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Ca se study: Cha lle ng e s fa c e d b y E MI F in utilising - - PowerPoint PPT Presentation

Feb 28, 2024 146 likes •385 views

Ca se study: Cha lle ng e s fa c e d b y E MI F in utilising the OMOP CDM Jo ha n va n de r L e i E ra smus Me dic a l Ce nte r Ro tte rda m Outline Sc a ffo lding E MI F a nd a CDM E MI F a nd the OMOP CDM Ong o

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Ca se study: Cha lle ng e s fa c e d b y E MI F in utilising the OMOP CDM

Jo ha n va n de r L e i E ra smus Me dic a l Ce nte r Ro tte rda m

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SLIDE 2

Outline

  • Sc a ffo lding
  • E

MI F a nd a CDM

  • E

MI F a nd the OMOP CDM

  • Ong o ing a c tivitie s/ c ha lle ng e s

2 De c 2017 E uro pe a n Me d ic a l Info rma tio n F ra me wo rk

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SLIDE 3

I nfo rma tio n

  • Synta c tic : “g ra mma r”
  • Se ma ntic : “me a ning ”
  • Pra g ma tic : “c o nse q ue nc e s”

3 De c 2017 E uro pe a n Me d ic a l Info rma tio n F ra me wo rk

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SLIDE 4

synta c tic se ma ntic pra g ma tic

CDM Analytical tools Mapped data Source Data Regulatory context

Vie ws o n info rma tio n

4 De c 2017 E uro pe a n Me d ic a l Info rma tio n F ra me wo rk

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SLIDE 5

synta c tic se ma ntic pra g ma tic

Vie ws o n info rma tio n

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SLIDE 6

CDM…

  • On the synta c tic le ve l
  • Multiple so lutio ns po ssib le
  • Mo de ls a re dyna mic
  • De b a te o fte n: se ma ntic a nd pra g ma tic
  • But tha t disc ussio n is o fte n inde pe nde nt o f a

spe c ific mo de l

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SLIDE 7
  • T

he q ue stio n whic h CDM to use is pro b a b ly no t the rig ht q ue stio n……

F ro m me dic a l info rma tic s pe rspe c tive :

synta c tic se ma ntic pra g ma tic

  • I

s a ‘ so c io -te c hnic a l’ c o nstruc t

  • Ca n o nly b e unde rsto o d in

c o nte xt

  • De fine s ro le s a nd re spo nsib ilitie s

7 De c 2017 E uro pe a n Me d ic a l Info rma tio n F ra me wo rk

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SLIDE 8

Pro je c t o ve rvie w

ACADEMIC PARTNERS

SME PART NE RS E F PIA PART NE RS PAT IE NT ORGANISAT I ON

14 E uro pe a n c o untrie s c o mb ining 57 pa rtne rs

€56 millio n wo rth o f re so urc e s 3 pro je c ts in o ne 5 ye a r pro je c t (2013–2017)

37 10 9 1

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SLIDE 9
  • Biomarke r disc ove ry
  • Pre dic tive mode lling
  • Dise ase insight ge ne ration

(opportunity ide ntific ation)

  • T

rial de sign and fe asibility analysis

  • E

le c tronic he alth re c ord (E HR)-fac ilitate d re c ruitme nt

  • Prospe c tive c ohort

se le c tion

  • Analysis of tre atme nt

pathways

  • Colle c tion of c linic al and

e c onomic e vide nc e

  • Ongoing e ffic ie nc y and

safe ty monitoring

Why is E MI F ne e de d?

Po te ntia l a pplic a tio ns o f Re a l Wo rld Da ta

Discovery Development Launch/ Post-Launch

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E MI F Se tting

  • Da ta fro m ve ry dive rse so urc e s
  • Po pula tio n b a se d
  • Ho spita l b a se d
  • Dise a se spe c ific c o ho rts
  • Bio b a nks
  • Dive rse da ta
  • Bro a d spe c trum o f re se a rc h q ue stio ns
  • Ove ra ll purpo se : fa c ilita te re -use o f da ta

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E MI F a nd CDM Cha lle ng e s

  • Cle a r ne e d fo r a CDM
  • Bro a d spe c trum o f c o ding sc he me s, la ng ua g e s, a nd

se tting s

  • Ne e d to sto re AL

L so urc e da ta inc luding so urc e vo c a b ula rie s

  • Po ssib ility to e sc a pe / re fine to study-spe c ific so lutio ns
  • Re pro duc ib le re se a rc h: Ope n, T

ra nspa re nt, So urc e da ta , Ma pping s, Ana lytic a l to o ls

  • F

le xib ility in ro le tra nsfe r (e .g ., study c o o rdina to r)

  • Multiple te c hnic a l infra struc ture s

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SLIDE 12

E MI F a nd OMOP CDM: why?

