Evidence map›Paper›PMID 35773948›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2022

Comparing the variability of ingredient, strength, and dose form information from electronic prescriptions with RxNorm drug product descriptions.

Corey A Lester, Allen J Flynn, Vincent D Marshall, Scott Rochowiak, Brigid Rowell, James P Bagian

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

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0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Corey A LesterDepartment of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0001-8774-793X
Allen J FlynnDepartment of Learning Health Sciences, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Vincent D MarshallDepartment of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.
Scott RochowiakSurescripts LLC, Minneapolis, Minnesota, USA.
Brigid RowellDepartment of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.
James P BagianCenter for Risk Analysis Informed Decision Engineering, Department of Industrial and Operations Engineering, College of Engineering, University of Michigan, Ann Arbor, Michigan, USA.

Funding

Surescripts LLC
6 · The paper itself

Abstract

objectiveTo determine the variability of ingredient, strength, and dose form information from drug product descriptions in real-world electronic prescription (e-prescription) data. MATERIALS AND

methodsA sample of 10 399 324 e-prescriptions from 2019 to 2021 were obtained. Drug product descriptions were analyzed with a named entity extraction model and National Drug Codes (NDCs) were used to get RxNorm Concept Unique Identifiers (RxCUI) via RxNorm. The number of drug product description variants for each RxCUI was determined. Variants identified were compared to RxNorm to determine the extent of matching terminology used.

resultsA total of 353 002 unique pairs of drug product descriptions and NDCs were analyzed. The median (1st-3rd quartile) number of variants extracted for each standardized expression in RxNorm, was 3 (2-7) for ingredients, 4 (2-8) for strength, and 41 (11-122) for dosage forms. Of the pairs, 42.35% of ingredients (n = 328 032), 51.23% of strengths (n = 321 706), and 10.60% of dose forms (n = 326 653) used matching terminology, while 16.31%, 24.85%, and 13.05% contained nonmatching terminology, respectively. DISCUSSION: A wide variety of drug product descriptions makes it difficult to determine whether 2 drug product descriptions describe the same drug product (eg, using abbreviations to describe an active ingredient or using different units to represent a concentration). This results in patient safety risks that lead to incorrect drug products being ordered, dispensed, and used by patients. Implementation and use of standardized terminology may reduce these risks.

conclusionDrug product descriptions on real-world e-prescriptions exhibit large variation resulting in unnecessary ambiguity and potential patient safety risks.

Indexed as

Electronic PrescribingRxNormDrug PrescriptionsHumansVocabulary, Controlleddrug databaseselectronic prescribingmedication errorsnatural language processingRxNorm

Identifiers

PMID35773948
PMCPMC9382370

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.