to$use$paentcentric$datato$derive$ … · • Perform$amul*9database$study$ ......

1 RWE perspec*ves Budapest 2016 March 2016

RWE Perspec,ves

“…to use pa*ent-‐centric data to derive real-‐life insights into value of medicines for individual pa*ents.”

Rients van Wijngaarden

March 2016

Budapest 2016


Outline

•  What is PHARMO •  Oral contracep*ves, a case study •  Challenges in leveraging EHR data


What is PHARMO


What is PHARMO In-‐house Regional core databases:

•  Out-‐pa*ent pharmacy •  In-‐pa*ent pharmacy •  General Prac**oner •  Clinical laboratory

Na*onal databases: •  Hospitaliza*on •  Death registry

Linked databases (predefined governance)

•  Na*onal perinatal registry •  Na*onal pathology registry •  Na*onal cancer registra*on

Incidental linked registries, such as

•  Vaccina*on registry •  Genera*on R •  …


Research area’s

•  Pharmacoepidemiology •  Drug U*liza*on studies •  Basic Epidemiology •  Post Authorisa*on Safety Studies (PASS) •  Post Authorisa*on Outcomes Studies (PAOS)


Oral contracep,ves, a case study

•  OC’s are widely used; exposure is huge •  Side effects have been associated with OC use:

•  Deep vein thrombosis •  Pulmonary embolism •  etc.

•  However the risks are rela*vely small: •  Hard to detect with RCT’s •  Large datasets from daily prac*ce needed



•  Perform a mul*-‐database study •  4 databases selected:

•  IPCI and PHARMO in the Netherlands •  THIN in the UK •  HSD in Italy

•  Results published: Bezemer ID, Verhamme KM, Gini R, Mosseveld M, Rijnbeek PR, Trifiro G, et al. Use of oral contracep*ves in three European countries: a popula*on-‐based mul*-‐database study. The European journal of contracep6on & reproduc6ve health care. 2015:1-‐7.



•  Where do these differences come from? •  Prescrip*on vs dispensing (ie. resupply strategy) •  Prescribed by specialists

•  Why only 4 databases? •  Oral contracep*ves are only partly reimbursed •  Therefore not captured in many databases


Challenges in leveraging EHR data •  Even if:

•  Mapped to a Common Data Model •  Seman*c interoperability •  Results are meaningless without context

•  We have to deal with constraints • What is captured? •  How/why is it captured? • What is not captured?

•  Constraints can change over *me •  There is no ‘one size fits all’


WWW.PHARMO.COM


Challenges in leveraging EHR data

•  What is needed? •  Feasability assesment

–  Is the event/drug of interest properly captured? –  Is there a ‘gold standard’ available?

•  Expert knowledge –  A solid understanding of what is captured, why and by whom

•  Collabora*on –  Get a feeling for what is missing

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