Lexically suggest, logically define: quality assurance of the use of qualifiers and expected results of post-coordination in SNOMED CT.
A study of the use of common qualifiers in SNOMED CT definitions and the resulting classification was undertaken using combined lexical and semantic techniques. The accuracy of SNOMED authors in formulating definitions for pre-coordinated concepts was taken as a proxy for the expected accuracy of users formulating post-coordinated expressions. The study focused on "acute" and "chronic" as used within a module based on the UMLS CORE Problem List and using the pattern of SNOMED CT's definition Acute disease and Chronic disease. Scripts were used to identify potential candidate concepts whose names suggested that they should be classified as acute or chronic findings. The potential candidates were filtered by local clinical experts to eliminate spurious lexical matches. Scripts were then use to determine which of the filtered candidates were not classified under acute or chronic findings as expected. The results were that 28% and 20% of candidate chronic and acute concepts, respectively, were not so classified. Of these candidate misclassifications, the large majority occurred because "acute" and "chronic" are sometimes specified by qualifiers for clinical course and sometimes for morphology, a fact mentioned but not fully detailed in the User Guide distributed with the SNOMED releases. This heterogeneous representation reflects a potential conflict between common usage in patient care and SNOMED's origins in pathology. Other incidental findings included questions about the qualifier hierarchies themselves and issues with the underlying model for anatomy. The effort required for the study was kept modest by using module extraction and scripts, showing that such quality assurance of SNOMED is practical. The results of a preliminary study using proxy measures must be taken with caution. However, the high rate of misclassification indicates that, until the specifications for qualifiers are better documented and/or brought more in line with common clinical usage, anyone attempting to use post-coordination in SNOMED CT must be aware that there are significant pitfalls.
School of Computer Science, University of Manchester, Manchester M13 9PL, UK. rector@cs.manchester.ac.uk
Alan
A
Rector
Luigi
L
Iannone
D000465:Algorithms; D002908:Chronic Disease / Q000145:classification*; D016208:Databases, Factual; D008490:Medical Informatics; D011785:Quality Assurance, Health Care; D039061:Systematized Nomenclature of Medicine; D009626:Terminology as Topic