The impact of pharmacist‐led strategies implemented to reduce errors related to cancer therapies: a systematic review
Bibliographic record
Abstract
Abstract Background Patients with cancer are managed across the whole healthcare spectrum. This complex system of interdependencies has high potential for errors to occur. This is a focus area for pharmacists, who possess the skillset to optimise cancer care across the health care system, identifying errors and other medication‐related problems (MRP). Aim The aim of this study was to identify and describe the impact of the pharmacist’s contribution in reducing cancer therapy‐related errors. Methods A search of English‐language publications in Embase, Medline and CINAHL was conducted. Databases were searched from 1 January 2010 until 29 September 2020 to identify all quantitative studies of a descriptive, observational or experimental design. Articles describing pharmacist‐led interventions in adults receiving one or more cancer therapies including oral chemotherapy, intravenous chemotherapy or immunotherapy compared to no intervention, usual care or a service delivered by another healthcare professional were included. Researchers screened articles to identify eligible studies, and then data were extracted using a standardised data collection sheet. Quality assessment was undertaken using the modified Cochrane and the Newcastle Ottawa risk of bias tools. Data were reported as number or percentage. Results Of 2292 papers identified, nine studies were eligible for inclusion in this review. Pharmacist interventions consistently showed an increased identification of medication errors and medication‐related problems. Pharmacist contributions in many of the included studies comprised medication reviews and monitoring, laboratory monitoring, adverse drug reaction and drug–drug interaction management, adherence monitoring and medication counselling. All studies showed pharmacist intervention in cancer care resulted in fewer errors compared to control arms. Error minimisation was described for parenteral and oral cancer therapies and also for supportive medications such as antiemetics. Conclusion Pharmacists reduce errors and potential patient harm in practice settings where cancer patients are treated. Pharmacists are an integral component of cancer care teams.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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; 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".