Adoption of the Nova Scotia (Canada) Community Pharmacy Medication Management Program, 1-Year Post-Initiation.
Bibliographic record
Abstract
BACKGROUND: Pharmacists conduct medication reviews to optimize drug therapy. Each jurisdiction implements and funds these programs differently. OBJECTIVE: To describe the uptake of the first year of a community pharmacy medication review program reimbursed by the publically insured seniors' drug benefit program in Nova Scotia, Canada. METHODS: This retrospective analysis included 294 pharmacies and 105,000 beneficiaries enrolled in the Nova Scotia Seniors' Pharmacare Program. Prescription and service claims data from this program were analyzed to determine type and number of beneficiaries receiving a medication review, number and predictors of pharmacies completing reviews, and number of prescribed medications 6-months before and 6-months after the review. RESULTS: 428 medication reviews were conducted and billed by 33% of Nova Scotia pharmacies (1-50 reviews per pharmacy per year). The mean number and range of medications before the review were 10.8 (4-28) and following the review 10.4 (0-24), with an average decrease of 0.4 medications (95% CI 0.1-0.6), p=.0043). Patients receiving a review had a mean age of 75.2 years; 64.9% were female. Most pharmacies conducted reviews when patients reached their annual copayment (93%). CONCLUSIONS: Approximately 33% of pharmacies billed at least one medication review in the first year of the program. In spite of a $150 reimbursement per community pharmacy medication review, only 428 reviews were conducted over a 13-month period for a population of over 100,000 seniors. Results suggest financial reimbursement alone is not sufficient to implement a medication management program; health systems need to determine patient, pharmacist, pharmacy and health system level strategies to implement medication reviews more broadly.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".