Evidence map›Paper›PMID 41840573›Full record

ArticleBMC medical informatics and decision making2026

Transforming Traditional Korean Medicine hospital EHRs into the OMOP common Data Model: methodology and implications.

Man Young Park, Jaeuk U Kim, SunMi Choi, Youngheum Yoon, Byung-Kwan Seo, Sangkwan Lee

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Man Young ParkDigital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea.
Jaeuk U KimDigital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea.
SunMi ChoiDepartment of Data Science for Korean Medicine, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea.
Youngheum YoonDigital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea.
Byung-Kwan SeoBig Data Center for Korean Medicine, National Institute for Korean Medicine Development, Seoul, Republic of Korea.
Sangkwan LeeDepartment of Internal Medicine and Neuroscience, College of Korean Medicine, Wonkwang University, 460 Iksan-daero, Sin-dong, Iksan, Jeollabuk-do, 54538, Republic of Korea. forher3@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStandardizing data from Traditional Korean Medicine (TKM) is essential for enhancing interoperability with international real-world data infrastructures, such as multi-institutional OMOP-CDM databases within the OHDSI network, multinational claims databases, and large-scale clinical data repositories, thereby enabling evidence-based research. This study aimed to convert electronic health records (EHRs) from a TKM hospital into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).

methodsWe transformed the EHR data from Wonkwang University Gwangju Korean Medicine Hospital into the OMOP CDM format. TKM-specific diagnoses, procedures, and medications were mapped to standard vocabularies, and new concept codes were developed when no standard terms were available. Beyond a direct application of conventional CDM transformation procedures, we established explicit mapping principles and introduced structural extensions-such as the KIOM terminology system-to accommodate pattern identification, complex herbal prescriptions, and detailed acupuncture techniques that could not be represented within existing vocabularies. An Extract, Transform, Load (ETL) process was conducted, and data quality was evaluated using the ACHILLES tool.

resultsThe converted dataset, named the Wonkwang Traditional Korean Medicine (WKTKM) database, included records from 88,449 patients. It comprised more than 4 million condition records, approximately 10 million drug prescriptions, and over 10 million procedure records. Most laboratory results and medications were successfully mapped to existing standard concepts, while TKM-specific concepts were integrated using a newly developed terminology system called the KIOM codes.

conclusionThis proof-of-concept study demonstrates the technical feasibility of deterministic, rule-based transformation of EHR data from a TKM hospital into the OMOP CDM. By incorporating methodological extensions-including the temporary creation of KIOM terminology and the development of generic mapping rules for TKM-specific diagnostic and therapeutic elements-this study illustrates a practical baseline pathway for harmonizing traditional medicine data. While the WKTKM database establishes an important methodological foundation, extensive cross-institutional validation and centralized terminology governance are necessary prerequisites before such standardized datasets can be reliably deployed for large-scale, generalizable international collaborative research.

Indexed as

Databases, FactualElectronic Health RecordsMedicine, Korean TraditionalHumansRepublic of KoreaData standardizationElectronic health recordsEvidence-based medicineOMOP Common Data ModelTraditional Korean Medicine

Identifiers

PMID41840573
PMCPMC13104197

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.