Evaluation of feasibility and potential impact of a clinical innovative software tool to support pharmacists to prescribe for minor ailments
Notice bibliographique
Résumé
Background: \nThe role of community pharmacists has shifted from dispensing prescribed medications to providing patient-focused clinical services such as Pharmacists Prescribing for Minor Ailments (PPMA). These services are expected to improve patient care. Pharmacists across provinces have been given some level of prescriptive authority since 2005. The PPMA service was first introduced in Alberta. Currently, nine out ten Canadian provinces have the authority for PPMA service. Ontario recently adopted PPMA, in January 2023. However, there are several barriers such as workload, time constraints and integration to workflow that can hinder pharmacists’ ability to implement PPMA into existing practice. Technological solutions such as computerized decision support system (CDSS) can help pharmacists and facilitate performing PPMA service. PharmAssess Diagnostics developed a CDSS that provides a digital software platform for pharmacists to provide clinical services such as minor ailment prescribing. Although previous research examined the acceptability and impact of CDSS in other professions, there is paucity of research about the feasibility and impact of implementing supporting software tools into the workflow of community pharmacies on adapting PPMA. \nObjective: \nThe aim of this project is to evaluate the feasibility and potential impact of a clinical innovative software tool to support pharmacists for minor ailment prescribing. \nMethod: \nThis project followed a mixed method design. It included an anonymous online survey and semi-structured interview with community pharmacists/pharmacy student interns to explore their perspectives on the usability, acceptability, potential impact on integration in workflow and overall effect on workload while using the PharmAssess Diagnostics software for PPMA. In Ontario, pharmacists were recruited via the list of pharmacists in the Ontario College of Pharmacist (OCP) who agreed to be contacted for research purposes. Additionally, the co-op supervisors list from the School of Pharmacy University of Waterloo, and clients of the software company were contacted to be recruited. Demographic (age, gender) and professional characteristics (academic qualification, type of community pharmacy, current position in the pharmacy) were collected from participating pharmacists. Then, pharmacists’ perspectives were examined using a survey with Likert’s scale questions and were summarized. Descriptive statistics were used to describe the distribution of the responses. \nSemi-structured interviews were conducted with pharmacists/student interns who were further interested in providing their detailed perspectives with the use of this software tool in community pharmacy settings. The interviews were analyzed thematically. Then both forms of data were merged using the side-by-side approach to draw conclusion. \nResults: A total of 11 survey responses were collected. Pharmacists agreed that the software tool was usable (72%), acceptable (81%), had positive impact on workload (63%) and had positive impact on workflow (45%). Overall, 90% of participants stated that the average time per consultation using the software tool ranged between 5 to 15 minutes. Seven pharmacists were interviewed virtually. Three major themes emerged from the interviews, revealing usability of the software tool, facilitators, and barriers to the implementation of the software tool and impact of the software tool implementation into community pharmacy.
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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 | 0,027 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».