Costs and savings associated with a pharmacists prescribing for minor ailments program in Saskatchewan
Bibliographic record
Abstract
BACKGROUND: Health care systems around the world have started to develop pharmacists prescribing for minor ailments (PPMA) programs. These programs aim to improve the efficiency of care, reduce physician visits, and increase the accessibility to prescription medication (Rx). This study performed an economic impact analysis of the pharmacists prescribing for minor ailments program in Saskatchewan. METHODS: We measured costs for the program and the alternative scenario (i.e. no PPMA program) from a public payer and societal perspective, using primary data on pharmacists prescribing consultations in Saskatchewan. Furthermore, we calculated public payer and societal savings, and return on investment ratios for the program, as well as projecting the costs and benefits over the next 5 years. RESULTS: Overall, we found that from a societal perspective, the Saskatchewan PPMA program saved the province approximately $546,832 in 2014, while according to the public payer perspective, the program was only marginally cost-saving in 2014. After 5 years of implementation, from a societal perspective, cumulative cost savings were projected to be $3,482,660, and the return on investment ratio was estimated to be 2.53. CONCLUSIONS: Our results demonstrate that this type of program may prove cost-saving and lead to improved access to the health care system in Canada, especially if savings to society are considered. This type of PPMA program may prove economically feasible and beneficial in many countries considering expanding pharmacists scope of practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".