The document discusses the European Medical Information Framework (EMIF) project. EMIF aims to create a platform and framework to integrate patient-level health data from across Europe to enable new research insights. Specifically, EMIF is developing tools and standards to pool data from various sources on over 48 million subjects from 7 EU countries. This will support research on predictors of metabolic diseases and Alzheimer's disease. EMIF is using the tranSMART platform to load clinical trial data and cohorts on over 33,000 subjects for analysis. The goal is for EMIF to become a trusted European hub for healthcare data to optimize clinical research.

1. EMIF
European Medical Information Framework
Bart Vannieuwenhuyse
7 November 2013
tranSMART meeting Paris

2. The IMI is a unique Public-Private Partnership (PPP) between the
pharmaceutical industry represented by the European Federation of
Pharmaceutical Industries and Associations (EFPIA) and the
European Union represented by the European Commission.
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3. Imagine a world where…
 Researchers can access existing patient-level data to gain new
insights into disease etiologies and to define new treatment
targets…
 We can optimize Clinical R&D by 50% using existing patient level
data…
 New treatments are effective in 95% of patients…
 Where we can continuously monitor the risk/benefit profile of new
therapies using real world data…
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4. Secondary use of health data to
improve clinical research
The value of healthcare data for secondary uses in clinical research and
development - Gary K. Mallow, Merck, HIMSS 2012
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5. Challenges with re-use of patient level
data
Data gaps
Data quality
• Missing data elements (e.g. outcomes)
• Longitudinal coherence
• Studies require details that may not be
routinely collected
• Coding for administrative reasons (up
– down coding)
• Coding often only at first level (e.g. ICD9) therefore missing granularity
• Coding often months after patient
encounter
• 80% of info stored as unstructured data
• Data provenance – who entered the
data?
“Semantics”
Privacy
• Many standards – many versions
• Clearly a top priority
• Complex care – many HCP’s involved
– many hand-overs
• Different interpretations by country, by
region – complex
• Need to pool data cross sites and
cross different countries
• TRUST
• Pharma focused on CDISC
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6. Successful example of data
re-use for research
24
Cholinesterase inhibitors
and Alzheimer’s disease
 RWE of ACHeI
 Cost effective
18
Text mining derivation of service
utilisation and costs.
Created in one month
20
MMSE score
22
> 2500 patient years of therapy
documented in EHR-system
> 8 fold dataset compared to
Cochrane
curve derived from clinical trials
Lovestone, S. et al - 2012
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 Resembles cognitive decline
-1
0
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time(years)
2
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7. EMIF Vision
To be the trusted European hub
for health care data intelligence
enabling new insights into
diseases and treatments
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8. Project objectives
EMIF: one project – three topics
1. EMIF-Platform: Develop a framework for
evaluating, enhancing and providing access to human
health data across Europe, to support the two specific
topics below as well as research using human health
data in general
2. EMIF-Metabolic: Identify predictors of metabolic
complications in obesity, with the support of EMIFPlatform
3. EMIF-AD: Identify predictors of Alzheimer’s Disease
(AD) in the pre-clinical and prodromal phase, with the
support of EMIF-Platform
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9. EMIF – platform for modular extension
Risk factor analysis
EMIF - AD
Call x
TBD
Predictive screening
CNS
Call x
Risk stratification
Patient generated data
Research Topics
EMIF - Metabolic
Metabolic
Prevention algorithms
EMIF governance
EMIF - Platform
Data Privacy
Analytical tools
Semantic Integration
Information standards
Data access / mgmt
IMI Structure and Network
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10. Data types in platform:
 Identified categories or “types” of data
 Types:
–
–
–
–
–
–
Primary care data sets
Hospital data
Administrative
Regional record-linkage systems
Disease-specific registries
Biobanks
 Selected representative examples –
 Combined around 48MM subjects from 7 EU countries
 And of course the cohorts from the Research Topics
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11. Data bases “typology”
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12. Data
access
Module
Site 2
Data
access
Module
Data
access
Module
Common ontology
Site 1
Data
access
Module
Common ontology
EMIF systems view
R
archive
Q
catalog
extract
TTP
R
User
admin
Remote
user 1
User
admin
Remote
user 2
User
admin
Remote
user 3
archive
Q
catalog
extract
TTP
Site 3
Data
access
Module
cohorts
User
Transactional
environment
Common ontology
Transient
data-pool
R
Analytical
tools
archive
Q
1:1
catalog
TTP
Local EMIF solution
Cloud-based EMIF solution
Security
Infrastructure
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13. EMIF catalogue - prototype
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14. Researcher
Browsing through directory of “data fingerprints”
Controled data access based on usage rights (Private Remote Research Environments)
AD
Metabolics
Metabolics
3
Common Data Model
Cohorts
Cohorts
Cohorts
Cohorts
Principle: EMIF will offer a
platform
to
integrate
available
data
allowing
pooled analysis
Principle: EHR data enables
the search for patients with
specific characteristics to
form new cohorts.
