An Archetype Query Language interpreter into MongoDB: Managing NoSQL standardized Electronic Health Record extracts systems

Abstract

The fast development of today's healthcare and the need to extract new medical knowledge from exponentially-growing volumes of standardized Electronic Health Records data, as required by studies in Precision Medicine, brings up a challenge that may probably only be addressed using NoSQL DBMSs, due to the non-optimal performance of traditional relational DBMSs on standardized data; and these database systems operated by semantic archetype-based query languages, because of the expected generalized extension of standardized EHR systems. An AQL into MongoDB interpreter has been developed to its first version. It translates system-independent AQL queries posed on ISO/EN 13606 standardized EHR extracts into the NoSQL MongoDB query language. The new interpreter has the advantages of both the archetype-based system-independent AQL queries and the dual-model-based standardized EHR extracts stored on document-centric NoSQL DBMSs, such as MongoDB. AQL queries are independent of applications, programming languages and system environments due to the use of the dual model, but EHR extracts featuring this model are best persisted on document-based NoSQL databases. Consequently, the interpreter allows us to query standardized EHR extracts semantically, and also affording optimal performance.

Keywords: Databases; Electronic Health Records; Health information interoperability; Information storage and retrieval; Management information systems; Medical record systems.