LAUSR

Electronic Health Records (EHRs) aggregate the entire patient’s data from different systems. Achieving interoperability for distributed EHR systems is expected to improve patient safety and care continuity, and therefore it improves the healthcare industry. However, achieving interoperability is challenging because of many standards, medical terminologies and ontologies, and different data formats. These formats make the integration of different systems an impossible process. If the hospital uses one standard to implement all of its medical systems, it will be no problem integrating them. However, hospitals usually depend on multiple standards and data formats to deliver different systems like hospital information systems, radiology information systems, laboratory information systems, etc. Semantic Web presents new technology for achieving EHRs interoperability. In this paper, we propose a novel ontological model to implement interoperability for distributed EHR environments. The proposed semantic ontology-based model can unify different EHRs data formats. In this study, We unify five different and popular healthcare data formats and standards. In addition, the framework could be extended straightforwardly to accept any other EHR data format. By implementing the proposed in real environments, we provide the physician with a single interface with a single terminology to query and interact with distributed healthcare systems that use different standards and data formats. This process is expected to help the physician to collect patient data from different systems quickly, completely, and correctly. The proposed ontological model has two stages. The first stage of the proposed converts each different input source to OWL ontology. In the second stage, it integrates all those ontologies into a merged crisp one. The integrated ontology includes 3753 axioms, 2606 logical axioms, 186 classes, 136 individuals, 126 datatype properties, and 257 object properties. We use SPARQL Protocol and RDF Query Language (SPARQL) and Description Logic (DL) queries to evaluate the output ontology. The obtained results ensure that the proposed framework helps physicians and specialists make a centralized point for all patients’ data. It could aggregate data with any heterogeneous structures with high precision.