Repeatable process for extracting health data from HL7 CDA documents
After a decade of focused work, our research has led to the development of an ETL pipeline that effectively transforms HL7 CDA documents into OMOP CDM in Estonia, addressing key data extraction and transformation challenges. The pipeline's repeatability and adaptability to various data subsets make it a valuable resource for researchers dealing with health data. While tested on Estonian data, the principles outlined are broadly applicable, potentially aiding in handling health data standards that vary by country. Despite newer health data standards emerging, the relevance of CDA for retrospective health studies ensures the continuing importance of this work.
Authors
- Jaak Vilo (ORCID: https://orcid.org/0000-0001-5604-4107)
- Kerli Mooses (ORCID: https://orcid.org/0000-0002-3651-6063)
- Harry-Anton Talvik (ORCID: https://orcid.org/0000-0002-1423-1497)
- Hendrik Šuvalov (ORCID: https://orcid.org/0000-0001-9625-3552)
- Sulev Reisberg (ORCID: https://orcid.org/0000-0001-6835-9632)
- Raivo Kolde (ORCID: https://orcid.org/0000-0003-2886-6298)
- Dage Särg (ORCID: https://orcid.org/0000-0002-9077-1604)
- Marek Oja (ORCID: https://orcid.org/0000-0002-3650-8194)
- Sirli Tamm (ORCID: https://orcid.org/0009-0009-8565-3572)
- Sven Laur (ORCID: https://orcid.org/0000-0002-9891-3347)
- Marcus Lõo (ORCID: https://orcid.org/0009-0003-1306-5639)
- Õie Renata Siimon
Institutions
- University of Tartu (EE)
Publication Details
- Journal
- Journal of Biomedical Informatics
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1016/j.jbi.2024.104765
- Citations
- 4
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- Eesti Teadusagentuur