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. 2021 Mar;39(3):275-285.
doi: 10.1007/s40273-020-00981-9. Epub 2020 Dec 18.

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

Affiliations

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

Seamus Kent et al. Pharmacoeconomics. 2021 Mar.

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.

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Conflict of interest statement

JTØ is an employee of Pfizer, an international pharmaceutical company that develops and markets drugs in a number of therapeutic areas. NH is an employee and shareholder at the pharmaceutical company J&J. PR received unconditional grants from Janssen Research & Development and the Innovative Medicines Initiative during the conduct of this work. All other authors report no conflicts of interest.

Figures

Fig. 1
Fig. 1
Overview of the OMOP common data model version 6.0 [8]. The tables relating to standardised vocabularies provide comprehensive information on mappings from between source and standard concepts and hierarchies for standard concepts (e.g. concept_ancestor). CDM common data model, NLP natural language processing
Fig. 2
Fig. 2
A visual representation of vocabularies and their relationships in the condition domain of the OMOP common data model [8]. ICD International Classification of Diseases, ICD-9 ICD, Ninth Revision, ICD-10-CM ICD Tenth Revision, Clinical Modification, MedDRA Medical Dictionary for Regulatory Activities, MeSH medical subject heading, SNOMED-CT Standard Nomenclature of Medicine Clinical Terminology

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