Pharmacists’ role in interventions addressing excessive polypharmacy: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Excessive polypharmacy, which is defined as the use of ten medications or more, poses considerable challenges regarding patient health and healthcare resources. Individuals who exhibit excessive polypharmacy are predisposed to adverse drug effects, drug interactions, and non-adherence, which can result in increased hospitalization, emergency room visits, and mortality. Given their expertise in medication management, pharmacists are uniquely positioned to address the risks associated with excessive medication use. Therefore, exploring their role in this phenomenon across their various fields of practice is essential. AIM: The aim of this review was to summarize the existing literature on the role of pharmacists in addressing excessive polypharmacy in different care settings and to highlight areas where more research is needed. METHOD: A scoping review was conducted by adopting Arksey and O'Malley's methodological framework, along with subsequent enhancements implemented by Levac et al. It was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A comprehensive search was conducted across five databases: MEDLINE, EMBASE, PubMed, CINAHL and Cochrane CENTRAL from their inception until June 2024. Covidence was used for data selection and extraction. The results were analyzed using narrative synthesis. RESULTS: We identified 5236 articles, of which 19 were included. All studies were available in English and were conducted in high-income countries with the majority (84%) being published after 2019. The interventions were carried out in primary care clinics, hospitals, home care, and long-term care facilities, but none of the studies were conducted in community pharmacies. The analysis identified four predominant roles: performing medication reconciliation during care transitions, assessing medication appropriateness, raising awareness among prescribing healthcare professionals, and ensuring patient follow-up and monitoring. These roles emphasized collaboration between patients and interprofessional teams and were supported by various polypharmacy management tools. CONCLUSION: This review's results highlight pharmacists' various roles in managing excessive polypharmacy across care settings. The scope of practice, physical proximity to other health professionals, expertise, higher qualifications and/or additional training all influenced these roles.
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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 | 0.028 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".