Toward Semantic Interoperability of Electronic Health Records

• URL: http://arxiv.org/abs/2401.11865v1
• Date: Mon, 22 Jan 2024 11:39:55 GMT
• Title: Toward Semantic Interoperability of Electronic Health Records
• Authors: Idoia Berges, Jes\'us Berm\'udez, Arantza Illarramendi
• Abstract summary: We present a proposal that smoothes out the way toward the achievement of semantic interoperability of electronic health records. The main contributions of our proposal are the following: first, it includes a canonical ontology whose EHR-related terms focus on semantic aspects. Second, it deals with modules that allow obtaining rich ontological representations of EHR information managed by proprietary models of health information systems. Third, it considers the necessary mapping axioms between ontological terms enhanced with so-called path mappings.
• Score: 0.05524804393257919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although the goal of achieving semantic interoperability of electronic health records (EHRs) is pursued by many researchers, it has not been accomplished yet. In this paper, we present a proposal that smoothes out the way toward the achievement of that goal. In particular, our study focuses on medical diagnoses statements. In summary, the main contributions of our ontology-based proposal are the following: first, it includes a canonical ontology whose EHR-related terms focus on semantic aspects. As a result, their descriptions are independent of languages and technology aspects used in different organizations to represent EHRs. Moreover, those terms are related to their corresponding codes in well-known medical terminologies. Second, it deals with modules that allow obtaining rich ontological representations of EHR information managed by proprietary models of health information systems. The features of one specific module are shown as reference. Third, it considers the necessary mapping axioms between ontological terms enhanced with so-called path mappings. This feature smoothes out structural differences between heterogeneous EHR representations, allowing proper alignment of information.

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