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

Author

Listed:
  • 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)

Abstract

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
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    Blog mentions

    As found by EconAcademics.org, 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

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