Observational Medical Outcomes Partnership Common Data Model

The purpose of the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is to establish a standardised framework for organising and analysing observational healthcare data. It aims to harmonise and structure diverse healthcare data sources, such as electronic health records, claims databases, and registries, into a consistent format. By adopting a common data model, researchers, healthcare professionals, and organisations can more effectively collaborate, share, and analyse data across different studies and settings.

The OMOP CDM enables researchers to conduct large-scale, multi-site analyses and generate reliable evidence for various purposes, including comparative effectiveness research, drug safety surveillance, and healthcare outcomes assessment. It facilitates the integration and standardisation of data from different sources, allowing for systematic analyses and the identification of trends, patterns, and associations across diverse patient populations.

The standardised structure of the OMOP CDM promotes data interoperability and facilitates the development and validation of analytical methods and tools. It also supports the use of common vocabularies and terminologies, such as the OHDSI standardised vocabularies, to enhance data consistency and enable meaningful comparisons and analyses.

In summary, the purpose of the OMOP Common Data Model is to promote data standardisation, interoperability, and collaborative research efforts, ultimately facilitating more robust and reliable analyses of observational healthcare data.

We are in collaboration with the VCCC and national hospitals to undertake the conversion of data into the international OMOP common data model format, facilitating more efficient execution of research projects utilising OMOP’s open source dashboards and ATLAS tools. Our experience with common data modelling extends to the conversion of our own Patron primary care data repository (140+ general practices, 2 million patients) across to OMOP which enables researchers to more readily perform international comparisons of clinical datasets and testing of hypotheses.

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