title: Identifying inconsistencies in SNOMED CT problem lists using structural indicators.
abstract: The National Library of Medicine has published the CORE and the VA/KP problem lists to facilitate the usage of SNOMED CT for encoding diagnoses and clinical data of patients in electronic health records. Therefore, it is essential for the content of the problem lists to be as accurate and consistent as possible. This study assesses the effectiveness of using a concept's word length and number of parents, two structural indicators for measuring concept complexity, to identify inconsistencies with high probability. The method is able to isolate concepts with over 40% expected of being erroneous. A structural indicator for concepts which is able to identify 52% of the examined concepts as having errors in synonyms is also presented. The results demonstrate that the concepts in problem lists are not free of inconsistencies and further quality assurance is needed to improve the quality of these concepts.
authors.affiliation: Manhattan College, Riverdale, NY;
authors.forename: Ankur
authors.initials: A
authors.lastname: Agrawal
authors.affiliation: New Jersey Institute of Technology, Newark, NJ;
authors.forename: Yehoshua
authors.initials: Y
authors.lastname: Perl
authors.affiliation: Borough of Manhattan Community College, New York, NY;
authors.forename: Yan
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Halfpenny Technologies, Blue Bell, PA.
authors.forename: Gai
authors.initials: G
authors.lastname: Elhanan
authors.affiliation: New Jersey Institute of Technology, Newark, NJ;
authors.forename: Mei
authors.initials: M
authors.lastname: Liu
mesh_terms: D057286:Electronic Health Records; D008501:Medical Records, Problem-Oriented; D013358:Subject Headings; D039061:Systematized Nomenclature of Medicine