Establishment of the Optimal Common Data Model Environment for EMR Data Considering the Computing Resources of Medical Institutions
Source abstract
Abstract
Electronic medical record (EMR) data vary between institutions. These data should be converted into a common data model (CDM) for multi-institutional joint research. To build the CDM, it is essential to integrate the EMR data of each hospital and load it according to the CDM model, considering the computing resources of each hospital. Accordingly, this study attempts to share experiences and recommend computing resource-allocation designs. Here, two types of servers were defined: combined and separated servers. In addition, three database (DB) setting types were selected: desktop application (DA), online transaction processing (OLTP), and data warehouse (DW). Scale, TPS, average latency, 90th percentile latency, and maximum latency were compared across various settings. Virtual memory (vmstat) and disk input/output (disk) statuses were also described. Transactions per second (TPS) decreased as the scale increased in all DB types; however, the average, 90th percentile and maximum latencies exhibited no tendency according to scale. When compared with the maximum number of clients (DA client = 5, OLTP clients = 20, DW clients = 10), the TPS, average latency, 90th percentile latency, and maximum latency values were highest in the order of OLTP, DW, and DA. In vmstat, the amount of memory used for the page cache field and free memory currently available for DA, OLTP, and DW were large compared to other fields. In the disk, DA, OLTP, and DW all recorded the largest value in the average size of write requests, followed by the largest number of write requests per second. In summary, this study presents recommendations for configuring CDM settings. The configuration must be tuned carefully, considering the hospital’s resources and environment, and the size of the database must consider concurrent client connections, architecture, and connections.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Innovation in Digital Healthcare Systems · Technology and Data Analysis · Electronic Health Records Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea
Department of Ophthalmology and Visual Science, College of Medicine, The Catholic University of Korea, Catholic University of Korea Yeouido Saint Mary’s Hospital, Seoul 06591, Korea
Funding
Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
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