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

Abstract

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 2012-2019. For the sample, complete information from all three 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 three 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.

Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model

“…MAITT, 12 IQVIA DA, 13 IQVIA LPD, and IMASIS 14 (database information in Table 2 and Data S3). All databases had been previously mapped to the OMOP CDM.…”

“…Using the dose formula for the specific route and drug strength pattern of the drug concepts of interest, we calculated overall median daily dose and the 25th and 75th percentile, and additional stratified results by route and drug strength pattern. The benchmarking was carried out in the three databases from which we derived the dose formulas and additionally in four European databases representing a variety of healthcare settings: MAITT, 12 IQVIA DA, 13 IQVIA LPD, and IMASIS 14 (database information in Table 2 and Data S3). All databases had been previously mapped to the OMOP CDM.…”

The use of prescription drugs and health care services during the 6-month post-COVID-19 period

“…Data was mapped to the Observational Medical Outcomes Partnership (OHDIS OMOP) common data model version 5.3 39 . The mapping process and detailed description of the databases have been previously described by Oja et al 40 .…”

Using electronic health records to evaluate the adherence to cervical cancer prevention guidelines: a cross-sectional study

“…The databases were linked using a unique personal code given to all persons living in Estonia. The data was transferred to Observational Medical Outcomes Partnership (OMOP) common data model (CDM) version 5.3 [22] described in more detail by Oja et al [23]. The study sample consisted of randomly selected 10% of the Estonian female population aged 16-65 years (N = 50 702) and followed the age distribution of the whole female population [23].…”