Aggregating Heterogeneous Health Data through an Ontological Common Health Language

Interoperability between healthcare information systems is a challenge for the identification of important health-related events (through correlation of information across systems) and as a result for improving the patient care quality. Most of these systems are far from being interoperable and operate independently, whilst the value emerging from their exploitation is limited. While several techniques to address this challenge are based on medical standards and technologies, these techniques are not applicable to different scenarios, thus a holistic solution is needed. This paper focuses on the semantic interoperability of multiple electronic health records' (EHRs) – and their standards, proposing a multi-step generic semantic architecture that can be implemented simultaneously to different medical standards, for efficiently managing heterogeneous EHRs' data. The proposed architecture combines a mechanism for extracting Domain Specific information from classified EHRs' datasets, transforming them into a Common Health Language (CHL) through Ontologies, whereas unknown datasets are mapped and translated into CHL, using Ontology-Mapping techniques.