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Interoperability among medication classification systems is known to be limited. We investigated the mapping of the Established Pharmacologic Classes (EPCs) to SNOMED CT. We compared lexical and instance-based methods to an expert-reviewed reference standard to evaluate contributions of these methods. Of the 543 EPCs, 284 had an equivalent SNOMED CT class, 205 were more specific, and 54 could not be mapped. Precision, recall, and F1 score were 0.416, 0.620, and 0.498 for lexical mapping and 0.616, 0.504, and 0.554 for instance-based mapping. Each automatic method has strengths, weaknesses, and unique contributions in mapping between medication classification systems. In our experience, it was beneficial to consider the mapping provided by both automated methods for identifying potential matches, gaps, inconsistencies, and opportunities for quality improvement between classifications. However, manual review by subject matter experts is still needed to select the most relevant mappings. [ABSTRACT FROM AUTHOR]

Value sets (VSs) used in electronic clinical quality measures are lists of codes from standard terminologies (“extensional” VSs), whose purpose (“intension”) is not always explicitly stated. We elicited the intension for the 09/01/2014 release of extensional medication value sets by comparison to drug classes from the October 2014 release of RxClass. Value sets matched drug classes if they shared common ingredients, as evidenced by Jaccard similarity score. We elicited the intension of 80 extensional value sets. The average Jaccard similarity was 0.65 for single classes and 0.80 for combination classes, with 34% (27/80) of the value sets having high similarity scores. Manual review by a pharmacist indicated 51% (41/80) of the drug classes selected as the best mapping for a value set matched the intension reflected in that value set name. This approach has the potential for facilitating the development and maintenance of medication value sets. [ABSTRACT FROM AUTHOR]

Objectives: To extend the coverage of phenotypes in SNOMED CT through post-coordination. Methods: We identify frequent modifiers in terms from the Human Phenotype Ontology (HPO), which we associate with templates for post-coordinated expressions in SNOMED CT. Results: We identified 176 modifiers, created 12 templates, and generated 1,617 post-coordinated expressions. Conclusions: Through this novel approach, we can increase the current number of mappings by 50%. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the extent to which pharmacoepidemiologic groupings are homogeneous in terms of clinical properties. Methods: In our analysis, we classified drug subgroups from the pharmacoepidemiologic Anatomical Therapeutic Chemical (ATC) classification system based on clinical drug properties. We established mappings from ATC fifth level drug entities to drug property annotations in the National Drug File Reference Terminology (NDF-RT), including therapeutic categories, mechanisms of action, and physiologic effects. Based on the annotations for the individual drugs we computed homogeneity scores for all ATC groups and analyzed their distribution. Conclusions: We found ATC groups to be generally homogeneous, more so for mechanisms of action, and physiologic effects than for therapeutic intent. However, only half of all ATC drugs can be analyzed with this approach, in part because of missing properties in NDF-RT. [ABSTRACT FROM AUTHOR]

The Value Set Authority Center (VSAC) at the National Library of Medicine (NLM) provides downloadable access to all official versions of vocabulary value sets contained in the Clinical Quality Measures (CQMs) used in the certification criteria for electronic health record systems ('Meaningful Use' incentive program). Each value set consists of the numerical values (codes) and human-readable names (descriptions), drawn from standard vocabularies such as LOINC, RxNorm and SNOMED CT®, that are used to define clinical data elements used in clinical quality measures (e.g., patients with diabetes, tricyclic antidepressants). The content of the VSAC will gradually expand to incorporate value sets for other use cases, as well as for new measures and updates to existing measures. [ABSTRACT FROM AUTHOR]