title: Ontology content patterns as bridge for the semantic representation of clinical information.
abstract: OBJECTIVE
  Semantic interoperability of the Electronic Health Record (EHR) requires a rigorous and precise modelling of clinical information. Our objective is to facilitate the representation of clinical facts based on formal principles.
  
  
  METHODS
  We here explore the potential of ontology content patterns, which are grounded on a formal and semantically rich ontology model and can be specialised and composed.
  
  
  RESULTS
  We describe and apply two content patterns for the representation of data on tobacco use, rendered according to two heterogeneous models, represented in openEHR and in HL7 CDA. Finally, we provide some query exemplars that demonstrate a data interoperability use case.
  
  
  CONCLUSIONS
  The use of ontology content patterns facilitate the semantic representation of clinical information and therefore improve their semantic interoperability. There are open issues such as the scalability and performance of the approach if a logic-based language is used. Implementation decisions might determine the final degree of semantic interoperability, influenced by the state of the art of the semantic technologies.
authors.affiliation: Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz , Austria.
authors.forename: C
authors.initials: C
authors.lastname: Martínez-Costa
authors.affiliation: Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz , Austria ; Institute of Medical Biometry and Medical Informatics, Freiburg University Medical Center , Germany.
authors.forename: S
authors.initials: S
authors.lastname: Schulz
mesh_terms: D064229:Biological Ontologies; D057286:Electronic Health Records / Q000592:standards*; D057208:Health Level Seven / Q000592:standards*; D016247:Information Storage and Retrieval / Q000592:standards*; D038622:Internationality; D008498:Medical Record Linkage / Q000592:standards*; D009323:Natural Language Processing; D017410:Practice Guidelines as Topic; D012660:Semantics
keywords: Electronic Health Records; HL7 CDA; Knowledge Representation; OpenEHR; SNOMED CT; Semantics