Impact of pharmacist-led medication review among hemodialysis patients: a systematic review
Bibliographic record
Abstract
Background: Medication-related problems (DRPs) are common among hemodialysis (HD) patients, and pharmacist-led medication reviews have been shown to address such issues. However, the impact of these interventions and the specific types of DRPs among this patient group remain unclear. Objectives: This systematic review aimed to assess the impact of pharmacist-led medication reviews among HD patients, identify the most prevalent types of DRPs, and explore the factors associated with these problems. Methods: , Science Direct, Google Scholar, and EBSCOHost, for studies published from January 2012 to July 2023. Studies included were those focusing on pharmacist interventions in HD patients. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of selected studies. Results: After screening 343 articles, 10 studies (involving 1342 HD patients) were included. Nine studies were rated as high quality, and one as fair quality. The studies predominantly used prospective designs. A total of 4511 DRPs were identified, with suboptimal drug treatment, non-adherence to medications, and drug use without indication being the most common issues. Pharmacist interventions led to the resolution or reduction of DRPs, shorter hospital stays, improvement in laboratory outcomes, better quality of life (QoL), and enhanced patient understanding. However, interventions had minimal or no significant impact on reducing unplanned admissions, mortality rates, or improving medication adherence. The reduction in healthcare utilisation costs was inconsistent across studies. Conclusion: Pharmacist-led medication reviews were effective in resolving DRPs and improving clinical outcomes in HD patients, such as quality of life and lab values. However, their impact on healthcare utilisation and mortality remains inconclusive. Further research with longer follow-up is needed to assess the long-term economic outcomes of these interventions.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".