Abstract
BackgroundThe objective of this research is to compare the relational and non-relational (NoSQL) database systems approaches in order to store, recover, query and persist standardized medical information in the form of ISO/EN 13606 normalized Electronic Health Record XML extracts, both in isolation and concurrently. NoSQL database systems have recently attracted much attention, but few studies in the literature address their direct comparison with relational databases when applied to build the persistence layer of a standardized medical information system.MethodsOne relational and two NoSQL databases (one document-based and one native XML database) of three different sizes have been created in order to evaluate and compare the response times (algorithmic complexity) of six different complexity growing queries, which have been performed on them. Similar appropriate results available in the literature have also been considered.ResultsRelational and non-relational NoSQL database systems show almost linear algorithmic complexity query execution. However, they show very different linear slopes, the former being much steeper than the two latter. Document-based NoSQL databases perform better in concurrency than in isolation, and also better than relational databases in concurrency.ConclusionNon-relational NoSQL databases seem to be more appropriate than standard relational SQL databases when database size is extremely high (secondary use, research applications). Document-based NoSQL databases perform in general better than native XML NoSQL databases. EHR extracts visualization and edition are also document-based tasks more appropriate to NoSQL database systems. However, the appropriate database solution much depends on each particular situation and specific problem.
Highlights
The objective of this research is to compare the relational and non-relational (NoSQL) database systems approaches in order to store, recover, query and persist standardized medical information in the form of ISO/EN 13606 normalized Electronic Health Record eXtensible Mark-up Language (XML) extracts, both in isolation and concurrently
The dual model used by standardized Electronic Health Record (EHR) documents requires the organization of the information following a specific structure, and medical knowledge must adopt the structure constrained by the archetypes, i.e. special data structures holding knowledge [1, 2, 6,7,8]
The openEHR node + path EHR extracts database system We provide a short introduction to openEHR’s Node + Path even though we have not used it, and later we will show some results. openEHR has developed an optimization over the Object Relational Mapping (ORM) relational methodology based on the Entity Attribute Value (EAV) model [42]
Summary
The objective of this research is to compare the relational and non-relational (NoSQL) database systems approaches in order to store, recover, query and persist standardized medical information in the form of ISO/EN 13606 normalized Electronic Health Record XML extracts, both in isolation and concurrently. Creating, maintaining and communicating EHR documents in those systems is not at all straightforward This is due to several factors affecting technical, syntactic and semantic interoperability between information systems, including the inevitable rapid change and evolution of medical knowledge. The ISO/EN 13606 and openEHR standards define a dual model that separates information and knowledge into two levels of abstraction, thereby guaranteeing semantic interoperability between systems operating EHR documents [5]. The special nature of medical knowledge that requires the separation into two levels of the dual model can have a profound effect on the way information in EHR documents is structured and how it is stored logically and physically in a database management system. The dual model used by standardized EHR documents requires the organization of the information following a specific structure, and medical knowledge must adopt the structure constrained by the archetypes, i.e. special data structures holding knowledge [1, 2, 6,7,8]
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