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Record W2094101228 · doi:10.4212/cjhp.v64i4.1036

Avoiding Potential Medication Errors Associated with Non-intuitive Medication Abbreviations

2011· article· en· W2094101228 on OpenAlexaffvenue
Jonas Shultz, Lisa Strosher, Shaheen Nenshi Nathoo, Jim Manley

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2011
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsMedicineComprehensionOxycodoneCategorizationDrug administrationEmergency departmentMedical emergencyNursingPharmacologyInternal medicineOpioidComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Background: Pharmaceutical companies use a variety of abbreviations to denote short- and long-acting medications. Errors involving the administration of these medications are frequently reported.Objectives: To evaluate comprehension rates for abbreviations used to denote short- and long-acting medications and to evaluate whether changes to medication labels could reduce potential errors in the selection and administration of medications.Methods: In phase 1 of the study, nursing staff were asked to define 4 abbreviations and then to categorize them by release rate. In phase 2, a simulation exercise, nursing staff were asked if it would be appropriate to administer a medication illustrated in a photograph (oxycodone CR 5-mg blister pack) on the basis of information highlighted in a screen shot of an electronic medication administration record (order for oxycodone 5 mg). Three different presentations were used to identify the medication in the medication administration record and on the drug label.Results: In phase 1, 10 (28%) of 36 nursing staff members knew what all 4 abbreviations meant, and 14 (39%) correctly classified all 4 abbreviations as indicating a short- or a long-acting medication. In the simulation exercise (phase 2), labelling changes reduced the likelihood of a potential medication administration error.Conclusions: Most abbreviations used to indicate short- versus longacting medications were not correctly understood by study participants. Of more concern was the incorrect interpretation of some abbreviations as indicating the opposite release rate (e.g., “ER” interpreted as meaning “emergency release”, rather than “extended release”, with incorrect classification as a short-acting medication). This evaluation highlighted the potential consequences of using non-intuitive abbreviations to differentiate high-risk medications having different release rates.RÉSUMÉContexte : Les sociétés pharmaceutiques utilisent une panoplie d’abréviations pour désigner leurs médicaments à action brève ou prolongée. Or, on signale des erreurs fréquentes d’administration de ces médicaments.Objectifs : Évaluer le taux de compréhension des abréviations utilisées pour désigner les médicaments à action brève et à action prolongée et si des changements aux étiquettes de ces médicaments pourraient réduire les erreurs potentielles dans le choix et l’administration de ceux-ci.Méthodes : Dans la 1re phase de l’étude, on a demandé au personnel infirmier de définir quatre abréviations et de les classer par vitesse de libération. Dans la 2e phase, un exercice de simulation, on a demandé au personnel infirmier s’il serait approprié d’administrer le médicament qu’on leur présentait sur une photo (oxycodone CR [controlled release, c.-à-d. à libération contrôlée] à 5 mg en plaquettes alvéolées) en tenant compte de l’information surlignée dans une capture d’écran d’un registre électronique d’administration des médicaments (prescription d’oxycodone 5 mg). Trois présentations différentes ont été utilisées pour désigner le médicament dans le registre d’administration des médicaments et sur l’étiquette du médicament.Résultats : Dans la 1re phase de l’étude, 10 (28 %) des 36 membres du personnel infirmier connaissaient la signification des quatre abréviations et 14 (39 %) les ont correctement classées dans la catégorie action brève ou action prolongée. Dans l’exercice de simulation (2e phase), les changements à l’étiquette ont réduit la possibilité d’une erreur potentielle d’administration du médicament.Conclusions : La plupart des abréviations utilisées pour désigner les médicaments à action brève par rapport à ceux à action prolongée n’étaient pas bien comprises du personnel infirmier. Mais plus inquiétante était l’interprétation erronée de certaines abréviations à l’inverse de leur vitesse de libération (p. ex., ER interprétée comme étant emergency release (c.-à-d. à libération d’urgence) plutôt que extended release (c.-à-d. à libération prolongée) et incorrectement classée comme un médicament à action brève). Cette évaluation souligne les conséquences potentielles de l’utilisation d’abréviations non intuitives pour différencier lesmédicaments à risque élevé ayant des vitesses de libération différentes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.034
metaresearch head score (Gemma)0.204
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.204
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.355
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations5
Published2011
Admission routes2
Has abstractyes

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