Impact of Drug Plan Management Policies in Canada: A Systematic Review
Bibliographic record
Abstract
Background/objective: In Canada, pharmaceutical expenditures require an increasing proportion of total health care dollars. Drug plan managers use various policies to manage medication use and control costs, but these policies can have unintended consequences. The objective of this systematic review is to evaluate the impact of drug policies on economic, clinical and humanistic (quality of life or satisfaction) outcomes. Methods: Articles in which the primary objective was to evaluate the impact of a drug policy tool or technique in Canada and that measured one or more economic, clinical or humanistic outcomes were considered for inclusion. Studies were excluded if they were based in institutions or not published in peer-reviewed journals. The search process included searching 9 electronic databases, searching the reference lists of identified articles and working papers and contacting drug policy researchers. Studies were assessed for quality and suitability for meta-analysis. We performed a qualitative synthesis of the study design, study sample and outcomes. Results: Thirty-five articles satisfied the inclusion criteria. Most ( n= 25, 71%) determined that drug policies reduced the costs, utilization or both of the studied drug therapy. Only 13 studies (37%) measured the impact of the drug policy on the cost or utilization of medical care, and the impact of the policy was favourable in only 4 of these cases. Six studies (17%) evaluated the impact of the policy on clinical outcomes; none reported a favourable impact. No studies evaluated the impact of the drug policy on humanistic outcomes. Discussion: Although the drug policies in most of the studies included in this systematic review did achieve the desired goal of reducing drug costs, utilization or both, the impact on other outcomes was seldom examined. In the 6 studies in which clinical outcomes were included in the evaluation framework, the impact was inconclusive. Humanistic outcomes have been completely excluded from evaluations to date.
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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 | 0.021 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".