Comparison and Analysis of the National Drug Code Systems Among Drug Information Databases

Guo, Jeff J.; Diehl, Mark; Felkey, Bill G.; Gibson, Justin T.

doi:10.1177/009286159803200317

Cited by 10 publications

(7 citation statements)

References 1 publication

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“…In order to visualize common patterns in the identified clusters, we built a bipartite graph linking the compounds identified in each cluster to their mechanism of action, their physiological effect, and their established pharmacological class as reported by the Food and Drug Administration (FDA) National Drug Code Directory [52]. The diagram was drawn using Rawgraphs [53].…”

Section: Methodsmentioning

confidence: 99%

Predicting gastrointestinal drug effects using contextualized metabolic models

Guebila

Thiele

2019

PLoS Comput Biol

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Gastrointestinal side effects are among the most common classes of adverse reactions associated with orally absorbed drugs. These effects decrease patient compliance with the treatment and induce undesirable physiological effects. The prediction of drug action on the gut wall based on in vitro data solely can improve the safety of marketed drugs and first-inhuman trials of new chemical entities. We used publicly available data of drug-induced gene expression changes to build drug-specific small intestine epithelial cell metabolic models. The combination of measured in vitro gene expression and in silico predicted metabolic rates in the gut wall was used as features for a multilabel support vector machine to predict the occurrence of side effects. We showed that combining local gut wall-specific metabolism with gene expression performs better than gene expression alone, which indicates the role of small intestine metabolism in the development of adverse reactions. Furthermore, we reclassified FDA-labeled drugs with respect to their genetic and metabolic profiles to show hidden similarities between seemingly different drugs. The linkage of xenobiotics to their transcriptomic and metabolic profiles could take pharmacology far beyond the usual indication-based classifications.

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Section: Methodsmentioning

confidence: 99%

Predicting gastrointestinal drug effects using contextualized metabolic models

Guebila

Thiele

2019

PLoS Comput Biol

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“…Drug manufacturers are identified by the first five digits of the NDC. [19] We expect ΔGENERIC it to have a negative impact on ΔRRx it or ΔMS it .…”

Section: Modelsmentioning

confidence: 95%

Price and Market-Share Competition of Anti-Ulcer Gastric Medications in the Ohio Medicaid Market

Guo

Kelton²,

Pasquale

et al. 2004

International Journal of Pharmaceutical Medicine

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Objectives: Our objectives are (i) to determine whether an increase in competition among companies selling prescription anti-ulcer pharmaceuticals has an impact on drug costs for Ohio Medicaid; (ii) to separate competition's impact into its effect on generic drugs and its effect on branded drugs; and (iii) to distinguish between inter-drug (market) and intra-drug (generic) competition in their effects on drug-prescription reimbursements and utilisation.Methods: Based on Ohio Medicaid pharmacy claims data, we constructed quarterly prescription numbers as well as per-prescription reimbursement figures for each of the anti-ulcer gastric medications that Medicaid reimburses, from quarter 1 1997, through quarter 3 2002. We measured two dependent variables: the change in per-prescription reimbursement and the change in market share. Economic determinants included change in the producer price index, change in the number of brand-name drugs in the submarket (histamine H2-receptor antagonists, coating agent or proton pump inhibitors), direct-to-consumer advertising expenditures, and change in the number of generic manufacturers. An unbalanced panel data set was transformed to correct for serial correlation; variables were added to account specifically for intra-drug error correlation; and models were then estimated using ordinary least squares. Results:Both list prices and per-prescription reimbursements for branded drugs have increased over the last decade, despite the introduction of a number of new branded and generic drugs. Competition affects brand-name drugs through their market shares; branded-drug market shares have decreased as generic drug companies have entered the market (p < 0.001). Both average wholesale price and per-prescription Medicaid reimbursement for all generic drugs were significantly lower than for their brand-name counterparts, and decreased over time. For generic drugs, the rise in the number of generic companies had a significant negative impact on generic drug prescription costs (p = 0.047).Conclusions: Brand-name drug prices increase regardless of new drug entry in the market. Competition among generic companies leads to a decrease in prescription costs for generic drugs only. Ohio Medicaid benefits in the branded-drug market through a fall in branded drug utilisation, as generic competition rises. Ohio Medicaid can lower costs further by taking better advantage of between-brand competition, through the use of a formulary, and between-market competition, through the use of other cost-containment strategies. Fig. 2. The monthly average wholesale price per daily dose for histamine H2-receptor antagonists (H2RAs) and coating agents: 1987 to 2002.

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“…For example, if all sites encoded diagnoses as ICD10, then the reporting logic doesn't change within the network and allows for clever reuse of analytical efforts [18] as we will discuss in Section III. Not every dimension about a patient will be as obvious to code as diagnoses and in fact, as in the case of medications, many competing vocabularies and drug databases exist where historically discrepancies are a known issue [19]. In the West Virginia and Kentucky HALN, medications across the sites were encoded as either Cerner's Multum [20] or National Drug Codes (NDCs) [21]; mappings between Multum and NDCs exist in Cerner's Lexicon database [20].…”

Section: Data Modeling and Integrationmentioning

confidence: 99%

Using i2b2 to bootstrap rural health analytics and learning networks

Harris

Baus

Harper

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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We demonstrate that the open-source i2b2 (Informatics for Integrating Biology and the Bedside) data model can be used to bootstrap rural health analytics and learning networks. These networks promote communication and research initiatives by providing the infrastructure necessary for sharing data and insights across a group of healthcare and research partners. Data integration remains a crucial challenge in connecting rural healthcare sites with a common data sharing and learning network due to the lack of interoperability and standards within electronic health records. The i2b2 data model acts as a point of convergence for disparate data from multiple healthcare sites. A consistent and natural data model for healthcare data is essential for overcoming integration issues, but challenges such as those caused by weak data standardization must still be addressed. We describe our experience in the context of building the West Virginia/Kentucky Health Analytics and Learning Network, a collaborative, multi-state effort connecting rural healthcare sites.

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Comparison and Analysis of the National Drug Code Systems Among Drug Information Databases

Cited by 10 publications

References 1 publication

Predicting gastrointestinal drug effects using contextualized metabolic models

Predicting gastrointestinal drug effects using contextualized metabolic models

Price and Market-Share Competition of Anti-Ulcer Gastric Medications in the Ohio Medicaid Market

Using i2b2 to bootstrap rural health analytics and learning networks

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