SNOMED CT module-driven clinical archetype management

ObjectiveTo explore semantic search to improve management and user navigation in clinical archetype repositories.MethodsIn order to support semantic searches across archetypes, an automated method based on SNOMED CT modularization is implemented to transform clinical archetypes into SNOMED CT extracts. Concurrently, query terms are converted into SNOMED CT concepts using the search engine Lucene. Retrieval is then carried out by matching query concepts with the corresponding SNOMED CT segments.ResultsA test collection of the 16 clinical archetypes, including over 250 terms, and a subset of 55 clinical terms from two medical dictionaries, MediLexicon and MedlinePlus, were used to test our method. The keyword-based service supported by the OpenEHR repository offered us a benchmark to evaluate the enhancement of performance. In total, our approach reached 97.4% precision and 69.1% recall, providing a substantial improvement of recall (more than 70%) compared to the benchmark.ConclusionsExploiting medical domain knowledge from ontologies such as SNOMED CT may overcome some limitations of the keyword-based systems and thus improve the search experience of repository users. An automated approach based on ontology segmentation is an efficient and feasible way for supporting modeling, management and user navigation in clinical archetype repositories.

Figure optionsDownload high-quality image (121 K)Download as PowerPoint slideHighlights
► We focus on exploring semantic queries to improve clinical archetype management
► Semantic content of archetypes can be successfully represented via SNOMED segments.
► A method to automatically transform archetypes into SNOMED modules is proposed.
► Semantic search provides a substantial improvement compared to keyword-based search.
► Semantic search may help guide the development of new standard archetypes.