Graph databases for openEHR clinical repositories. (28th November 2019)

• Record Type: Journal Article
• Authors: Helou, Samar El; Kobayashi, Shinji; Yamamoto, Goshiro; Kume, Naoto; Kondoh, Eiji; Hiragi, Shusuke; Okamoto, Kazuya; Tamura, Hiroshi; Kuroda, Tomohiro
• Is Part Of: International journal of computational science and engineering. Volume 20:Number 3(2019)
• Page Start: 281
• Page End: 298
• Publication Date: 2019-11-28
• Subjects: openEHR -- graph database -- electronic health records -- EHR -- database -- performance -- archetypes -- reference model -- EHR repository -- archetype-based storage -- query response time -- clinical repository; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

The archetype-based approach has now been adopted by major EHR interoperability standards. Soon, due to an increase in EHR adoption, more health data will be created and frequently accessed. Previous research shows that conventional persistence mechanisms such as relational and XML databases have scalability issues when storing and querying archetype-based datasets. Accordingly, we need to explore and evaluate new persistence strategies for archetype-based EHR repositories. To address the performance issues expected to occur with the increase of data, we proposed an approach using labelled property graph databases for implementing openEHR clinical repositories. We implemented the proposed approach using Neo4j and compared it to an object relational mapping (ORM) approach using Microsoft SQL server. We evaluated both approaches over a simulation of a pregnancy home-monitoring application in terms of required storage space and query response time. The results show that the proposed approach provides a better overall performance for clinical querying.