Measuring drug name similarity to prioritise the application of tall-man lettering in a computerised pharmacy dispensing system
Notice bibliographique
Résumé
Abstract Introduction Medicine name similarity is a contributory factor to medication errors.1 Published lists exists highlighting medicine pairs that are easily confused; locally a Look-Alike-Sound-Alike (LASA) list has expanded over time with no formal system of triage. A multipronged approach is required to address LASA risks. Tall-Man Lettering (TML) is one intervention that uses uppercase lettering for the dissimilarities in look-alike drug names to alert staff to the risk of error.1 For greatest impact, it should be reserved for pairs with the highest risk for patient safety. Aim This study aimed to use Levenshtein Distance (LD), Bigram (Bi) and Trigram (Tri) methods to prioritise medicine pairs for TML in the Pharmacy dispensing system. Objectives were to: Produce a comprehensive list of medicines pairs; Establish normalised thresholds from LD, Bi and Tri to prioritise medicine pairs for TML; and, measure drug name similarity using validated software2 applying LD, Bi and Tri to medication pairs as a method of triage for orthographic assessment. Methods Approval was obtained by the Trust Pharmacy Research Committee. The need for ethical submission was waived. A LASA list was developed combining medicine name pairs from National Pharmacy Association list3, historical local list and internal incidents where medicine name confusion was cited. Duplicated, branded and non-stock pairs were excluded. A literature search was undertaken to identify published thresholds for accuracy and sensitivity of the methods in the measure of medicine name similarity. LD measures the minimum number of edit operations needed to transform one string into another; Bi and Tri measures the frequency in which two/three similar sequential strings appear within a medicine name respectively. Two assessors independently entered medicine pairs through a validated computer program2 applying LD, Bi and Tri to measure orthographic similarity. Normalised computed similarity scores (between 0-1 where higher values represent increased drug similarity) were collated on Microsoft excel for comparison against thresholds. Results Two-hundred and twelve medicine pairs were identified for review. The literature defined Bi and Tri thresholds at ≥0.3 and ≥0.1 respectively; in absence of this for LD, in-house thresholds were assessed then defined at ≥0.6. LD identified 84 medicine pairs; Bi identified 144; and Tri identified 158; none were uniquely found by LD, four by Bi and 18 by Tri. A final look-alike list with 82 medicine pairs meeting all three thresholds was identified for TML. Discussion/Conclusion Screening using all three methods led to a 61% reduction in medicines pairs allowing prioritisation of TML as an effective intervention based on look-alike pairs with the highest risk of error. This study focused on identifying orthographic similarity in ‘look-alike’ medicine pairs only. With no single intervention available to prevent LASA errors, future work can explore other interventions. In the absence of literature around normalised LD, the definition of an in-house threshold posed to be another limitation and an area where further exploratory work should be considered. As new LASA incidents arise or the Trust catalogue increases, these methods should be applied to triage their look-alike potential, confirming if TML is an appropriate intervention. References 1. World Health Organisation. Look-alike, sound-alike medication names. patient safety solutions. 2007 May, Volume 1: Solution 1. 2. Strcmp2. [Internet] Department of Computer Science, University of Toronto: Aditya Bhargava [Date of publication unknown]. Available from: https://www.cs.toronto.edu/~aditya/strcmp2/, Work based on: Grzegorz Kondrak. N-gram similarity and distance. Proceedings of the Twelfth International Conference on String Processing and Information Retrieval. 2005 Nov; pp. 115—126. 3. The National Pharmacy Association Pharmacy Team. Look-alike sound-alike (LASA) items [Internet]. The National Pharmacy Association; 2021. Available from: https://www.npa.co.uk/information-and-guidance/look-alike-sound-alike-lasa-items/
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».