  • No silve r b ulle t…
  • b ut no t ye t a no the r mo de l !!!
  • Dive rsity o f the E

U se tting : Suppo rt fo r Sta nda rdize d Vo c a b ula rie s

  • No t limite d to spe c ific a na lytic a l use c a se
  • Ope n so urc e
  • Multiple pla tfo rms
  • OHDSI
  • Ope n c o lla b o ra tive
  • Gro wing in E

U

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SLIDE 13

E MI F Da ta b a se s b e ing ma ppe d to the OMOP-CDM

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E T L re q uire s multi-disc iplina ry te a m

Medical knowledge ETL Development Database / Infrastructure management Project Coordination CDM Knowledge (Local) Data knowledge

Local EMIF

Depending on preferences & available skills, EMIF can take on different roles

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SLIDE 15

T

  • o ls suppo rting the pro c e ss

White rabbit Rabbit in a hat github usagi Achilles

Ana lyze Da ta So urc e E va lua te Infra struc ture De fine Ma pping L

  • g ic

De ve lo p Ma pping s (E T L ) T e st E T L De plo y E T L Ac c e pta nc e T e sting / QA 15 De c 2017 E uro pe a n Me d ic a l Info rma tio n F ra me wo rk

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SLIDE 16

16 No v 2017 E MI F Intro d uc tio n

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Curre nt Cha lle ng e s: E T L

The following factors were found to be most impactful on overall speed and quality of the ETL:

  • 1. Source Database research readiness: The ‘quality’ of the input

data structure – and the availability of internal knowledge on how the database is defined- are the primary driver of efficiency and quality of the CDM Mapping.

  • 2. Strong project management: superior results in terms of quality

and speed can be achieved when resources are allocated and active project management is executed.

  • 3. Vocabulary mappings: establishing the vocabulary mappings is

the most resource intensive step. It’s recommended to set realistic goals with associated timings (e.g. map the top 20% of lab tests, covering 80% of all occurrences).

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E va lua tio n o f tra nsla tio n: Struc tura l Ma pping

Did all my source data end up in the CDM? Prevalence Source Prevalence CDM

  • Can be very good reason for differences: business rules assessment
  • Iterative process to optimize the ETL
  • No structural CDM limitations encountered so far

Lost in translation?

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E va lua tio n o f tra nsla tio n: Vo c a b ula ry Ma pping

  • High data coverage.
  • Term coverage is further improved by extending the Standard

Vocabularies, e.g. RxNorm-Extension to accommodate European Drug market IPCI Database Example

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Curre nt E MI F CDM Ac tivitie s

  • Re plic a tio n o f e xisting E

MI F Use Ca se (s) o n CDM

  • Co ntrib ute to vo c a b ula ry e xte nsio n
  • Co ntrib ute to to o l de ve lo pme nt
  • T

ra ining o f sta ke ho lde rs in using the CDM a nd OHDSI T

  • o ls
  • I

nitia te a nd pa rtic ipa te in OHDSI Ne two rk studie s

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E xa mple : T re a tme nt Pa thwa y Study

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Hripcsak G. et al. Characterizing treatment pathways at scale using the OHDSI network. PNAS 2016 113 (27) 7329-7336;

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SLIDE 22

I PCI : T ype 2 Dia b e te s

EMIF will run this study

  • n CDM databases in

Europe

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F ina l Re ma rks

  • E

MI F will inte nsify its pa rtic ipa tio n in the OHDSI ne two rk b y suppo rting the E uro pe a n OHDSI initia tive (www.o hdsi-e uro pe .o rg ) c o o rdina te d b y E ra smus MC

  • E

MI F is in the pro c e ss o f a susta ina b ility a sse ssme nt to suppo rt a nd use the da ta ne two rk po st E MI F We b e lie ve tha t the a do ptio n o f the OMOP-CDM a nd the a c tive OHDSI c o mmunity will e na b le tra nspa re nt a nd re pro duc ib le re se a rc h a t a n unpre c e de nte d sc a le in E uro pe

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