• 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.

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.106-114
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• 2009

In this study, we are analysing adverse drug events and proposing a technical structure of "adverse drug event surveillance system" using business intelligence technology, hoping that we can use the system commonly and actively. It is the recent trend to adopt both of electronic review and manual review process to surveil adverse drug events and this study construct CDW applying ETL in BI Technology. As the result of analysis, the data pool included 701 doctors who prescribed and 3059 patients(1528 male, 1531 female), of total 318,222 cases, 2,086cases(0.6%) were suspected as having adverse drug events. And the single type of T.bilirubin> 3mg/dL(ADE type-LabR0005) was the most common(548 among 2085 cases) within the framework of signals.

• Korean Journal of Clinical Laboratory Science
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• v.48 no.4
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• pp.348-354
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• 2016

Data stored in hospital information systems has a great potential to improve adequacy assessment and quality management. Moreover, an establishment of a data warehouse has been known to improve quality management and to offer help to clinicians. This study constructed a data mart that can be used to analyze antibiotic usage as a part of systematic and effective data analysis of infection control information. Metadata was designed by using the XML DTD method after selecting components and evaluation measures for infection control. OLAP-a multidimensional analysis tool-for antibiotic usage analysis was developed by building a data mart through modeling. Experimental data were obtained from data on antibiotic usage at a university hospital in Cheonan area for one month in July of 1997. The major components of infection control metadata were antibiotic resistance information, antibiotic usage information, infection information, laboratory test information, patient information, and infection related costs. Among them, a data mart was constructed by designing a database to apply antibiotic usage information to a star schema. In addition, OLAP was demonstrated by calculating the statistics of antibiotic usage for one month. This study reports the development of a data mart on antibiotic usage for infection control through the implementation of XML and OLAP techniques. Building a conceptual, structured data mart would allow for a rapid delivery and diverse analysis of infection control information.