• Korea Journal of Hospital Management
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• v.29 no.1
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• pp.1-18
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• 2024

Purposes: The Common Data Model(CDM) is very important for multi-institutional research. There are various domestic and international CDM construction cases to actively utilize it. In order to construct a CDM, different terms from each institution must be mapped to standard terms. Therefore, we intend to derive the importance and major issues of terminology mapping and propose a solution in CDM construction. Methodology/Approach: This study conducted terminology mapping between Electronic Health Record(EHR) and MOA CDM for constructing Medical Record Observation & Assessment for Drug Safety(MOA) CDM at Dankook University Hospital in 2022. In the process of terminology mapping, a CDM standard terminology process and method were developed and terminology mapping was performed by applying this. The constructions of CDM mapping terms proceeded in the order of diagnosis, drug, measurement, and treatment_procedure. Findings: We developed mapping guideline for CDM construction and used this for mapping. A total of 670,993 EHR data from Dankook University Hospital(January 1, 2013 to December 31, 2021) were mapped. In the case of diagnosis terminology, 19,413 were completely mapped. Drug terminology mapped 92.1% of 2,795. Measurement terminology mapped 94.5% of 7,254 cases. Treatment and procedure were mapped to 2,181 cases, which are the number of mapping targets. Practical Implications: This study found the importance of constructing MOA CDM for drug side effect monitoring and developed terminology mapping guideline. Our results would be useful for all future researchers who are conducting terminology mapping when constructing CDM.

• Korea Journal of Hospital Management
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• v.28 no.3
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• pp.1-14
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• 2023

Purposes: It is very important to establish a clinical data warehouse based on a common data model to offset the different data characteristics of each medical institution and for drug surveillance. This study attempted to establish a clinical data warehouse for Dankook university hospital for drug surveillance, and to derive the main items necessary for development. Methodology/Approach: This study extracted the electronic medical record data of Dankook university hospital tracked for 9 years from 2013 (2013.01.01. to 2021.12.31) to build a clinical data warehouse. The extracted data was converted into the Observational Medical Outcomes Partnership Common Data Model (Version 5.4). Data term mapping was performed using the electronic medical record data of Dankook university hospital and the standard term mapping guide. To verify the clinical data warehouse, the use of angiotensin receptor blockers and the incidence of liver toxicity were analyzed, and the results were compared with the analysis of hospital raw data. Findings: This study used a total of 670,933 data from electronic medical records for the Dankook university clinical data warehouse. Excluding the number of overlapping cases among the total number of cases, the target data was mapped into standard terms. Diagnosis (100% of total cases), drug (92.1%), and measurement (94.5%) were standardized. For treatment and surgery, the insurance EDI (electronic data interchange) code was used as it is. Extraction, conversion and loading were completed. R language-based conversion and loading software for the process was developed, and clinical data warehouse construction was completed through data verification. Practical Implications: In this study, a clinical data warehouse for Dankook university hospitals based on a common data model supporting drug surveillance research was established and verified. The results of this study provide guidelines for institutions that want to build a clinical data warehouse in the future by deriving key points necessary for building a clinical data warehouse.