Implementing pharmacist‐led deprescribing in the haemodialysis unit: a quality use of medicine activity in the Queensland hospital setting
Bibliographic record
Abstract
Abstract Background Polypharmacy in haemodialysis patients can not only manifest as the continued prescribing of unnecessary medications but also has the potential to increase medication‐related hospital admissions, morbidity, and mortality. A validated deprescribing algorithm has recently been developed, specifically targeted at dialysis patients. This paper audits the experience of implementing this algorithm in Australian dialysis settings. Aim The aim of this paper was to evaluate the usefulness and applicability of the pharmacist‐led Toronto deprescribing tool in Australian haemodialysis settings. Method The pharmacist‐led deprescribing algorithm was implemented across two metropolitan sites and one rural site. The audit focused on five medications that could potentially be deprescribed in the target patient group (diuretics, alpha blockers, statins, proton pump inhibitors [PPIs], and quinine). Between 1 and 12 months later, a reaudit was conducted, with patients followed up to confirm if medications remained deprescribed. Results Two hundred and eleven patients across three sites were reviewed. Application of the algorithm resulted in 168 medications deemed appropriate to deprescribe. Of these 168 medications, 56 (33%) were initially deprescribed, with 50 medications (30%) remaining deprescribed on reaudit. The deprescribing rates varied between the three different services, with initial deprescribing rates ranging from 18% to 61%. After follow‐up, deprescribing changes across target medications were fairly static, with only a small number of patients restarting either their diuretic or PPI. Conclusion The pharmacist‐led deprescribing algorithm resulted in substantial deprescribing across the three sites. Deprescribing rates varied between the sites due to differences in the team model that the pharmacist worked within and the method of the rollout. Further studies should draw on aspects such as finding enablers to overcome prescriber and patient concerns about deprescribing and the aspects of which team models lead to higher rates of successful deprescribing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".