IDEAS home Printed from
   My bibliography  Save this article

Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment


  • Seamus Kent

    (National Institute for Health and Care Excellence)

  • Edward Burn

    (University of Oxford
    Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol))

  • Dalia Dawoud

    (National Institute for Health and Care Excellence)

  • Pall Jonsson

    (National Institute for Health and Care Excellence)

  • Jens Torup Østby

    (Pfizer Inc)

  • Nigel Hughes

    (Janssen Research and Development)

  • Peter Rijnbeek

    (Erasmus University Medical Center)

  • Jacoline C. Bouvy

    (National Institute for Health and Care Excellence)


There is growing interest in using observational data to assess the safety, effectiveness, and cost effectiveness of medical technologies, but operational, technical, and methodological challenges limit its more widespread use. Common data models and federated data networks offer a potential solution to many of these problems. The open-source Observational and Medical Outcomes Partnerships (OMOP) common data model standardises the structure, format, and terminologies of otherwise disparate datasets, enabling the execution of common analytical code across a federated data network in which only code and aggregate results are shared. While common data models are increasingly used in regulatory decision making, relatively little attention has been given to their use in health technology assessment (HTA). We show that the common data model has the potential to facilitate access to relevant data, enable multidatabase studies to enhance statistical power and transfer results across populations and settings to meet the needs of local HTA decision makers, and validate findings. The use of open-source and standardised analytics improves transparency and reduces coding errors, thereby increasing confidence in the results. Further engagement from the HTA community is required to inform the appropriate standards for mapping data to the common data model and to design tools that can support evidence generation and decision making.

Suggested Citation

  • Seamus Kent & Edward Burn & Dalia Dawoud & Pall Jonsson & Jens Torup Østby & Nigel Hughes & Peter Rijnbeek & Jacoline C. Bouvy, 2021. "Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment," PharmacoEconomics, Springer, vol. 39(3), pages 275-285, March.
  • Handle: RePEc:spr:pharme:v:39:y:2021:i:3:d:10.1007_s40273-020-00981-9
    DOI: 10.1007/s40273-020-00981-9

    Download full text from publisher

    File URL:
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL:
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item

    As the access to this document is restricted, you may want to search for a different version of it.


    Blog mentions

    As found by, the blog aggregator for Economics research:
    1. Journal round-up: PharmacoEconomics 39(3)
      by Chris Sampson in The Academic Health Economists' Blog on 2021-04-05 06:00:12

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pharme:v:39:y:2021:i:3:d:10.1007_s40273-020-00981-9. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.