Data
enrichment
Patient
selection
4
Cross
Validation
2
Analytical tools / methods
1
AD
EHR datasets
EHR datasets
Historic patient data
allowing “roll-back” to
study trajectories
Source of new
epidemiology insights for
patient sub-segments
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15. Re-using existing solutions
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16. Data being loaded in tranSMART
AD
MCI
Cohort 1
259
225
Cohort 2
160
100
ADNI 1
188
Subjective Memory
impairment
405
Cohort 3
20
716
80
390
229
822
881
Cohort 4
Total
232
50
CTL/Controls
881
14
Total
607
1631
64
76
420
Cohort 5
42
420
1003
3305
4 times the number of subjects in ADNI
Data available for analysis end Oct 2013
Data structure aligned with CDISC
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17. AD Cohort upload into tranSMART
Cohort track
tranSMART track
• Cohort selection
• Install PostgreSQL version
• AddNeuroMed, Descripa, ADNI, 2 Spanish
cohorts
• tranSMART release 1.1 installed
• Develop process for data privacy protection
• Curation process
• Understand documentation & column headers
• Development “Generic Taxonomy” with consistent
variable naming
• Map variables to global taxonomy
• CDISC compliance
• Select priority variables
• Develop mapping files & upload scripts
• Sent proposal (based on Barcelona headers) to
Custodix
• Develop process for user authentication
and authorization
• Connect tranSMART to Custodix CIAM
• Sent approval process proposal to Custodix (Jul
10), awaiting feedback
• Adapt to new upload process in 1.1 release
EMIF tranSMART release 1: independent cohorts
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18. tranSMART platform – analytical
tools
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19. Additional data
Cohorts
MCI
Controls
Cognition
3242
3242
148
13000
4125
4273
358
150
350
500
470
300
60
300
120
360
120
360
120
100
300
200
200
300
500
300
50
150
MRI
Plasma
Genomics
2900
2900
555
358
500
500
45
360
120
360
300
50
300
300
300
300
50
150
CSF
150
150
50
50
50
50
50
50
50
50
50
500
500
400
300
300
400
150
1080
150
1080
100
409
64
150
1080
84
300
150
400
84
300
1718
7149
6302
50
64
300
Totals
20
84
300
100
300
84
280
3052
21607
11659
2872
Access to ~24000 subjects*
important amount of pheno/genotype data available
Involvement pending on scientific agenda
*Associated data providers in consortium
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20. More info
 EMIF general
–
–
–
Bart Vannieuwenhuyse (bvannieu@its.jnj.com)
Simon Lovestone (simon.lovestone@kcl.ac.uk)
Johan van der Lei (j.vanderlei@erasmusmc.nl)
 EMIF-Platform
–
–
Johan van der Lei (j.vanderlei@erasmusmc.nl)
Patrick Genyn (pgenyn1@its.jnj.com)
 EMIF-Metabolics
–
–
Ulf Smith (ulf.smith@medic.gu.se)
Dawn Waterworth
(Dawn.M.Waterworth@gsk.com)
www.emif.eu
 EMIF-AD
–
–
Pieter Jelle Visser
(pj.visser@maastrichtuniversity.nl)
Mike Krams (mkrams@its.jnj.com)
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