Designing a Pharmacist Opioid Safety and Intervention Tool
Bibliographic record
Abstract
ABSTRACTBackground: Despite the recent increase in opioid overdoses across Canada, few pharmacy-led initiatives have been implemented to address issues related to opioid prescribing in the hospital setting.Objectives: The primary objective of this study was to develop a clinical tool, intended for use by hospital pharmacists and informed by best practices from the literature, that would provide a structured approach to enhancingthe safety of opioid prescribing. The secondary objective was to collect pharmacists’ opinions about the feasibility and utility of this tool.Methods: A comprehensive literature search and pharmacist focus group analysis provided content for development of a candidate clinical tool. This tool was then piloted by clinical pharmacists working on generalmedical and surgical units in a single hospital. Pharmacists participating in the pilot were invited to complete an online survey concerning their perceptions of the tool. Descriptive statistics were used to analyze the survey results.Results:The literature search and focus group analysis led to development of a candidate clinical tool that focused on Medication review, Optimization, Reassessment, and Education (MORE). It included key risk factors relating to opioid safety, along with suggested mitigating strategies. The MORE tool was piloted for 3 weeks by 14 clinical pharmacists, 9 of whom responded to the subsequent survey. Five respondents indicated that the clinical tool increased their ability to identify risk factors. Five respondents also noted an increase in their ability to identify possible interventions. Most respondents felt that the tool was useful and that it would be feasible to integrate it into their practice; however, they noted that a more streamlined version could improve ease of use.Conclusions: The MORE tool was well received by clinical pharmacists. Implementation of the tool into routine practice requires additional changes to improve ease of use. Suggestions for modifying and streamlining the tool will be incorporated into future versions.RÉSUMÉContexte : Malgré l’augmentation récente des surdoses d’opioïdes au Canada, peu d’initiatives menées sous la houlette de pharmacies ont été mises en place sur les enjeux potentiels liés à la prescription d’opiacés en milieu hospitalier.Objectifs : L’objectif principal de cette étude visait à élaborer un outil destiné aux pharmaciens d’hôpitaux, s’inspirant des meilleures pratiques rapportées dans la documentation, qui fournirait une approche structuréepour améliorer la sécurité de la prescription d’opioïdes. L’objectif secondaire consistait à recueillir les opinions des pharmaciens sur la faisabilité et l’utilité d’un tel outil.Méthode : Des recherches bibliographiques étendues ainsi qu’une analyse de groupes de discussion de pharmaciens ont fourni le contenu necessaire à l’élaboration d’un outil clinique expérimental. Ensuite, cet outil a été testé par des pharmaciens cliniciens travaillant dans des unités médicales générales et chirurgicales au sein d’un seul hôpital. Les pharmaciens participant au projet pilote ont été invités à répondre à une enquête en ligne sur leur perception de l’outil. Des statistiques descriptives ont permis d’analyser les résultats de l’enquête.Résultats : Les recherches bibliographiques et l’analyse des groupes de discussion ont débouché sur le développement d’un outil clinique nommé MORE [pour Medication review, Optimization, Reassessment, and Education, ou Examen, optimisation, réévaluation et éducation aux médicaments]. Il comprenait des facteurs de risque liés à la sécurité des opioïdes ainsi que des suggestions de stratégies d’atténuation. Quatorzepharmaciens cliniciens, dont neuf ont répondu à l’enquête qui a suivi, ont testé le MORE pendant trois semaines. Cinq répondants ont indiqué que l’outil clinique augmentait leur capacité à déterminer les facteurs de risque. Cinq ont également noté une meilleure capacité à déterminer les interventions possibles. La plupart des répondants ont estimé que l’outil était utile et qu’il serait possible de l’intégrer dans leur pratique; cependant, ils ont aussi noté qu’une version simplifiée pourrait faciliter son utilisation.Conclusions : Les pharmaciens cliniciens ont bien accueilli l’outil MORE. Sa mise en oeuvre dans la pratique courante exige cependant des changements supplémentaires pour faciliter son utilisation. Les versions à venir tiendront compte des propositions visant à le modifier et à le simplifier.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".