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Oja M, Tamm S, Mooses K, Pajusalu M, Talvik HA, Ott A, Laht M, Malk M, Lõo M, Holm J, Haug M, Šuvalov H, Särg D, Vilo J, Laur S, Kolde R, Reisberg S. Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned. JAMIA Open 2023; 6:ooad100. [PMID: 38058679 PMCID: PMC10697784 DOI: 10.1093/jamiaopen/ooad100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 11/15/2023] [Indexed: 12/08/2023] 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.
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Affiliation(s)
- Marek Oja
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Sirli Tamm
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Kerli Mooses
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Maarja Pajusalu
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Harry-Anton Talvik
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
- STACC, 51009 Tartu, Estonia
| | - Anne Ott
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Marianna Laht
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Maria Malk
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Marcus Lõo
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Johannes Holm
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Markus Haug
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Hendrik Šuvalov
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Dage Särg
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
- STACC, 51009 Tartu, Estonia
| | - Jaak Vilo
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
- STACC, 51009 Tartu, Estonia
| | - Sven Laur
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Raivo Kolde
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Sulev Reisberg
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
- STACC, 51009 Tartu, Estonia
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