Standardized Nursing Language in the Systematized Nomenclature of Medicine Clinical Terms
Der-Fa Lu, David Eichmann, Debra Konicek, HYUN TAE PARK, Prangtip Ucharattana, Connie Delaney
Abstract
Der-Fa Lu, David Eichmann, Debra Konicek, HYUN TAE PARK, Prangtip Ucharattana, Connie Delaney
Abstract
Many standardized healthcare languages have been mapped to the Systematized Nomenclature of Medicine Clinical Terms known as SNOMED CT, which was developed by the College of American Pathologists. This study describes a methodology for detecting misassigned concepts from source systems to SNOMED CT and presents the results of applying this methodology to a subset of concepts from two standardized nursing languages, the Nursing Interventions Classification and the Nursing Outcomes Classification. The methodology is based on comparing the knowledge representations of a set of nursing concepts between source systems (nursing languages) and SNOMED CT. If any nursing concept differs in knowledge representation in the target system compared with the source system, editorial misassignment of the concept was declared and recommendations for target system developers were made. In a total of 75 nursing concepts used to test this method, five misassigned concepts(6.6%) were found in SNOMED CT. This method can be used to validate other healthcare languages.
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Many standardized healthcare languages have been mapped to the Systematized Nomenclature of Medicine Clinical Terms known as SNOMED CT, which was developed by the College of American Pathologists. This study describes a methodology for detecting misassigned concepts from source systems to SNOMED CT and presents the results of applying this methodology to a subset of concepts from two standardized nursing languages, the Nursing Interventions Classification and the Nursing Outcomes Classification. The methodology is based on comparing the knowledge representations of a set of nursing concepts between source systems (nursing languages) and SNOMED CT. If any nursing concept differs in knowledge representation in the target system compared with the source system, editorial misassignment of the concept was declared and recommendations for target system developers were made. In a total of 75 nursing concepts used to test this method, five misassigned concepts(6.6%) were found in SNOMED CT. This method can be used to validate other healthcare languages.
Key concepts: SNOMED CT, Systematized Nomenclature of Medicine, Nursing Outcomes Classification, Nursing Interventions Classification, Nursing Minimum Data Set, Set (abstract data type), Computer science, Health care