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

• Published in: JAMIA open Vol. 6; no. 4; p. ooad100
• Main Authors: Oja, Marek, Tamm, Sirli, Mooses, Kerli, Pajusalu, Maarja, Talvik, Harry-Anton, Ott, Anne, Laht, Marianna, Malk, Maria, Lõo, Marcus, Holm, Johannes, Haug, Markus, Šuvalov, Hendrik, Särg, Dage, Vilo, Jaak, Laur, Sven, Kolde, Raivo, Reisberg, Sulev
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.12.2023
• Subjects: Computational linguistics; Drugs; Language processing; Medical records; Natural language interfaces; Online databases; Online health care information services; Research and Applications; Estonia; mapping; OMOP; ETL; electronic health record; EHR
• ISSN: 2574-2531; 2574-2531
• DOI: 10.1093/jamiaopen/ooad100

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. 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 (  = 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. 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. During the transformation process, we encountered several challenges, which are described in detail with concrete examples and solutions. 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.