Optimising clinical data standardisation to enable scalable, low-cost research

Oxford University Hospitals is a major NHS teaching trust and a leading centre of clinical excellence. As part of its work to establish the Thames Valley and Surrey Secure Data Environment, the trust needed to ensure that clinical data could be accessed by researchers using standard research tooling.

Alongside the national rollout of Secure Data Environments, Oxford University Hospitals adopted the Observational Medical Outcomes Partnership common data model. The aim was to provide researchers with seamless access to high-quality, standardised clinical data at a lower cost and with reduced effort.

However, mapping complex clinical datasets into the OMOP model required a robust, repeatable approach that balanced accuracy, transparency and long-term maintainability. Off-the-shelf solutions did not fully meet the need for close integration with documentation and governance processes.

Answer Digital worked with Oxford University Hospitals to assess the available options and design an approach that aligned with the trust’s requirements.

We carried out an options appraisal, assessing both off-the-shelf and bespoke solutions against validated business needs. A custom approach was selected to ensure tight coupling between data transformation and documentation, providing greater long-term value despite higher initial effort.

We then built and iterated the solution while mapping four source datasets into the OMOP Common Data Model version 5.4. A structured process was developed to define scope, analyse source data, transform it into the target model and validate the outputs internally and externally.

To support adoption and transparency, we established a public GitHub repository, allowing the research community to review mappings and provide feedback. The tooling was designed to generate both human-readable documentation and machine-readable outputs, enabling reuse within Oxford University Hospitals’ existing data management tooling.

Oxford University Hospitals now has a scalable and documented approach to mapping clinical data into the OMOP model, including oncology-specific datasets.

The solution provides a repeatable process that can be reused in future phases of Secure Data Environment development, improving efficiency and consistency as new datasets are onboarded. Researchers benefit from easier access to standardised, analysis-ready data, reducing the time and cost required to begin studies.

By combining robust mapping, transparent documentation and community engagement, the work supports higher-quality research and contributes to improved patient outcomes through more efficient and reproducible analysis.