Scalable quality assurance for large SNOMED CT hierarchies using subject-based subtaxonomies

Standards terminologies may be large and complex, making their quality assurance challenging. Some terminology quality assurance (TQA) methodologies are based on abstraction networks (AbNs), compact terminology summaries. We have tested AbNs and the performance of related TQA methodologies on small...

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Veröffentlicht in:Journal of the American Medical Informatics Association : JAMIA Jg. 22; H. 3; S. 507
Hauptverfasser: Ochs, Christopher, Geller, James, Perl, Yehoshua, Chen, Yan, Xu, Junchuan, Min, Hua, Case, James T, Wei, Zhi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England 01.05.2015
Schlagworte:
Classification
Hemorrhage - classification
Humans
Neoplasms - classification
Quality Control
Systematized Nomenclature of Medicine
abstraction network
SNOMED CT
scalable quality assurance
subject-based terminology quality assurance
terminology quality assurance
standards quality assurance
ISSN:1527-974X, 1527-974X
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Zusammenfassung:Standards terminologies may be large and complex, making their quality assurance challenging. Some terminology quality assurance (TQA) methodologies are based on abstraction networks (AbNs), compact terminology summaries. We have tested AbNs and the performance of related TQA methodologies on small terminology hierarchies. However, some standards terminologies, for example, SNOMED, are composed of very large hierarchies. Scaling AbN TQA techniques to such hierarchies poses a significant challenge. We present a scalable subject-based approach for AbN TQA. An innovative technique is presented for scaling TQA by creating a new kind of subject-based AbN called a subtaxonomy for large hierarchies. New hypotheses about concentrations of erroneous concepts within the AbN are introduced to guide scalable TQA. We test the TQA methodology for a subject-based subtaxonomy for the Bleeding subhierarchy in SNOMED's large Clinical finding hierarchy. To test the error concentration hypotheses, three domain experts reviewed a sample of 300 concepts. A consensus-based evaluation identified 87 erroneous concepts. The subtaxonomy-based TQA methodology was shown to uncover statistically significantly more erroneous concepts when compared to a control sample. The scalability of TQA methodologies is a challenge for large standards systems like SNOMED. We demonstrated innovative subject-based TQA techniques by identifying groups of concepts with a higher likelihood of having errors within the subtaxonomy. Scalability is achieved by reviewing a large hierarchy by subject. An innovative methodology for scaling the derivation of AbNs and a TQA methodology was shown to perform successfully for the largest hierarchy of SNOMED.
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ISSN:1527-974X
1527-974X
DOI:10.1136/amiajnl-2014-003151