UNLABELLED: To explore the strengths and pitfalls of mapping structured EPR (electronic patient record) terms (OpenSDE) to SNOMED codes. METHODS: The OpenSDE model was developed for cardiovascular diseases in the context of the I4C project. We employed 35 patient records as references to adjust the model. We then performed automated and manual matches following the design of matching terms in the thesaurus of the resulting OpenSDE domain model to SNOMED concepts. Subsequently, we assessed what number of OpenSDE terms within the domain model can be matched to SNOMED concepts. RESULTS: The OpenSDE domain tree contains 3230 nodes, involving 689 unique terms (terms can be associated with more than one node in different parts of a domain model tree). After final manual work for the 689 tree terms, 616 resulted in a good match, 31 in a partial match, and 42 in no match. Of the good matches, 23 produced multiple matches. The matches were used to represent the mapping of each node in the domain tree by concatenation of the matching terms. CONCLUSIONS: Mapping predefined terms in OpenSDE domain models to SNOMED Clinical Terms (CT) concepts eliminates laborious mapping for each individual patient record. The assignment of SNOMED codes to OpenSDE tree nodes facilitates exchange, aggregation, and research involving patient data. The mapping will serve the construction of queries at higher semantic levels than explicitly modeled in an OpenSDE domain model. However, the usefulness of the mapping result depends on the completeness of the mapping to SNOMED CT, for which there is no gold standard.
UNLABELLED: To explore the strengths and pitfalls of mapping structured EPR (electronic patient record) terms (OpenSDE) to SNOMED codes. METHODS: The OpenSDE model was developed for cardiovascular diseases in the context of the I4C project. We employed 35 patient records as references to adjust the model. We then performed automated and manual matches following the design of matching terms in the thesaurus of the resulting OpenSDE domain model to SNOMED concepts. Subsequently, we assessed what number of OpenSDE terms within the domain model can be matched to SNOMED concepts. RESULTS: The OpenSDE domain tree contains 3230 nodes, involving 689 unique terms (terms can be associated with more than one node in different parts of a domain model tree). After final manual work for the 689 tree terms, 616 resulted in a good match, 31 in a partial match, and 42 in no match. Of the good matches, 23 produced multiple matches. The matches were used to represent the mapping of each node in the domain tree by concatenation of the matching terms. CONCLUSIONS: Mapping predefined terms in OpenSDE domain models to SNOMED Clinical Terms (CT) concepts eliminates laborious mapping for each individual patient record. The assignment of SNOMED codes to OpenSDE tree nodes facilitates exchange, aggregation, and research involving patient data. The mapping will serve the construction of queries at higher semantic levels than explicitly modeled in an OpenSDE domain model. However, the usefulness of the mapping result depends on the completeness of the mapping to SNOMED CT, for which there is no gold standard.
Authors: Sacha E Bleeker; Gerarda Derksen-Lubsen; Astrid M van Ginneken; Johan van der Lei; Henriëtte A Moll Journal: BMC Med Inform Decis Mak Date: 2006-07-13 Impact factor: 2.796