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| Construction Method of Clinical Medical Data Warehouse Based on openEHR |
1 College of Business Administration, Zhejiang University of Technology, Hangzhou 310023, China
2 College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China |
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Abstract Complexity and continuous evolution of healthcare information is the biggest challenge for healthcare information systems, and two-level information model from openEHR can facilitate healthcare information systems to adapt to the change of healthcare information requirements. However, the medical information system based on openEHR two-level modeling displays low performance when facing with specific operational demands such as a large number of data query and complex and mutative query conditions. This is mainly related to the fundamental data storage model. The multidimensional data model of data warehouse has the advantage of high-performance query, the data mart that is established for fulfilling users′ particular demand can increase the particular query speed. Due to the particularity of the medical information system that based on openEHR two-level modeling, the outcomes for traditional data warehouse construction method include low users′ participation and time consuming. In this work, a mapping method between template and multidimensional data model of data warehouse based on openEHR two-level modeling was proposed to solve this problem, aiming to achieve the configuration of multidimensional data model by users, and use the mapping path to accelerate the implementation of ETL. This research provided one kind of dirigible and scalable data warehouse construction method reflecting user′s requirements for medical industry. We also validated this method in performance. Results showed that the data mart that was built based on this method was 5.6 times faster than the database which was generated based on openEHR two-level modeling and was 0.97 times than the data mart that was built by traditional method when querying 329 results in 10861522 records.
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