Lessons Learned from Mapping UK Pain Datasets to the OMOP CDM

Background

Chronic Pain is defined as pain lasting for three months or longer and a report by the British Pain Society in 2016 found it “affects more than two fifths of the UK population, meaning that around 28 million adults” 1 live with this condition. Chronic pain research is challenging as it is poorly represented in health data and often in silos. Alleviate Pain Data Hub2 is the Advanced Pain Discovery Platform (APDP) Data Hub3 which is removing barriers to improve representation and consistency of pain data and ultimately facilitating access to research data across the UK.

Methods

The aim for Alleviate is to transform pain data sets within the UK to be Findable, Accessible, Interoperable and Reusable (FAIR) and be more discoverable for researchers via Health Data Research UK’s Cohort Discovery Tool (CDT) 4. The tool enables identification of potential cohorts in real-time from datasets Alleviate provides. The onboarding requires datasets to be transformed to the open and widely used Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Alleviate uses the Carrot tools5,6 developed by the Universities of Nottingham, Dundee and Edinburgh, during the CO-CONNECT project7, to perform these mappings consistently and efficiently without necessitating that our data engineers have access to the data. The federated process used for mapping the data can be seen in Figure 1.

Results

We developed Carrot tools, initially for CO-CONNECT and now upgrading to new features for Alleviate and wider use. We use these tools along with our mapping expertise to transform datasets to OMOP-CDM. The tools have improved the efficiency of the mapping of clinical and research data, and we have mapped approximately ~10 million individuals’ records. Subsequently we learned valuable lessons on how to accurately and consistently map across data sets to support federated discovery. We also identified a lack of standard vocabulary representations for pain specific data.

Conclusion

Mapping data to OMOP CDM is not straightforward. Attention to detail is required to ensure that consistent and accurate mapping decisions are made to maximise data reusability and interoperability. In sharing the lessons learned of mapping datasets to OMOP we aim to help improve interoperability of disparate data sources.