National Drug File Reference Terminology Research Articles

Medical Knowledge Graph Completion Based on Word Embeddings

The aim of Medical Knowledge Graph Completion is to automatically predict one of three parts (head entity, relationship, and tail entity) in RDF triples from medical data, mainly text data. Following their introduction, the use of pretrained language models, such as Word2vec, BERT, and XLNET, to complete Medical Knowledge Graphs has become a popular research topic. The existing work focuses mainly on relationship completion and has rarely solved entities and related triples. In this paper, a framework to predict RDF triples for Medical Knowledge Graphs based on word embeddings (named PTMKG-WE) is proposed, for the specific use for the completion of entities and triples. The framework first formalizes existing samples for a given relationship from the Medical Knowledge Graph as prior knowledge. Second, it trains word embeddings from big medical data according to prior knowledge through Word2vec. Third, it can acquire candidate triples from word embeddings based on analogies from existing samples. In this framework, the paper proposes two strategies to improve the relation features. One is used to refine the relational semantics by clustering existing triple samples. Another is used to accurately embed the expression of the relationship through means of existing samples. These two strategies can be used separately (called PTMKG-WE-C and PTMKG-WE-M, respectively), and can also be superimposed (called PTMKG-WE-C-M) in the framework. Finally, in the current study, PubMed data and the National Drug File-Reference Terminology (NDF-RT) were collected, and a series of experiments was conducted. The experimental results show that the framework proposed in this paper and the two improvement strategies can be used to predict new triples for Medical Knowledge Graphs, when medical data are sufficiently abundant and the Knowledge Graph has appropriate prior knowledge. The two strategies designed to improve the relation features have a significant effect on the lifting precision, and the superposition effect becomes more obvious. Another conclusion is that, under the same parameter setting, the semantic precision of word embedding can be improved by extending the breadth and depth of data, and the precision of the prediction framework in this paper can be further improved in most cases. Thus, collecting and training big medical data is a viable method to learn more useful knowledge.

Simplifying the use of pharmacogenomics in clinical practice: Building the genomic prescribing system

BackgroundA barrier to the use of genomic information during prescribing is the limited number of software solutions that combine a user-friendly interface with complex medical data. We built and designed an online, secure, electronic custom interface termed the Genomic Prescribing System (GPS). MethodsActionable pharmacogenomic (PGx) information was reviewed, collected, and stored in the back-end of GPS to enable creation of customized drug- and variant-specific clinical decision support (CDS) summaries. The database architecture utilized the star schema to store information. Patient raw genomic data underwent transformation via custom-designed algorithms to enable gene and phenotype-level associations. Multiple external data sets (PubMed, The Systematized Nomenclature of Medicine (SNOMED), National Drug File – Reference Terminology (ND-FRT), and a publically-available PGx knowledgebase) were integrated to facilitate the delivery of patient, drug, disease, and genomic information. Institutional security infrastructure was leveraged to securely store patient genomic and clinical data on a HIPAA-compliant server farm. ResultsAs of May 17, 2016, the GPS back-end housed 257 CDS encompassing 112 genetic variants, 42 genes, and 46 PGx-actionable drugs. The GPS user interface presented patient-specific CDS alongside a recognizable traffic light symbol (green/yellow/red), denoting PGx risk for each genomic result. The number of traffic lights per visit increased with the corresponding increase in the number of available PGx-annotated drugs over time. An integrated drug and disease search functionality, links to primary literature sources, and potential alternative PGx drugs were indicated. The system, which was initially used as stand-alone CDS software within our clinical environment, was then integrated with the institutional electronic medical record for enhanced usability. There have been nearly 2000 logins in 43months since inception, with usage exceeding 56 logins per month and system up-times of 99.99%. For all patient-provider visits encompassing >3years of implementation, unique alert click-through rates corresponded to genomic risk: red lights clicked 100%, yellow lights 79%, green lights 43%. ConclusionsSuccessful deployment of GPS by combining complex data and recognizable iconography led to a tool that enabled point-of-care genomic delivery with high usability. Continued scalability and incorporation of additional clinical elements to be considered alongside PGx information could expand future impact.

