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. 2023 Dec 5;6(4):ooad100.
doi: 10.1093/jamiaopen/ooad100. eCollection 2023 Dec.

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

Marek Oja  1 , Sirli Tamm  1 , Kerli Mooses  1 , Maarja Pajusalu  1 , Harry-Anton Talvik  1   2 , Anne Ott  1 , Marianna Laht  1 , Maria Malk  1 , Marcus Lõo  1 , Johannes Holm  1 , Markus Haug  1 , Hendrik Šuvalov  1 , Dage Särg  1   2 , Jaak Vilo  1   2 , Sven Laur  1 , Raivo Kolde  1 , Sulev Reisberg  1   2
Affiliations

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

Marek Oja et al. JAMIA Open. .

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 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.

Keywords: EHR; ETL; OMOP; electronic health record; mapping.

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Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
The data acquisition process of national health databases in Estonia and the context of this article.
Figure 2.
Figure 2.
Overview of the data transformation process.
Figure 3.
Figure 3.
Population pyramids of an Estonian population in 2019 (lines) and the study sample (bars).
See this image and copyright information in PMC

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