Semantic Interoperability between heterogeneous healthcare systems requires improvements in the precision of meaning and understandability during the exchange of information; in particular, for Electronic Health Record (EHR) systems, it is essential to enable the consistent use of clinical terminologies.
SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) (http://www.nlm.nih.gov/research/umls/) is a compendium of three knowledge sources in the biomedical sciences; it may also be viewed as a comprehensive thesaurus and ontology of biomedical concepts One of them is the UMLS Metathesaurus, a large, multi-purpose and multilingual vocabulary database that currently comprises more than 1.5 millions biomedical terms from over 100 sources.
Although SNOMED CT (SCT) has gained adoption in the last years, as previously mentioned, its practical application for coding clinical information is hampered by its complexity and size. It contains over 300,000 concepts in the latest release, and over 1 million relationships between these concepts; therefore, the large scale of SNOMED CT is a major barrier to its progress while there is evidence that only a small fraction of its content is being used (Stroetmann et al., 2009).
Because of the very wide range of concepts, and the rate of change of those concepts (Spackman K., 2005), dealing with the entire set of terms contained in SNOMED CT is difficult. In particular, when clinicians use SNOMED browsers to code clinical documents, experiments have shown their performance to be disappointing (Chiang, et al., 2006). Moreover, these ontologies that sometimes contain more than a hundred thousand concepts (terms) are hard to maintain as changes can affect large parts of the model.