Quality assurance of chemical ingredient classification for the National Drug File – Reference Terminology

The National Drug File – Reference Terminology (NDF-RT) is a large and complex drug terminology consisting of several classification hierarchies on top of an extensive collection of drug concepts. These hierarchies provide important information about clinical drugs, e.g., their chemical ingredients, mechanisms of action, dosage form and physiological effects. Within NDF-RT such information is represented using tens of thousands of roles connecting drugs to classifications. In previous studies, we have introduced various kinds of Abstraction Networks to summarize the content and structure of terminologies in order to facilitate their visual comprehension, and support quality assurance of terminologies.However, these previous kinds of Abstraction Networks are not appropriate for summarizing the NDF-RT classification hierarchies, due to its unique structure. In this paper, we present the novel Ingredient Abstraction Network (IAbN) to summarize, visualize and support the audit of NDF-RT’s Chemical Ingredients hierarchy and its associated drugs. A common theme in our quality assurance framework is to use characterizations of sets of concepts, revealed by the Abstraction Network structure, to capture concepts, the modeling of which is more complex than for other concepts. For the IAbN, we characterize drug ingredient concepts as more complex if they belong to IAbN groups with multiple parent groups. We show that such concepts have a statistically significantly higher rate of errors than a control sample and identify two especially common patterns of errors.

Querying the National Drug File Reference Terminology (NDFRT) to assign drugs to decision support categories.

The accurate categorization of drugs is a prerequisite for decision support rules. The manual process of creating drug classes can be laborious and error-prone. All 142 drug classes currently used at Regenstrief Institute for drug interaction alerts were extracted. These drug classes were replicated as fully-defined concepts in our local instance of the NDFRT knowledge base. The performance of these two strategies (manual classification vs. NDFRT-based queries) was compared, and the sensitivity and specificity of each was calculated. Compared to existing manual classifications, NDFRT-based queries made a greater number of correct class-drug assignments: 1528 vs. 1266. NDFRT queries have greater sensitivity (74.9% vs. 62.1%) to classify drugs. However, they have less specificity (85.6% vs. 99.8%). The NDFRT knowledge base shows promise for use in an automated strategy to improve the creation and update of drug classes. The chief disadvantage of our NDFRT-based approach was a greater number of false positive assignments due to the inclusion of non-systemic doseforms.

Standardized drug and pharmacological class network construction.

Dozens of drug terminologies and resources capture the drug and/or drug class information, ranging from their coverage and adequacy of representation. No transformative ways are available to link them together in a standard way, which hinders data integration and data representation for drug-related clinical and translational studies. In this paper, we introduce our preliminary work for building a standardized drug and drug class network that integrates multiple drug terminological resources, using Anatomical Therapeutic Chemical (ATC) and National Drug File Reference Terminology (NDF-RT) as network backbone, and expanding with RxNorm and Structured Product Label (SPL). The network consists of 39,728 drugs and drug classes. We calculated and compared structure similarity for each drug/drug class pair from ATC and NDF-RT, and analyzed constructed drug class network from chemical structure perspective.

Using description logics to evaluate the consistency of drug-class membership relations in NDF-RT.

BackgroundThe NDF-RT (National Drug File Reference Terminology) is an ontology, which describes drugs and their properties and supports computerized physician order entry systems. NDF-RT’s classes are mostly specified using only necessary conditions and lack sufficient conditions, making its use limited until recently, when asserted drug-class relations were added. The addition of these asserted drug-class relations presents an opportunity to compare them with drug-class relations that can be inferred using the properties of drugs and drug classes in NDF-RT.MethodsWe enriched NDF-RT’s drug-classes with sufficient conditions, added property equivalences, and then used an OWL reasoner to infer drug-class membership relations. We compared the inferred class relations to the recently added asserted relations derived from FDA Structured Product Labels.ResultsThe inferred and asserted relations only match in about 50% of the cases, due to incompleteness of the drug descriptions and quality issues in the class definitions.ConclusionsThis investigation quantifies and categorizes the disparities between asserted and inferred drug-class relations and illustrates issues with class definitions and drug descriptions. In addition, it serves as an example of the benefits DL can add to ontology development and evaluation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0007-3) contains supplementary material, which is available to authorized users.

