Introduction
Real world data (RWD) analysis of electronic medical records (EMR) for comparative efficacy and safety assessment is challenging.1 Two key challenges are factors relating to the data itself and the analytical methods applied on the data. Predominantly observational in nature, RWD is rarely collected and organized in a form suitable for analysis. Disparate data coding standards, database schema architectures and vocabularies – observable even within one organization – can hinder accurate analysis.2 Adopting a common data model (CDM) may address some of these data challenges.
Converting source data into a CDM creates a copy of the original data, reshapes it to fit the CDM structure. Source data elements are translated to the standard vocabularies (e.g. RxNorm for medication data and SNOMED-CT for diagnosis data, as specified by the CDM) and columns from various source data tables are split or merged, to fit into target table columns in the CDM.3,4 CDM conversion of EMR has several merits – the most obvious of which being the ability to conduct multi-database studies and pool results for obtaining valid inferences to important clinical questions.5,6
However, converting data from EMR sources to CDM can be challenging. The value of conversion may not be as apparent to individual institutions as it is to the research community intending to carry out multi-centre analyses. There are however potential benefits of locally analysing a single healthcare system’s data – particularly if the system serves a unique patient population. Healthcare providers often struggle to apply new medical findings to their own patients because the evidence regarding efficacy and safety of a given drug would have been generated by studies involving patients with different clinical characteristics and who were studied under highly controlled research settings.7,8 An introspective analysis of an institution’s EMR data in the post-market setting can generate insights that are directly relevant to the patients that the institution serves.
In this study we explored the usefulness of the CDM by converting EMR data from a tertiary care facility in Singapore, into the Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM). Potential advantages of converting to the OMOP-CDM include its large and active user community and its emphasis on open source software use and analytic code sharing and peer review. This could address another key challenge of RWD – its appropriate analysis. Upon conversion, we illustrated the possibilities that CDM conversion can potentially offer by delving deeper into a use case involving oral anticoagulants (OAC) in patients with atrial fibrillation.