Economic evaluation of pharmacists prescribing for minor ailments in Ontario, Canada: a cost-minimization analysis
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to use a decision-analytic model to examine the potential economic impact of establishing a remunerated programme for pharmacists prescribing for minor ailments (PPMA) in Ontario, Canada. METHODS: A novel decision tool was developed to assess the economic impact of pharmacists prescribing for upper respiratory tract infections (URTIs), contact dermatitis (CD) and conjunctivitis by performing a cost-minimization analysis from a public payer perspective. Two prescribing strategies were compared: (1) PPMA, where patients may seek care from pharmacists or physicians, and (2) the usual care model (UCM), where all patients receive care from physicians. Two remuneration models for the PPMA strategy were also compared: (1) a prescription-detached scenario (PDS), where pharmacists were remunerated CAD$18.00 for each consultation, and (2) a Prescription-Attached Scenario (PAS), where pharmacists were only remunerated if a decision to prescribe was made. KEY FINDINGS: At a service uptake rate of 38% for the PDS, the PPMA model led to savings of $7.51, $4.08 and $5.15 per patient for URTIs, CD and conjunctivitis, respectively. Per 30 000 patients, the PPMA model for these minor ailments was projected to lead to cumulative reductions in visits to the emergency department, family physician and walk-in clinics by 799, 3677 and 5090, respectively. CONCLUSIONS: The results of the study strongly suggest that enabling community pharmacists to assess and prescribe for minor ailments could potentially lead to large savings for the government in Ontario, Canada. In 100% of the PAS scenarios simulated, pharmacists as prescribers led to cost savings.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".