Identifying problematic concepts in SNOMED CT using a lexical approach.
SNOMED CT (SCT) has been endorsed as a premier clinical terminology by many organizations with a perceived use within electronic health records and clinical information systems. However, there are indications that, at the moment, SCT is not optimally structured for its intended use by healthcare practitioners. A study is conducted to investigate the extent of inconsistencies among the concepts in SCT. A group auditing technique to improve the quality of SCT is introduced that can help identify problematic concepts with a high probability. Positional similarity sets are defined, which are groups of concepts that are lexically similar and the position of the differing word in the fully specified name of the concepts of a set that correspond to each other. A manual auditing of a sample of such sets found 38% of the sets exhibiting one or more inconsistent concepts. Group auditing techniques such as this can thus be very helpful to assure the quality of SCT, which will help expedite its adoption as a reference terminology for clinical purposes.
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
Ankur
A
Agrawal
Yehoshua
Y
Perl
Gai
G
Elhanan
D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D062527:Meaningful Use; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D011785:Quality Assurance, Health Care / Q000379:methods*; D012660:Semantics; D039061:Systematized Nomenclature of Medicine