Unprincipled modeling decisions in large-domain ontologies, such as SNOMED CT, are problematic and might act as a barrier for their quality assurance and successful use in electronic health records. Most previous work has focused on clustering problematic concepts, which is helpful for quality control but faces difficulties in pinpointing the origin of those modeling problems. In this study, we examined the underlying structural patterns in SNOMED CT’s data model as such patterns directly reflect the modeling strategies of editors. Our results showed that 92% of all structural patterns found accumulated in the Procedure and Clinical finding sub-hierarchies, and pattern reuse was low; over 30% of patterns were only used once. A qualitative analysis of a sample of 50 such singleton patterns revealed modeling problems, including redundancy, omission, and inconsistency. The problems detected in the sample suggest that the analysis of structural patterns is a valuable technique for reveali)