This retrospective study used the Asan Medical Center Heart Registry database built on electronic medical records (EMR) from 2000 to 2016.
#DWS HEALTH CARE TYP O NC TRIAL#
To estimate the risk of recurrent cardiovascular events in a real-world population of very high-risk Korean patients with prior myocardial infarction (MI), ischemic stroke (IS), or symptomatic peripheral artery disease (sPAD), similar to the Further cardiovascular OUtcomes Research with proprotein convertase subtilisin–kexin type 9 Inhibition in subjects with Elevated Risk (FOURIER) trial population. However, these analysis results are still valid in Korea, and we have developed clinical research data warehouse based on these desiderata. The proposed characteristics desired in a CDW may have limited transfer value to organizations in other countries. Also, it should be a biomedical research platform for data repository use as well as data analysis. It should also include a simple query interface, an anonymized data review tool, and a data extraction tool. After reviewing the three CDW examples, we propose some key characteristics needed in a CDW for biomedical research.Ī CDW for research should include an honest broker system and an Institutional Review Board approval interface to comply with governmental regulations. Three examples of CDWs were reviewed: a multipurpose CDW in a hospital, a CDW for independent multi-institutional research, and a CDW for research use in an institution.
The characteristics necessary for the successful implementation and operation of a CDW for biomedical research have not clearly defined yet. Specifically, the use of CDWs for biomedical research has been relatively unsuccessful thus far. Due to the unique characteristics of clinical data, clinical data warehouses (CDWs) have not been successful so far.
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