Graph databases for openEHR clinical repositories

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.