ClereMed: Lessons Learned From a Pilot Study of a Mobile Screening Tool to Identify and Support Adults Who Have Difficulty With Medication Labels
Notice bibliographique
Résumé
BACKGROUND: In order to take medications safely and effectively, individuals need to be able to see, read, and understand the medication labels. However, one-half of medication labels are currently misunderstood, often because of low literacy, low vision, and cognitive impairment. We sought to design a mobile tool termed ClereMed that could rapidly screen for adults who have difficulty reading or understanding their medication labels. OBJECTIVE: The aim of this study was to build the ClereMed prototype; to determine the usability of the prototype with adults 55 and over; to assess its accuracy for identifying adults with low-functional reading ability, poor ability on a real-life pill-sorting task, and low cognition; and to assess the acceptability of a touchscreen device with older adults with age-related changes to vision and cognition. METHODS: This pilot study enrolled adults (≥55 years) who were recruited through pharmacies, retirement residences, and a low-vision optometry clinic. ClereMed is a hypertext markup language (HTML)-5 prototype app that simulates medication taking using an iPad, and also provides information on how to improve the accessibility of prescription labels. A paper-based questionnaire included questions on participant demographics, computer literacy, and the Systems Usability Scale (SUS). Cognition was assessed using the Montreal Cognitive Assessment tool, and functional reading ability was measured using the MNRead Acuity Chart. Simulation results were compared with a real-life, medication-taking exercise using prescription vials, tablets, and pillboxes. RESULTS: The 47 participants had a mean age of 76 (SD 11) years and 60% (28/47) were female. Of the participants, 32% (15/47) did not own a computer or touchscreen device. The mean SUS score was 76/100. ClereMed correctly identified 72% (5/7) of participants with functional reading difficulty, and 63% (5/8) who failed a real-life pill-sorting task, but only 21% (6/28) of participants with cognitive impairment. Participants who owned a computer or touchscreen completed ClereMed in a mean time of 26 (SD 16) seconds, compared with 52 (SD 34) seconds for those who do not own a device (P<.001). Those who had difficulty, struggled with screen glare, button activation, and the "drag and drop" function. CONCLUSIONS: ClereMed was well accepted by older participants, but it was only moderately accurate for reading ability and not for mild cognitive impairment. Future versions may be most useful as part of a larger medication assessment or as a tool to help family members and caregivers identify individuals with impaired functional reading ability. Future research is needed to improve the sensitivity for measuring cognitive impairment and on the feasibility of implementing a mobile app into pharmacy workflow.
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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 | 0,017 | 0,033 |
| 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 | 0,002 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».