Building integrated ontological knowledge structures with efficient approximation algorithms.

The integration of ontologies builds knowledge structures which brings new understanding on existing terminologies and their associations. With the steady increase in the number of ontologies, automatic integration of ontologies is preferable over manual solutions in many applications. However, available works on ontology integration are largely heuristic without guarantees on the quality of the integration results. In this work, we focus on the integration of ontologies with hierarchical structures. We identified optimal structures in this problem and proposed optimal and efficient approximation algorithms for integrating a pair of ontologies. Furthermore, we extend the basic problem to address the integration of a large number of ontologies, and correspondingly we proposed an efficient approximation algorithm for integrating multiple ontologies. The empirical study on both real ontologies and synthetic data demonstrates the effectiveness of our proposed approaches. In addition, the results of integration between gene ontology and National Drug File Reference Terminology suggest that our method provides a novel way to perform association studies between biomedical terms.

Standardizing adverse drug event reporting data.

BackgroundThe Adverse Event Reporting System (AERS) is an FDA database providing rich information on voluntary reports of adverse drug events (ADEs). Normalizing data in the AERS would improve the mining capacity of the AERS for drug safety signal detection and promote semantic interoperability between the AERS and other data sources. In this study, we normalize the AERS and build a publicly available normalized ADE data source. The drug information in the AERS is normalized to RxNorm, a standard terminology source for medication, using a natural language processing medication extraction tool, MedEx. Drug class information is then obtained from the National Drug File-Reference Terminology (NDF-RT) using a greedy algorithm. Adverse events are aggregated through mapping with the Preferred Term (PT) and System Organ Class (SOC) codes of Medical Dictionary for Regulatory Activities (MedDRA). The performance of MedEx-based annotation was evaluated and case studies were performed to demonstrate the usefulness of our approaches.ResultsOur study yields an aggregated knowledge-enhanced AERS data mining set (AERS-DM). In total, the AERS-DM contains 37,029,228 Drug-ADE records. Seventy-one percent (10,221/14,490) of normalized drug concepts in the AERS were classified to 9 classes in NDF-RT. The number of unique pairs is 4,639,613 between RxNorm concepts and MedDRA Preferred Term (PT) codes and 205,725 between RxNorm concepts and SOC codes after ADE aggregation.ConclusionsWe have built an open-source Drug-ADE knowledge resource with data being normalized and aggregated using standard biomedical ontologies. The data resource has the potential to assist the mining of ADE from AERS for the data mining research community.

Evaluating standard terminologies for encoding allergy information.

Allergy documentation and exchange are vital to ensuring patient safety. This study aims to analyze and compare various existing standard terminologies for representing allergy information. Five terminologies were identified, including the Systemized Nomenclature of Medical Clinical Terms (SNOMED CT), National Drug File-Reference Terminology (NDF-RT), Medication Dictionary for Regulatory Activities (MedDRA), Unique Ingredient Identifier (UNII), and RxNorm. A qualitative analysis was conducted to compare desirable characteristics of each terminology, including content coverage, concept orientation, formal definitions, multiple granularities, vocabulary structure, subset capability, and maintainability. A quantitative analysis was also performed to compare the content coverage of each terminology for (1) common food, drug, and environmental allergens and (2) descriptive concepts for common drug allergies, adverse reactions (AR), and no known allergies. Our qualitative results show that SNOMED CT fulfilled the greatest number of desirable characteristics, followed by NDF-RT, RxNorm, UNII, and MedDRA. Our quantitative results demonstrate that RxNorm had the highest concept coverage for representing drug allergens, followed by UNII, SNOMED CT, NDF-RT, and MedDRA. For food and environmental allergens, UNII demonstrated the highest concept coverage, followed by SNOMED CT. For representing descriptive allergy concepts and adverse reactions, SNOMED CT and NDF-RT showed the highest coverage. Only SNOMED CT was capable of representing unique concepts for encoding no known allergies. The proper terminology for encoding a patient's allergy is complex, as multiple elements need to be captured to form a fully structured clinical finding. Our results suggest that while gaps still exist, a combination of SNOMED CT and RxNorm can satisfy most criteria for encoding common allergies and provide sufficient content coverage.

