Community pharmacists as catalysts for deprescribing: An exploratory study using quality improvement processes
Bibliographic record
Abstract
BACKGROUND: There is growing international emphasis on deprescribing, involving the monitored reduction or stopping of medications that are no longer needed or that cause more harm than benefits, especially for the elderly. Community pharmacists are well positioned to partner with patients and their other health care providers in facilitating deprescribing activities. OBJECTIVE: To build community pharmacists' capacity to integrate deprescribing into their daily practices through training and workflow strategies. METHODS: This study used an exploratory mixed-methods (primarily qualitative) design. Staff at 4 Ontario pharmacies were trained to use deprescribing guidelines. Qualitative data were collected through field observations, notes from advisory group meetings and documented Plan-Do-Study-Act (PDSA) plans. Quantitative data were derived from process and output measures reported by the pharmacies. Iterative PDSA cycles allowed the project team to appraise and accelerate process improvements over time and to summarize findings on facilitators, barriers and the adaptation of processes. RESULTS: All 4 pharmacies identified individual and common goals related to deprescribing; however, drugs targeted and use of professional services to identify and address deprescribing opportunities varied. Each demonstrated that deprescribing activities could be integrated into daily practice and workflow. Common themes characterized approaches taken by each pharmacy: (1) processes used for capacity building among staff to identify patients for possible deprescribing, (2) approaches for preliminary interactions with patients, (3) in-depth medication reviews and (4) follow-up and monitoring. Approaches changed over time. CONCLUSION: 2019;152:xx-xx.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".