Next-generation phenotyping using UMLS and Meaningful Use ontologies: SNOMED CT, RxNorm, and LOINC
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SNOMED CT, LOINC, and RxNorm, fuelled by the Meaningful Use legislation, are poised to become the cornerstone of U.S. health information interchange. SNOMED CT is one of the most comprehensive, ...
SNOMED CT, LOINC, and RxNorm, fuelled by the Meaningful Use legislation, are poised to become the cornerstone of U.S. health information interchange. SNOMED CT is one of the most comprehensive, multilingual medical terminologies in the world. LOINC is a universal standard for identifying laboratory observations. RxNorm is a standardized nomenclature for generic and branded drugs. All three are integrated within the Unified Medical Language System (UMLS) maintained by the U.S. National Library of Medicine.
While physicians rarely have to deal with clinical terminologies directly, these are indispensable for data querying, validation and reconciliation. The Clinical Informatics team at the Medical College of Wisconsin has developed ClinMiner (https://clinminer.hmgc.mcw.edu), a clinical research portal for clinical and diagnostic information on patients in genetics clinics and clinical sequencing programs, as well as other clinical research projects. ClinMiner is a larger system that incorporates data entry forms, patient reports, advanced querying, export and data visualization. Data for the system consists of many clinical and referral documents the patients have accumulated throughout their clinic and diagnostic histories, and are standardized through the three Meaningful Use ontologies: SNOMED CT, RxNorm and LOINC; integrated into a single UMLS perspective that allows for seamless and dynamic translation between the annotating sources, as well as provides a consolidated view of the underlying patient data.
This approach is unique in integrating all three terminologies into a single workflow of a clinical application, and in fact is not limited to Meaningful Use, as any terminology integrated within the UMLS can be used to annotate, visualize, and query data. This is of particular significance for reintegrating legacy clinical information, for example, billing data annotated with ICD-9 codes in the process of transitioning to ICD-10. Most importantly, as large resources such as SNOMED CT and the UMLS often remain underused due to their sheer size and complexity, ClinMiner demonstrates that the additional effort is well worth it.
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