Pharmacist interventions in optimising opioid medication therapy in pain management for palliative care patients: A systematic review
Bibliographic record
Abstract
Background Opioid therapy is a critical component in managing pain in palliative care, where pharmacists’ specialised expertise is crucial in ensuring quality care for patients. This systematic review aims to document available evidence on pharmacist interventions and their impact on optimising opioid therapy for pain management in palliative care patients. Methods We searched Medline (OVID), Embase (OVID), APA PsycINFO and Cochrane Central Register of Controlled Trials (CENTRAL) for relevant articles published from the beginning to 31 st December, 2022. All original studies documenting pharmacists’ intervention and impact in optimising patients receiving opioid therapy for their pain management in palliative care settings were included in this review. Results The database and reference search yielded to a total of 7154 studies. Out of these, only 3 studies met the eligibility criteria and were included in this study. These studies were conducted in Korea, Canada and United States. Pharmacists were involved in assessing pain, suggesting medication for pain and other symptom management, providing patient education, counselling and recommendation, assessing patient's medication effects such as adverse effects, drug interaction and duplication, and adjusting medication. Similarly, their involvement showed improvements in pain management, opioid usage and management strategies . Conclusion This systematic review highlights the important role of pharmacists in optimising opioid medication therapy for pain management in palliative care patients. Their contributions to palliative patient care improve pain outcomes and overall quality of life. Integrating pharmacists into palliative care teams can enhance pain management practices and provide better care for palliative patients. Further studies accompanying the robust methodologies and broader settings will validate the findings of this review.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".