Implementation of the appointment-based model in community pharmacies: An analysis of refills and adherence
Bibliographic record
Abstract
BACKGROUND: Traditionally, much of community pharmacy practice relies on patients to request their own medication refills. These refills are often not aligned, which has been shown to decrease adherence and workflow efficiencies. The appointment-based model (ABM) is designed to proactively synchronize refills and schedule patient-pharmacist appointments. OBJECTIVES: To describe the characteristics of patients enrolled in the ABM; and to compare the number of distinct refill dates, number of refills, and adherence for antihypertensives, oral antihyperglycemics, and statins 6-months and 12-months pre-post ABM implementation. METHODS: In September 2017, the ABM was implemented across independent community pharmacies within a pharmacy banner in Ontario, Canada. In December 2018, a convenience sample of three pharmacies was extracted. Demographic and clinical characteristics were collected on program enrollment (index) date for individual patients and their medication fill histories were used to investigate adherence measures including distinct number of refill dates, number of refills, and proportion of days covered. Descriptive statistics were analyzed using StataCorp. RESULTS: Analysis of 131 patients (48.9% male; mean age 70.8 years ± 10.5 SD) filled on average 5.1 ± 2.7 medications with 73 (55.7%) experiencing polypharmacy. Patients had a significant reduction in mean number of refill dates (6.8 ± 3.8 SD six-months pre-enrollment, 4.9 ± 3.1 SD six-months post-enrollment, p < 0.0001). Adherence to chronic medications remained high (PDC ≥95%). CONCLUSION: The ABM was implemented for a cohort of established users, already highly adherent to their chronic medications. Results demonstrate reduced filling complexity and fewer refill dates while also sustaining the high baseline adherence across all chronic medications studied. Future studies should investigate patient perspectives and potential clinical benefits of the ABM.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".