Engaging Community Pharmacy as Part of a Multidisciplinary Preventive Care Approach
Notice bibliographique
Résumé
Objectives Preventive care is a crucial aspect of patient management in rheumatology. Whether it is initiating antiresorptive therapy to reduce fracture risk, lipid panel verification for cardiovascular disease prevention, or ensuring adequate vaccination for patients on immunosuppressive therapy; preventive interventions are a key part of rheumatology practice. In this quality improvement study, rheumatologists and rheumatology nurse clinic members implemented a referral service engaging community pharmacy deliver preventive care for patients with a noted concern. Methods Inclusion: Patients >18, seen at the South Health Campus Rheumatology Clinic, and a relevant concern in osteoporosis management, cardiac risk management, routine vaccination, tobacco cessation, or other ambulatory care issue (ex. Diabetes). Patients were referred using a common referral form designed by the nursing clinic. Referrals were sent to the regional Co-op Specialty Hub with pharmacists trained in preventive concerns for rheumatology patients. Pharmacist reports were reviewed to determine pharmacist interventions. Results 36 patients were referred for pharmacist preventive care, 1 declined to participate. Rheumatologists sent 21 referrals and the nurse clinic sent 15. The average time to pharmacist appointment was 10.12 days (median 8 days, range 1-44). On average 4.3 (range 1-9) pharmacist interventions were performed for each referred patient. 3/35 of patients did not have family physicians; they received an average of 8 pharmacy interventions. The most common referrals were for osteoporosis and cardiovascular risk management (25/35 each). For osteoporosis management, the most common interventions were FRAX scoring (15/25), non-pharm patient education (14/25), and sending BMD requests (9/25). Pharmacists initiated 4 patients on bisphosphonates. For cardiovascular risk management, the most common interventions were Framingham risk scoring and non-pharm patient education (13/25 each). Statins were initiated in 5 patients and 5 drug-related problems were identified (ie, 3 cases of suboptimal statin or antihypertensive dosing, 2 relevant interactions). For routine vaccination, rheumatology team members noted 25 outstanding vaccines for 11 patients. The pharmacists were able to administer 5 vaccines to these patients. Other ambulatory care issues which were addressed by pharmacy included smoking cessation (5/35) and diabetes management (3/35). To date, 12/35 patients have had at least 1 follow-up with community pharmacy for ongoing management (9/12 for cardiovascular risk-related issues). Conclusion In this study we found that community pharmacists were able to provide requested preventive health services as part of a multidisciplinary referral service. Follow-up studies will look at gauging the impact of these services longitudinally based on previous literature.[1-3] [1.] Yuksel N. Osteoporos Int 2010;21(3):391-8. [2.] Al Hamarneh YN. RxIALTA: evaluating the effect of a pharmacist-led intervention on CV risk in patients with chronic inflammatory diseases in a community pharmacy setting: a prospective pre-post intervention study. BMJ Open 2021;11(3):e043612. [3.] Choquette D. Pharm Pract 2021;19(3):2377.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,008 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
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 ».