Eos and OMOCL: towards a seamless integration of openEHR records into the OMOP Common Data Model
BACKGROUND: The reuse of data from electronic health records (EHRs) for research purposes promises to improve the data foundation for clinical trials and may even support to enable them. Nevertheless, EHRs are characterized by both, heterogeneous structure and semantics. To standardize this data for...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
24 July 2023
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| In: |
Journal of biomedical informatics
Year: 2023, Volume: 144, Pages: 1-22 |
| ISSN: | 1532-0480 |
| DOI: | 10.1016/j.jbi.2023.104437 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.jbi.2023.104437 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S1532046423001582 
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| Author Notes: | Severin Kohler, Diego Boscá, Florian Kärcher, Birger Haarbrandt, Manuel Prinz, Michael Marschollek, Roland Eils |
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| 245 | 1 | 0 | |a Eos and OMOCL |b towards a seamless integration of openEHR records into the OMOP Common Data Model |c Severin Kohler, Diego Boscá, Florian Kärcher, Birger Haarbrandt, Manuel Prinz, Michael Marschollek, Roland Eils |
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| 520 | |a BACKGROUND: The reuse of data from electronic health records (EHRs) for research purposes promises to improve the data foundation for clinical trials and may even support to enable them. Nevertheless, EHRs are characterized by both, heterogeneous structure and semantics. To standardize this data for research, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standard has recently seen an increase in use. However, the conversion of these EHRs into the OMOP CDM requires complex and resource intensive Extract Transform and Load (ETL) processes. This hampers the reuse of clinical data for research. To solve the issues of heterogeneity of EHRs and the lack of semantic precision on the care site, the openEHR standard has recently seen wider adoption. A standardized process to integrate openEHR records into the CDM potentially lowers the barriers of making EHRs accessible for research. Yet, a comprehensive approach about the integration of openEHR records into the OMOP CDM has not yet been made. METHODS: We analyzed both standards and compared their models to identify possible mappings. Based on this, we defined the necessary processes to transform openEHR records into CDM tables. We also discuss the limitation of openEHR with its unspecific demographics model and propose two possible solutions. RESULTS: We developed the OMOP Conversion Language (OMOCL) which enabled us to define a declarative openEHR archetype-to-CDM mapping language. Using OMOCL, it was possible to define a set of mappings. As a proof-of-concept, we implemented the Eos tool, which uses the OMOCL-files to successfully automatize the ETL from real-world and sample EHRs into the OMOP CDM. DISCUSSION: Both Eos and OMOCL provide a way to define generic mappings for an integration of openEHR records into OMOP. Thus, it represents a significant step towards achieving interoperability between the clinical and the research data domains. However, the transformation of openEHR data into the less expressive OMOP CDM leads to a loss of semantics. | ||
| 650 | 4 | |a Databases, factual | |
| 650 | 4 | |a EHR | |
| 650 | 4 | |a Electronic health records | |
| 650 | 4 | |a ETL | |
| 650 | 4 | |a OHDSI | |
| 650 | 4 | |a OMOP | |
| 650 | 4 | |a OpenEHR | |
| 650 | 4 | |a Reference standards | |
| 650 | 4 | |a Secondary use | |
| 650 | 4 | |a Semantics | |
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