The impact of medication reviews by community pharmacists
Bibliographic record
Abstract
OBJECTIVES: Many Canadians use prescription medicines that are unnecessary or that can lead to adverse events. In response, many provinces have introduced programs in which pharmacists are paid to perform medication reviews with patients. As the evidence on such programs is equivocal, we investigated the impact of British Columbia's program. DESIGN: Interrupted time series. SETTING: British Columbia, Canada. PARTICIPANTS: All residents of British Columbia who received a medication review between May 1, 2012, and June 30, 2013 (163,776 individuals). INTERVENTION: Using British Columbia's population-based PharmaNet drug utilization system, we collected data on community pharmacist-led medication reviews. The PharmaNet database contains a record of all medication reviews conducted in an ambulatory setting. MAIN OUTCOME MEASURES: We studied the impact of first medication reviews conducted between May 2012 and June 2013. We used interrupted time series analysis to assess longitudinal changes in patients receiving a standard review (n = 147,770) and a more intensive pharmacist consultation (n = 16,006). Our outcomes included drug utilization, costs, potentially inappropriate prescriptions, and medication persistence measured through the proportion of commonly used chronic medications that were eventually refilled. RESULTS: Overall, we observed few changes in the level or trend of any of the outcomes we studied. Both review types were followed by significant increases in both the number of prescriptions per month and expenditures. The continuation of long-term medications did not change for 3 of 4 classes, and increased very slightly for the final class. We found no evidence of deprescribing, either for classes that are potentially problematic for long-term use (benzodiazepines and proton pump inhibitors) or for potentially inappropriate prescriptions in seniors. CONCLUSIONS: Our results suggest that medication reviews did not significantly modify prescription drug use by recipients. Future iterations of such programs might be modified to be better targeted and to encourage closer collaboration between pharmacists and prescribing health care professionals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".