Engineering Resilient Community Pharmacies for Chronic Care Management: Protocol for the Development of a Medication Safety Map
Notice bibliographique
Résumé
BACKGROUND: The increase in people with complex chronic health conditions is stressing the US health care delivery system. Community pharmacies play a role in ensuring patients' safe medication use for chronic care management (CCM), but their efforts are undermined by volatile work demands and other system barriers. Medication safety in community pharmacies is a multidimensional issue shaped by the work system and interactions among pharmacists, primary care providers, and patients. OBJECTIVE: The objective is to create and evaluate a system of CCM that supports safe medication use. The CCM system design will focus on creating and evaluating a Medication Safety Map (MedSafeMap) for patients with complex chronic health conditions. This study has three aims: (1) identify and define community pharmacy work system design requirements for safe medication practices, enabling resilient performance; (2) design and develop MedSafeMap, a feasible and sustainable solution, to facilitate safe medication practices through resilient performance; and (3) implement MedSafeMap in community pharmacies and pilot-test its impact on pharmacy staff attitudes, behaviors, and performance. METHODS: This study will leverage participatory design and human factors engineering methods throughout the 3 aims. For aim 1, four rounds of qualitative observations within 6 pharmacy sites will be conducted to parse areas MedSafeMap could address. Two rounds of interviews with pharmacists and technicians from each of the sites will be used to expand upon areas of interest identified during the observations. Observational and interview data will be used to construct functional resonance analysis method models and resilience narratives to map both risks and best practices within the system based on daily workplace factors. For aim 2, focus groups with pharmacist and technician stakeholders will be guided by participatory stakeholder engagement to inform prototyping for MedSafeMap. Simulation-based research involving standardized patients in CCM scenarios will be used to test and refine MedSafeMap components. Finally, for aim 3, MedSafeMap will be implemented in pharmacies. Observations using the work observation method by activity timing (WOMBAT) for the time and motion study will aid in understanding how MedSafeMap impacts pharmacy staff workflow. We will assess adoption challenges and resilience-focused attitudes, behaviors, and performance to support CCM. RESULTS: As of August 2025, all 6 pharmacy sites have been recruited. Three of the 4 rounds of observations, 2 rounds of interviews with 12 pharmacists and 12 technicians from the study sites, and the 6 focus groups have been conducted. Preparations for the simulations are ongoing. CONCLUSIONS: MedSafeMap is an innovative approach that will guide pharmacists and technicians in safely providing care to patients with complex chronic health conditions. It will help them navigate the complex tasks and communications between the pharmacy, patient, and primary care provider arising with this type of complex care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69011.
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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 | 0,048 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,007 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,064 | 0,017 |
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 ».