Algorithmic detection of inconsistent modeling among SNOMED CT concepts by combining lexical and structural indicators

SNOMED CT is important for clinical applications, such as Electronic Health Record (EHR) encoding. However, inconsistency in modeling its concepts may prevent SNOMED CT from providing proper support for clinical use. This study provides an effective methodology for locating inconsistently modeled SNOMED CT concepts. One can expect lexically similar concepts to be modeled similarly. Positional similarity sets, sets of lexically similar concepts having only one different word at the same position of their names, are introduced. Concepts in such sets have a higher likelihood of being unjustifiably inconsistently modeled. A technique to incorporate three structural indicators into the selected sets is provided to further improve the likelihood of finding inconsistently modeled concepts. An analysis of a sample of 50 such sets and for each of these three indicators is performed. The sample of positional similarity sets is found to have 18.6% inconsistent concepts. The use of structural indicators is shown to further improve the likelihood of finding inconsistently modeled concepts up to 41.6% with high statistical significance when compared to the previous sample of positional similarity sets. Positional similarity sets with different structural indicators are shown to help identify inconsistencies in concept modeling with high likelihood. Furthermore, such sets enable the comparison of concept modeling in the context of other lexically similar concepts, which enhances the effectiveness of corrections by auditors. Such quality assurance methods can be used to supplement IHTSDO´s own efforts in order to improve the quality of SNOMED CT.