Title: Graph databases for openEHR clinical repositories

Authors: Samar El Helou; Shinji Kobayashi; Goshiro Yamamoto; Naoto Kume; Eiji Kondoh; Shusuke Hiragi; Kazuya Okamoto; Hiroshi Tamura; Tomohiro Kuroda

Addresses: Department of Social Informatics, Graduate School of Informatics, Kyoto University, Japan ' Department of Electronic Health Record, Graduate School of Medicine, Kyoto University, Japan ' Division of Medical IT and Administration Planning, Kyoto University Hospital, Japan ' Department of Electronic Health Record, Graduate School of Medicine, Kyoto University, Japan ' Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, Japan ' Division of Medical IT and Administration Planning, Kyoto University Hospital, Japan ' Division of Medical IT and Administration Planning, Kyoto University Hospital, Japan ' Center for Innovative Research and Education in Data Science, Kyoto University, Japan ' Division of Medical IT and Administration Planning, Kyoto University Hospital, Japan

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.

Keywords: openEHR; graph database; electronic health records; EHR; database; performance; archetypes; reference model; EHR repository; archetype-based storage; query response time; clinical repository.

DOI: 10.1504/IJCSE.2019.103955

International Journal of Computational Science and Engineering, 2019 Vol.20 No.3, pp.281 - 298

Received: 06 Mar 2017
Accepted: 11 Oct 2017
Published online: 03 Dec 2019 *

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