Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned

Objective To describe the reusable transformation process of electronic health records (EHR), claims, and prescriptions data into Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM), together with challenges faced and solutions implemented.Materials and Methods We used Estonian national health databases that store almost all residents' claims, prescriptions, and EHR records. To develop and demonstrate the transformation process of Estonian health data to OMOP CDM, we used a 10% random sample of the Estonian population (n = 150 824 patients) from 2012 to 2019 (MAITT dataset). For the sample, complete information from all 3 databases was converted to OMOP CDM version 5.3. The validation was performed using open-source tools.Results In total, we transformed over 100 million entries to standard concepts using standard OMOP vocabularies with the average mapping rate 95%. For conditions, observations, drugs, and measurements, the mapping rate was over 90%. In most cases, SNOMED Clinical Terms were used as the target vocabulary.Discussion During the transformation process, we encountered several challenges, which are described in detail with concrete examples and solutions.Conclusion For a representative 10% random sample, we successfully transferred complete records from 3 national health databases to OMOP CDM and created a reusable transformation process. Our work helps future researchers to transform linked databases into OMOP CDM more efficiently, ultimately leading to better real-world evidence. Health data can be found in various sources and formats, making it challenging for researchers. To address this issue, one possible approach is to transform the data into a standardized common data model (CDM). In this study, we describe the process of converting electronic health records (EHR), claims, and prescriptions data into the Observational Medical Outcome Partnership (OMOP) CDM, along with the challenges faced and solutions implemented. We used Estonian national health databases containing information on claims, prescriptions, and EHR records of 10% of Estonian residents (MAITT dataset). The study describes how data were mapped to standardized vocabulary and successfully converted to the OMOP CDM. We discuss the encountered difficulties and problems and propose solutions to help future researchers transform linked databases into OMOP CDM more efficiently, leading to better real-world evidence.