Standardizing drug adverse event reporting data.

Normalizing data in the Adverse Event Reporting System (AERS), an FDA database, would improve the mining capacity of AERS for drug safety signal detection. In this study, we aim to normalize AERS and build a publicly available normalized Adverse drug events (ADE) data source.he drug information in AERS is normalized to RxNorm, a standard terminology source for medication. Drug class information is then obtained from the National Drug File - Reference Terminology (NDF-RT). Adverse drug events (ADE) are aggregated through mapping with the PT (Preferred Term) and SOC (System Organ Class) codes of MedDRA. Our study yields an aggregated knowledge-enhanced AERS data mining set (AERS-DM). The AERS-DM could provide more perspectives to mine AERS database for drug safety signal detection and could be used by research community in the data mining field.

Age and Sex Patterns of Drug Prescribing in a Defined American Population

ObjectiveTo describe the age and sex patterns of drug prescribing in Olmsted County, Minnesota. Patients and MethodsPopulation-based drug prescription records for the Olmsted County population in 2009 were obtained using the Rochester Epidemiology Project medical records linkage system (n=142,377). Drug prescriptions were classified using RxNorm codes and were grouped using the National Drug File–Reference Terminology. ResultsOverall, 68.1% of the population (n=96,953) received a prescription from at least 1 drug group, 51.6% (n=73,501) received prescriptions from 2 or more groups, and 21.2% (n=30,218) received prescriptions from 5 or more groups. The most commonly prescribed drug groups in the entire population were penicillins and β-lactam antimicrobials (17%; n=23,734), antidepressants (13%; n=18,028), opioid analgesics (12%; n=16,954), antilipemic agents (11%; n=16,082), and vaccines/toxoids (11%; n=15,918). However, prescribing patterns differed by age and sex. Vaccines/toxoids, penicillins and β-lactam antimicrobials, and antiasthmatic drugs were most commonly prescribed in persons younger than 19 years. Antidepressants and opioid analgesics were most commonly prescribed in young and middle-aged adults. Cardiovascular drugs were most commonly prescribed in older adults. Women received more prescriptions than men for several drug groups, in particular for antidepressants. For several drug groups, use increased with advancing age. ConclusionThis study provides valuable baseline information for future studies of drug utilization and drug-related outcomes in this population.

Health Care Transformation Through Collaboration on Open-Source Informatics Projects: Integrating a Medical Applications Platform, Research Data Repository, and Patient Summarization

