Effects of pharmacist prescribing on health-related outcomes in secondary care compared with medical prescribing or no treatment: A systematic review
Bibliographic record
Abstract
BACKGROUND: Pharmacist prescribing has gained momentum over the years and is currently utilised in Brazil, Canada, New Zealand, Poland, South Africa, United Kingdom (UK), and the United States of America (USA). While pharmacist prescribing has been associated with symptom improvement, reduced prescribing error rates, medication omissions, and improved medication access, the effects of pharmacist prescribing in secondary care have not been previously evaluated. OBJECTIVES: This systematic review aimed to assess the effects of pharmacist prescribing on health-related outcomes in secondary care. METHODS: MEDLINE, Cochrane, CINAHL, Scopus, and Web of Science Core Collection databases were searched for studies published in English from database inception to October 3, 2024. Studies were included if the intervention was pharmacist prescribing and the outcomes were health-related and measurable. Narrative synthesis of the outcomes from included studies was conducted. RESULTS: A total of 21 studies from five countries (Australia, Egypt, Hong Kong, UK, USA) were included. Pharmacist prescribing significantly improved disease management, reduced the number of hospital revisits, improved blood pressure control, international normalised ratio control and decreased the number of adverse events when compared to medical prescribing. The evidence further suggests that the healthcare costs per patient per month, median cost of treatment, inpatient hospitalisations and emergency department admissions were significantly lower when pharmacists prescribed medicines. The evidence was low to moderate quality. CONCLUSION: This systematic review provides evidence that pharmacist prescribing has a positive effect on therapeutic outcomes, and that pharmacist prescribing is comparable or better than medical prescribing in secondary care.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".