Research Article Conversion of Traditional Korean Medicine Hospital Electronic Health Records to the OMOP Common Data Model: Methodology and Implications
by Man Young Park1, Jaeuk U Kim1,6, Sun-Mi Choi2,3, Youngheum Yoon3, Byung-Kwan Seo3,4, Sangkwan Lee5*
1Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
2Department of Data Science for Korean Medicine, Korea Institute of Oriental Medicine, Daejeon, South Korea
3Big Data Center for Korean Medicine, National Institute for Korean Medicine Development, Seoul, South Korea
4Department of Acupuncture & Moxibustion, College of Korean Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seoul, South Korea
5Department of Internal Medicine and Neuroscience, College of Korean Medicine, Wonkwang University, Iksan, South Korea
6KM Convergence Science, University of Science and Technology, Daejeon, South Korea
*Corresponding author: Sangkwan Lee, Department of Internal Medicine and Neuroscience, College of Korean Medicine, Wonkwang University 460 Iksan-daero, Sin-dong, Iksan, Jeollabuk-do, 54538, Republic of Korea.
Received Date: 14 February 2025
Accepted Date: 26 February 2025
Published Date: 3 March 2025
Citation: Park MY, Kim JU, Choi S-M, Yoon Y, Seo B-K, et al. (2025) Conversion of Traditional Korean Medicine Hospital Electronic Health Records to the OMOP Common Data Model: Methodology and Implications. Curr Res Cmpl Alt Med 9: 266. https://doi.org/10.29011/2577-2201.100266
Abstract
Background: The standardization of Traditional Korean Medicine (TKM) data is crucial for enhancing its integration with global healthcare systems and facilitating evidence-based practice. This study aimed to convert electronic health records (EHRs) from a TKM hospital to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Methods: We converted EHR data from Wonkwang University Gwangju Korean Medicine Hospital to the OMOP CDM format. The process involved mapping TKM-specific concepts to standard terminologies, developing new concepts where necessary, and implementing an Extract, Transform, Load (ETL) procedure. We utilized ACHILLES for data quality assessment and visualization. Results: The conversion resulted in a standardized dataset named WKTKM (Wonkwang Traditional Korean Medicine) database, including data from 88,449 patients, comprising 4,015,386 condition occurrences, 9,968,003 drug exposures, and 10,472,161 procedure occurrences. We successfully mapped most laboratory and medication codes to standard concepts. For TKM-specific concepts, we created a new terminology system 'KIOM'. Conclusion: This study demonstrates the feasibility of converting TKM hospital data to the OMOP CDM, marking a significant milestone in the international standardization of TKM data. The standardized database provides a foundation for large-scale, multi-center studies and comparative effectiveness research, potentially facilitating the integration of TKM with conventional medical practices and promoting international collaborations in TKM research.
Keywords: Data standardization; Electronic health records; Evidence-based medicine; OMOP common data model; Traditional korean medicine