Clinical Pharmacist And Nursing Roles In Managing High-Alert Medications In Emergency Departments: A Systematic Review
Bibliographic record
Abstract
Background: High-alert medications pose a significant risk of patient harm when mismanaged, particularly in Emergency Departments (EDs), where time-sensitive decisions and high workload increase the likelihood of medication errors. Clinical pharmacists and emergency nurses play critical and complementary roles in mitigating these risks through dose verification, medication preparation, administration, and patient monitoring. However, the extent of their collaboration and its impact on medication safety outcomes remain insufficiently explored. Aim: This systematic review aims to synthesize current evidence on the roles of clinical pharmacists and emergency nurses in managing high-alert medications within EDs, and to examine how interprofessional collaboration influences medication safety and workflow efficiency. Methods: Following PRISMA 2020 guidelines, a comprehensive search was conducted across PubMed, Scopus, Web of Science, CINAHL, and Google Scholar for studies published between 2010 and 2025. Eighteen studies met the inclusion criteria and were analyzed through narrative synthesis. Quality appraisal was performed using the Newcastle–Ottawa Scale, CASP, and JBI tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".