Authors:
Jesús Bisbal
1
and
Damon Berry
2
Affiliations:
1
Universitat Pompeu Fabra (CISTIB) and CIBER-BBN, Spain
;
2
Dublin Institute of Technology, School of Electrical Systems Engineering, Ireland
Keyword(s):
Electronic health records, Two-level modelling, Archetypes, Interoperability, Semantic interoperability, Ontologies, Ontology alignment.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Collaboration and e-Services
;
Complex Systems Modeling and Simulation
;
Data Engineering
;
e-Business
;
Enterprise Information Systems
;
Health Information Systems
;
Integration/Interoperability
;
Interoperability
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Semantic Interoperability
;
Sensor Networks
;
Simulation and Modeling
;
Software Agents and Internet Computing
;
Software and Architectures
;
Symbolic Systems
Abstract:
Semantic interoperability between electronic health record systems and other information systems in the health domain implies agreement about the structure and the meaning of the information that is communicated. There are still a number of similar but different EHR system approaches. Some of the newer approaches adopt the two-layer model approach where a generic reference model is constrained by archetypes into valid clinical concepts which can be exchanged. The meaning of the concepts that are represented by an archetype can be conveyed by embedding codes from a commonly recognised terminology at appropriate points in the archetype. However, as the number of archetypes multiply it will become necessary to match archetypes from different sources to facilitate interoperability.
This paper describes an approach that supports semantic interoperability between heterogeneous two-level health information systems by identifying similarities between archetypes. The approach identifies rela
tionships between ontological terms which have been embedded in pairs of archetypes as a means of matching these terms. The matched terms can then in turn be used to identify similarities between archetypes. The limited contextual scope of an archetype simplifies this matching process.
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