BackgroundThe Strategic Health IT Advanced Research Projects (SHARP) program seeks to conquer well-understood challenges in medical informatics through breakthrough research. Two SHARP centers have found alignment in their methodological needs: (1) members of the National Center for Cognitive Informatics and Decision-making (NCCD) have developed knowledge bases to support problem-oriented summarizations of patient data, and (2) Substitutable Medical Apps, Reusable Technologies (SMART), which is a platform for reusable medical apps that can run on participating platforms connected to various electronic health records (EHR). Combining the work of these two centers will ensure wide dissemination of new methods for synthesized views of patient data. Informatics for Integrating Biology and the Bedside (i2b2) is an NIH-funded clinical research data repository platform in use at over 100 sites worldwide. By also working with a co-occurring initiative to SMART-enabling i2b2, we can confidently write one app that can be used extremely broadly.ObjectiveOur goal was to facilitate development of intuitive, problem-oriented views of the patient record using NCCD knowledge bases that would run in any EHR. To do this, we developed a collaboration between the two SHARPs and an NIH center, i2b2.MethodsFirst, we implemented collaborative tools to connect researchers at three institutions. Next, we developed a patient summarization app using the SMART platform and a previously validated NCCD problem-medication linkage knowledge base derived from the National Drug File-Reference Terminology (NDF-RT). Finally, to SMART-enable i2b2, we implemented two new Web service “cells” that expose the SMART application programming interface (API), and we made changes to the Web interface of i2b2 to host a “carousel” of SMART apps.ResultsWe deployed our SMART-based, NDF-RT-derived patient summarization app in this SMART-i2b2 container. It displays a problem-oriented view of medications and presents a line-graph display of laboratory results.ConclusionsThis summarization app can be run in any EHR environment that either supports SMART or runs SMART-enabled i2b2. This i2b2 “clinical bridge” demonstrates a pathway for reusable app development that does not require EHR vendors to immediately adopt the SMART API. Apps can be developed in SMART and run by clinicians in the i2b2 repository, reusing clinical data extracted from EHRs. This may encourage the adoption of SMART by supporting SMART app development until EHRs adopt the platform. It also allows a new variety of clinical SMART apps, fueled by the broad aggregation of data types available in research repositories. The app (including its knowledge base) and SMART-i2b2 are open-source and freely available for download.

Disambiguation of PharmGKB drug–disease relations with NDF-RT and SPL

PharmGKB is a leading resource of high quality pharmacogenomics data that provides information about how genetic variations modulate an individual’s response to drugs. PharmGKB contains information about genetic variations, pharmacokinetic and pharmacodynamic pathways, and the effect of variations on drug-related phenotypes. These relationships are represented using very general terms, however, and the precise semantic relationships among drugs, and diseases are not often captured. In this paper we develop a protocol to detect and disambiguate general clinical associations between drugs and diseases using more precise annotation terms from other data sources. PharmGKB provides very detailed clinical associations between genetic variants and drug response, including genotype-specific drug dosing guidelines, and this procedure will armGKB. The availability of more detailed data will help investigators to conduct more precise queries, such as finding particular diseases caused or treated by a specific drug.We first mapped drugs extracted from PharmGKB drug–disease relationships to those in the National Drug File Reference Terminology (NDF-RT) and to Structured Product Labels (SPLs). Specifically, we retrieved drug and disease role relationships describing and defining concepts according to their relationships with other concepts from NDF-RT. We also used the NCBO (National Center for Biomedical Ontology) annotator to annotate disease terms from the free text extracted from five SPL sections (indication, contraindication, ADE, precaution, and warning). Finally, we used the detailed drug and disease relationship information from NDF-RT and the SPLs to annotate and disambiguate the more general PharmGKB drug and disease associations.

Extracting drug indication information from structured product labels using natural language processing.

To extract drug indications from structured drug labels and represent the information using codes from standard medical terminologies. We used MetaMap and other publicly available resources to extract information from the indications section of drug labels. Drugs and indications were encoded by RxNorm and UMLS identifiers respectively. A sample was manually reviewed. We also compared the results with two independent information sources: National Drug File-Reference Terminology and the Semantic Medline project. A total of 6797 drug labels were processed, resulting in 19 473 unique drug-indication pairs. Manual review of 298 most frequently prescribed drugs by seven physicians showed a recall of 0.95 and precision of 0.77. Inter-rater agreement (Fleiss κ) was 0.713. The precision of the subset of results corroborated by Semantic Medline extractions increased to 0.93. Correlation of a patient's medical problems and drugs in an electronic health record has been used to improve data quality and reduce medication errors. Authoritative drug indication information is available from drug labels, but not in a format readily usable by computer applications. Our study shows that it is feasible to use publicly available natural language processing resources to extract drug indications from drug labels. The same method can be applied to other sections of the drug label-for example, adverse effects, contraindications. It is feasible to use publicly available natural language processing tools to extract indication information from freely available drug labels. Named entity recognition sources (eg, MetaMap) provide reasonable recall. Combination with other data sources provides higher precision.

