Identifying inconsistencies in SNOMED CT problem lists using structural indicators.

Ankur Agrawal, Yehoshua Perl, Yan Chen, Gai Elhanan, Mei Liu

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)17-26
Number of pages10
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Medicine


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