A Context-based Crowd Sourcing Tool for Quality Assurance of SNOMED CT

SNOMED CT is a prominent clinical terminology that is globally used by healthcare professionals to record patient data. As such, it is important that SNOMED CT be free from errors and inconsistencies so that they do not trickle down to applications that rely on it. Studies have shown that, owing to its large and complex nature as well as its regular revisions, errors and inconsistencies are inherent in SNOMED CT. However, auditing resources are limited, and manual auditing is cumbersome. This calls for computational quality assurance techniques to identify areas that have a high likelihood of modeling discrepancies. This study presents a visualization tool that is based on a context-based auditing methodology and can be used as a crowdsourcing application to allow SNOMED CT users from across the globe to comment on the modeling of a concept that is found by the underlying algorithm to be inconsistent. A manual auditing of a sample of 50 concept pairs using the visualization tool found 66% of the pairs to exhibit inconsistencies. Tools such as this and the feedback obtained from SNOMED CT users through collective collaboration can be used by the curators of SNOMED CT to further enhance the terminology.