Lessons learned from developing a drug evidence base to support pharmacovigilance.

This work identified challenges associated with extraction and representation of medication-related information from publicly available electronic sources. We gained direct observational experience through creating and evaluating the Drug Evidence Base (DEB), a repository of drug indications and adverse effects (ADEs), and supplemented this through literature review. We extracted DEB content from the National Drug File Reference Terminology, from aggregated MEDLINE co-occurrence data, and from the National Library of Medicine's DailyMed. To understand better the similarities, differences and problems with the content of DEB and the SIDER Side Effect Resource, and Vanderbilt's MEDI Indication Resource, we carried out statistical evaluations and human expert reviews. While DEB, SIDER, and MEDI often agreed on medication indications and side effects, cross-system shortcomings limit their current utility. The drug information resources we evaluated frequently employed multiple, disparate vaguely related UMLS concepts to represent a single specific clinical drug indication or adverse effect. Thus, evaluations comparing drug-indication and drug-ADE coverage for such resources will encounter substantial numbers of false negative and false positive matches. Furthermore, our review found that many indication and ADE relationships are too complex - logically and temporally - to represent within existing systems. To enhance applicability and utility, future drug information systems deriving indications and ADEs from public resources must represent clinical concepts uniformly and as precisely as possible. Future systems must also better represent the inherent complexity of indications and ADEs.

Profiling structured product labeling with NDF-RT and RxNorm

BackgroundStructured Product Labeling (SPL) is a document markup standard approved by Health Level Seven (HL7) and adopted by United States Food and Drug Administration (FDA) as a mechanism for exchanging drug product information. The SPL drug labels contain rich information about FDA approved clinical drugs. However, the lack of linkage to standard drug ontologies hinders their meaningful use. NDF-RT (National Drug File Reference Terminology) and NLM RxNorm as standard drug ontology were used to standardize and profile the product labels.MethodsIn this paper, we present a framework that intends to map SPL drug labels with existing drug ontologies: NDF-RT and RxNorm. We also applied existing categorical annotations from the drug ontologies to classify SPL drug labels into corresponding classes. We established the classification and relevant linkage for SPL drug labels using the following three approaches. First, we retrieved NDF-RT categorical information from the External Pharmacologic Class (EPC) indexing SPLs. Second, we used the RxNorm and NDF-RT mappings to classify and link SPLs with NDF-RT categories. Third, we profiled SPLs using RxNorm term type information. In the implementation process, we employed a Semantic Web technology framework, in which we stored the data sets from NDF-RT and SPLs into a RDF triple store, and executed SPARQL queries to retrieve data from customized SPARQL endpoints. Meanwhile, we imported RxNorm data into MySQL relational database.ResultsIn total, 96.0% SPL drug labels were mapped with NDF-RT categories whereas 97.0% SPL drug labels are linked to RxNorm codes. We found that the majority of SPL drug labels are mapped to chemical ingredient concepts in both drug ontologies whereas a relatively small portion of SPL drug labels are mapped to clinical drug concepts.ConclusionsThe profiling outcomes produced by this study would provide useful insights on meaningful use of FDA SPL drug labels in clinical applications through standard drug ontologies such as NDF-RT and RxNorm.

Normalized names for clinical drugs: RxNorm at 6 years.

In the 6 years since the National Library of Medicine began monthly releases of RxNorm, RxNorm has become a central resource for communicating about clinical drugs and supporting interoperation between drug vocabularies. Built on the idea of a normalized name for a medication at a given level of abstraction, RxNorm provides a set of names and relationships based on 11 different external source vocabularies. The standard model enables decision support to take place for a variety of uses at the appropriate level of abstraction. With the incorporation of National Drug File Reference Terminology (NDF-RT) from the Veterans Administration, even more sophisticated decision support has become possible. While related products such as RxTerms, RxNav, MyMedicationList, and MyRxPad have been recognized as helpful for various uses, tasks such as identifying exactly what is and is not on the market remain a challenge.

