Abstract
SNOMED CT has continued to expand its adoption as a comprehensive clinical terminology. In 2013, the US government mandated SNOMED CT as a requirement for Stage 2 meaningful use criteria for Electronic Health Records. Studies have, however, identified inconsistencies in the content of SNOMED CT that may lessen its effectiveness when used for encoding patient data. Auditing thus becomes an integral part of SNOMED CT's evolution. However, manually auditing every concept in SNOMED CT can be a humongous and a tedious task. Algorithmic techniques are needed to identify concepts with inconsistencies. This study uses a lexical method to identify concepts with high likelihood of inconsistencies in their structural modeling. A sample of concepts derived by using this technique is manually audited. Over a third (36.7%) of the sample concepts are found to exhibit one or more inconsistencies in their structural modeling. Such technique can extend SNOMED International's own effort to maintain the quality of the content of SNOMED CT.