Engaging Community Pharmacy as Part of a Multidisciplinary Preventive Care Approach
Bibliographic record
Abstract
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.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".