Further revamping VA's NDF-RT drug terminology for clinical research

Biomedical terminology and vocabulary standards (for the purposes of this correspondence, we use the terms ‘terminology,’ ‘vocabulary’ and ‘ontology’ interchangeably.) play an important role in enabling consistent, comparable, and meaningful sharing of data within and across institutional boundaries, as well as ensuring semantic interoperability. An important domain for developing standardized vocabularies is medications, where existing standards structure and organize approved drug products and ingredients by various characteristics or properties to support a multitude of clinical and epidemiological research questions across the spectrum of health and disease. Veteran Affairs' National Drug File-Reference Terminology (NDF-RT; see figure 1) is a Federal Medication-recommended standardized terminology resource encompassing medications, ingredients, and high-level drug classes for Chemical Structure (eg, Acetanilides), Mechanism of Action (eg, Prostaglandin Receptor Antagonists), Physiological Effect (eg, Decreased Prostaglandin Production), drug–disease relationship describing the Therapeutic Intent (eg, Pain), and Pharmacokinetics describing the mechanisms of absorption and distribution of an administered drug within a body (eg, Hepatic Metabolism). Additionally, NDF-RT contains two independent lists of drug classes: Legacy VA classes and External Pharmacologic classes, where the former simply provides a shallow hierarchy of ‘clinically oriented’ classes …

Analyzing categorical information in two publicly available drug terminologies: RxNorm and NDF-RT

The RxNorm and NDF-RT (National Drug File Reference Terminology) are a suite of terminology standards for clinical drugs designated for use in the US federal government systems for electronic exchange of clinical health information. Analyzing how different drug products described in these terminologies are categorized into drug classes will help in their better organization and classification of pharmaceutical information. Mappings between drug products in RxNorm and NDF-RT drug classes were extracted. Mappings were also extracted between drug products in RxNorm to five high-level NDF-RT categories: Chemical Structure; cellular or subcellular Mechanism of Action; organ-level or system-level Physiologic Effect; Therapeutic Intent; and Pharmacokinetics. Coverage for the mappings and the gaps were evaluated and analyzed algorithmically. Approximately 54% of RxNorm drug products (Semantic Clinical Drugs) were found not to have a correspondence in NDF-RT. Similarly, approximately 45% of drug products in NDF-RT are missing from RxNorm, most of which can be attributed to differences in dosage, strength, and route form. Approximately 81% of Chemical Structure classes, 42% of Mechanism of Action classes, 75% of Physiologic Effect classes, 76% of Therapeutic Intent classes, and 88% of Pharmacokinetics classes were also found not to have any RxNorm drug products classified under them. Finally, various issues regarding inconsistent mappings between drug concepts were identified in both terminologies. This investigation identified potential limitations of the existing classification systems and various issues in specification of correspondences between the concepts in RxNorm and NDF-RT. These proposals and methods provide the preliminary steps in addressing some of the requirements.

The pharmaceutical management system at Shade Tree Family Clinic: A medical student-run free clinic's experience

The Shade Tree Family Clinic (STFC) is a student-run free walk-in health clinic opened by Vanderbilt University medical students in October 2005 to address the acute and chronic health needs of the underinsured community in East Nashville. STFC founders decided that the clinic would provide complete medical care, including dispensing commonly prescribed medications at no charge to patients. After several months of managing the inventory in a log book, a medical student author created a Web-based pharmaceutical tracking system to manage the medication formulary. In the process, the authors found little literature available addressing the logistics of setting up an electronic pharmacy system. The system created uses the freely available RxNorm and US Department of Veterans Affairs National Drug File Reference Terminology databases for medication and classification data. Incorporation of these databases allows medical students to dispense and restock medications with ease. The system ensures accurate data entry, improves efficiency, and facilitates continuity of care at a clinic staffed by hundreds of different students and physicians. The STFC pharmaceutical tracking system has facilitated the acquisition and efficient management of medications and consequently has had a great impact on the success of STFC.