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Description

Session Notes: This workshop does not offer MOC-II credit.

Large prescription datasets have become increasingly available to researchers (e.g., claims data from Medicare and private insurance companies, pharmacy data from clinical institutions, feeds from health information networks, such as Surescripts). Prescription data are generally recorded at a level that is very detailed (e.g., with National Drug Codes (NDCs) that include manufacturer and packaging information), and often need to be aggregated for meaningful clinical analysis (e.g., at the level of the ingredient or drug class).
Resources such as RxNorm, the standard terminology for drugs in the U.S. developed by the National Library of Medicine, can facilitate the mapping of NDCs to RxNorm concepts for clinical drugs. RxNorm also supports aggregation by linking clinical drug products to their ingredients, and to drug classes from ATC, MED-RT and DailyMed. The RxNorm and RxClass application programming interfaces (APIs) and companion browsers facilitate the use of RxNorm for aggregation purposes. Additionally, features have recently been added to the drug APIs to facilitate the interpretation of obsolete drug identifiers often found in clinical data warehouses.
The first part of this tutorial presents basic information about drug datasets and resources for analyzing them, with emphasis on RxNorm. The audience will be invited to participate (active exploration of RxNorm through RxNav and RxClass; follow-along activities with RxMix).
In the second part, we demonstrate an application of these resources to common use cases, including the comparison of prescribed vs. defined daily doses for drugs and the identification of potentially inappropriate medications (e.g., during pregnancy, for the elderly). Finally, we present the experience of the OHDSI (Observational Health Data Sciences and Informatics) community in integrating various kinds of drug data in a large clinical data warehouse compliant with the OMOP (Observational Medical Outcomes Partnership) clinical data model, and we address issues in integrating drugs from different countries.

Learning Objective 1: Understand issues in analyzing drug prescription datasets, including mapping NDC identifiers to standard terminologies, and aggregating fine-grained drug codes into drug classes

Learning Objective 2: List specific resources available through RxNav and its companion application programming interfaces (APIs) to support drug analytics.

Authors:

Olivier Bodenreider (Presenter)
NLM

Vojtech Huser (Presenter)
NLM

Christian Reich (Presenter)
QuintilesIMS

Presentation Materials:

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