEND

http://data.ohdsi.org/LegendBasicViewer/

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Journey of Patient-Level Prediction

An example of large-scale analysis enabled by data standardisation

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Problem definition

Among a target population (T), we aim to predict which patients at a defined moment in time (t=0) will experience some outcome (O) during a time-at-risk Prediction is done using only information about the patients in an observation window prior to that moment in time.

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Types of prediction problems in healthcareType Structure Example

Disease onset and progression

Amongst patients who are newly diagnosed with <insert your favorite disease>, which patients will go on to have <another disease or related complication> within <time horizon from diagnosis>?

Among newly diagnosed AFib patients, which will go onto to have ischemic stroke in next 3 years?

Treatment choice Amongst patients with <indicated disease> who are treated with either <treatment 1> or <treatment 2>, which patients were treated with <treatment 1> (on day 0)?

Among AFib patients who took either warfarin or rivaroxaban, which patients got warfarin? (as defined for propensity score model)

Treatment response Amongst patients who are new users of <insert your favorite chronically-used drug>, which patients will <insert desired effect> in <time window>?

Which patients with T2DM who start on metformin stay on metformin after 3 years?

Treatment safety Amongst patients who are new users of <insert your favorite drug>, which patients will experience <insert your favorite known adverse event from the drug profile> within <time horizon following exposure start>?

Among new users of warfarin, which patients will have GI bleed in 1 year?

Treatment adherence Amongst patients who are new users of <insert your favorite chronically-used drug>, which patients will achieve <adherence metric threshold> at <time horizon>?

Which patients with T2DM who start on metformin achieve >=80% proportion of days covered at 1 year?

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Current status of predictive modelling

• Inadequate internal validation

• Small sets of features

• Incomplete dissemination of model and results

• No transportability assessment

• Impact on clinical decision making unknown

Relatively few prediction models are used in clinical practice

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OHDSI aims to develop a systematic process to learn and evaluate large-scale patient-level prediction models using observational health data in a data network

OHDSI Mission for Patient-Level Prediction

Evidence

Generation

Evidence

Evaluation

Evidence

Dissemination

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Patient-Level Prediction

51

R-package

www.github.com/OHDSI/PatientLevelPrediction

• Vignettes• Videos• Online training material

Book-of-OHDSI https://ohdsi.github.io/TheBookOfOhdsi/

Study Resultswww.data.ohdsi.org

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LARGE-SCALE PATIENT-LEVEL PREDICTION NOT THE FUTURE!

www.github.com/OHDSI/PatientLevelPrediction

Jenna M Reps, Martijn J Schuemie, Marc A Suchard, Patrick B Ryan, Peter R Rijnbeek; Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data, Journal of the American Medical Informatics Association, Volume 25, Issue 8, 1 August 2018, Pages 969–975, https://doi.org/10.1093/jamia/ocy032

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Model Specification

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Generate R-Package and share with the world

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Share model and performance

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Large scale analysis enables wide-spread dissemination

• The tool auto generates a word document containing all the model specifications, internal and external validation results, model details etc. etc. which serves as a kickstart for result dissemination.

• We can generate numerous visualisations of study results.

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THE POWER OF DISRUPTIVE OPEN SCIENCE: THE STUDY-A-THON CONCEPT

Why do we seem to accept that answering important clinical questions takes a lot of time?

We have an obligation to be disruptive and push hard to change the current paradigm!!

This requires a team effort, no one has all the necessary competences: clinical knowledge, data source expertise, analytics, writing skills, etc.

Why not bring them together at a nice location and focus !!

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OXFORD STUDY-A-THON

To compare the risk of post-operative complications and

mortality between unicompartmentaland total knee replacement.

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WE CAN DO THIS IN ONE WEEK (STUDY-A-THON)??

Monday

Group consensus on the problemDraft cohort definitions

Tuesday

Review clinical characterisationDraft patient-level prediction design

Wednesday

Review patient-level prediction resultsExternally validate prediction model

Thursday

Draft population-level effect estimation designReview population-level effect estimation diagnostics

Friday

Review of resultsPlan for completing publications

“To compare the risk of post-operative complications and mortalitybetween unicompartmental vs total knee replacement.”

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THE SECOND STUDY-A-THON IN BARCELONADifferent Location: Barcelona

Different Topic: Rheumatoid Arthritis

Different Team: RA Experts, Industry, Academia, Data Custodians

More datasources: 14

More countries: USA, Japan, Spain, TheNetherlands, Estonia, UK, Germany, France, Belgium

Different approach:

Protocols were developed prior to the meeting and approved by governance board is applicable.

AIM: Submission of abstracts for European League Against Rheumatism (EULAR) and multiple publications

We will publish a video about this week soon!!

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61

AN EXCITING JOURNEY AHEAD

The uptake of the OMOP-CDM and success of OHDSI enables the EHDEN project to build the European eco-system that brings reliable evidence quicker to our patients.

The EHDEN project is collaborating with OHDSI to further develop the CDM, Vocabularies, and analytical tools.

Interactions with the SNOMED Community are ongoing to collaborate.

Expanding the Data Network, Community, and the support system with SMEs, will drive the sustainability of the eco-system.

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This project has received funding from the Innovative MedicinesInitiative 2 Joint Undertaking (JU) under grant agreement No806968. The JU receives support from the European Union’sHorizon 2020 research and innovation programme and EFPIA.

@IMI_EHDEN

IMI_EHDEN

www.ehden.eu

github.com/EHDEN

62

NEED MORE INFORMATION?

[email protected]

https://book.ohdsi.org

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www.ohdsi-europe.org