A framework to lower the risk of medication prescribing and dispensing errors: A usability study of an NFC-based mobile application
Notice bibliographique
Résumé
BACKGROUND: Wrong medication and wrong dosage are major risks in the pharmaceutical industry, as many medication errors occur when dispensing medication. The dispensing process in its current form is limited in verifying the patient's identity before dispensing the medication. Furthermore, this process does not offer a robust method for providing accurate medication intake instructions. Therefore, we have developed a framework to accurately and securely overcome issues associated with transferring patient credentials and prescription information. The long-term goal of this research is to develop a framework to mitigate medication dispensing errors. One of the framework components is the mobile application that uses near-field communication (NFC) to transfer information. Therefore, in this paper, we designed a user study to assess the proposed NFC-based mobile application in terms of usefulness and ease of use compared with the traditional method of picking up a prescribed medication. METHODS: We conducted a usability study with 21 participants to perform four tasks to simulate the process of picking up a prescribed medication using the proposed NFC application method and the traditional method of picking up medication. Then, we asked the participants to complete two post-questionnaires after using each method to evaluate the participants' experience of the process. Next, we asked the participants to complete an additional questionnaire about the usefulness of the NFC application method. Finally, we conducted semi-structured interviews with the participants to get more evidence to back up the questionnaire answers. RESULTS: Our findings show that 91% of the participants believe using the NFC application method will improve patient safety during the medication pickup process. Nearly 97% of participants found the NFC application method easy to use. Our findings show that the participants scored lower when using the NFC application method compared with the traditional method when trying to identify the wrong medication after dispensing. In addition, 90% of the participants successfully identified the wrong medication when using the NFC application method, compared to only 38% when using the traditional method. Finally, the results show that the participants preferred using the NFC application method in terms of information availability, security, and privacy. CONCLUSIONS: The study findings show that the proposed NFC application for managing patients' prescriptions and picking up medication might improve patient safety. The results show that the participants believe the NFC application will mitigate medication dispensing errors, at least from their end. The participants believe the application will provide a fast and accurate method of verifying dispensed medication from the patient end. Moreover, the application will help the patient to track their current prescription, which also helps them remember the medication intake instructions. Finally, the study indicates that the application will provide a secure, private, and accurate method to help verify the patient's identity, thus minimizing medication errors during the medication dispensing process.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».