Interoperability of Medication Classification Systems: Lessons Learned Mapping Established Pharmacologic Classes (EPCs) to SNOMED CT.

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.

Categorical information in pharmaceutical terminologies.

Drug information sources use category labels to assist in navigating and organizing information. Some category labels describe drugs from multiple perspectives (e.g., both structure and function). The National Drug File - Reference Terminology (NDF RT) is a drug information source that augments a "legacy" categorization system with a formal reference model specifying Chemical Structure, Cellular or Sub-Cellular Mechanism of Action, Organ- or System-Level Physiological Effect, and Therapeutic Intent categories. We examined drug category names from three sources to better understand their information content and evaluate NDF RT's semantic coverage. On average, category names contain more than 1.5 attributes. NDF RT's reference model covers more than 76% of the information identified in drug category labels. A new NDF RT reference axis of drug formulations could improve NDF RT's coverage to 85%. The distinction between Physiological Effect and Therapeutic Intent, prompted many questions among category reviewers, suggesting that further clarification of these reference concepts is required. Careful review of existing categorization schemes may guide structured terminology and ontology development efforts toward greater fidelity to deployed information sources.

U.S. Department of Veterans Affairs Enterprise Reference Terminology strategic overview.

The Veterans Health Affairs (VHA) branch of the Department of Veterans Affairs has undertaken an Enterprise Reference Terminology (ERT). VHA, arguably the largest integrated healthcare provider in the United States, has completely computerized virtually all clinical transactions, including physician orders and documentation. The VA is now integrating its clinical records across hundreds of sites of care by means of a Health Data Repository (HDR) project. ERT has been designed to provide a terminology development environment, terminology services, and maintenance services for the clinical and business content in HDR and other VHA applications. Drug, laboratory observations, and clinical document title files have been developed, and the ERT will encompass all HDR domains by 2008. Commercial tools are used to host the VHA's ERT terminology development and server environments. We will select and adopt both open-source and licensable terminology systems to provide ERT content, as well as reuse existing VA-specific terminology content.

VA National Drug File Reference Terminology: a cross-institutional content coverage study.

BACKGROUND: Content coverage studies provide valuable information to potential users of terminologies. We detail the VA National Drug File Reference Terminology's (NDF-RT) ability to represent dictated medication list phrases from the Mayo Clinic. NDF-RT is a description logic-based resource created to support clinical operations at one of the largest healthcare providers in the US. METHODS: Medication list phrases were extracted from dictated patient notes from the Mayo Clinic. Algorithmic mappings to NDF-RT using the SmartAccess Vocabulary Server (SAVS) were presented to two non-VA physicians. The physicians used a terminology browser to determine the accuracy of the algorithmic mapping and the content coverage of NDF-RT. RESULTS: The 509 extracted documents on 300 patients contained 847 medication concepts in medication lists. NDF-RT covered 97.8% of concepts. Of the 18 phrases that NDF-RT did not represent, 10 were for OTC's and food supplements, 5 were for prescription medications, and 3 were missing synonyms. The SAVS engine properly mapped 773 of 810 phrases with an overall sensitivity (precision) was 95.4% and positive predictive value (recall) of 99.9%. CONCLUSIONS: This study demonstrates that NDF-RT has more general utility than its initial design parameters dictated

Adequacy of representation of the National Drug File Reference Terminology Physiologic Effects reference hierarchy for commonly prescribed medications.

The National Drug File Reference Terminology contains a novel reference hierarchy to describe physiologic effects (PE) of drugs. The PE reference hierarchy contains 1697 concepts arranged into two broad categories; organ specific and generalized systemic effects. This investigation evaluated the appropriateness of the PE concepts for classifying a random selection of commonly prescribed medications. Ten physician reviewers classified the physiologic effects of ten drugs and rated the accuracy of the selected term. Inter reviewer agreement, overall confidence, and concept frequencies were assessed and were correlated with the complexity of the drug's known physiologic effects. In general, agreement between reviewers was fair to moderate (kappa 0.08-0.49). The physiologic effects modeled became more disperse with drugs having and inducing multiple physiologic processes. Complete modeling of all physiologic effects was limited by reviewers focusing on different physiologic processes. The reviewers were generally comfortable with the accuracy of the concepts selected. Overall, the PE reference hierarchy was useful for physician reviewers classifying the physiologic effects of drugs. Ongoing evolution of the PE reference hierarchy as it evolves should take into account the experiences of our reviewers.

Initializing the VA medication reference terminology using UMLS metathesaurus co-occurrences.

We developed and evaluated a UMLS Metathesaurus Co-occurrence mining algorithm to connect medications and diseases they may treat. Based on 16 years of co-occurrence data, we created 977 candidate drug-disease pairs for a sample of 100 ingredients (50 commonly prescribed and 50 selected at random). Our evaluation showed that more than 80% of the candidate drug-disease pairs were rated "APPROPRIATE" by physician raters. Additionally, there was a highly significant correlation between the overall frequency of citation and the likelihood that the connection was rated "APPROPRIATE." The drug-disease pairs were used to initialize term definitions in an ongoing effort to build a medication reference terminology for the Veterans Health Administration. Co-occurrence mining is a valuable technique for initializing term definitions in a large-scale reference terminology creation project.

A semantic normal form for clinical drugs in the UMLS: early experiences with the VANDF.

A semantic normal form (SNF) for a clinical drug, designed to represent the meaning of an expression typically seen in a practitioner's medication order, has been developed and is being created in the UMLS Metathesaurus. The long term goal is to establish a relationship for every concept in the Metathesaurus with semantic type "clinical drug" with one or more of these semantic normal forms. First steps have been taken using the Veterans Administration National Drug File (VANDF). 70% of the entries in the VANDF could be parsed algorithmically into the SNF. Next steps include parsing other drug vocabularies included in the UMLS Metathesaurus and performing human review of the parsed vocabularies. After machine parsed forms have been merged in the Metathesaurus Information Database (MID), editors will be able to edit matched SNFs for accuracy and establish relationships and relationship attributes with other clinical drug concepts.