Intravenous Medication Administration Errors in Hospitalised Patients: An Updated Systematic Review
Bibliographic record
Abstract
BACKGROUND: Administering intravenous (IV) drugs carries a high risk of adverse effects due to their direct entry into circulation. Identifying the prevalence and types of IV administration errors and the drugs involved is crucial for implementing effective interventions to reduce such errors. AIM: This systematic review aimed to examine and synthesise the available articles on medication errors involving IV administration in hospitalised patients. METHODS: A comprehensive search was conducted using electronic databases, including PubMed, Ovid Medline, and CINAHL. The search was performed without time limitation up to July 2023. However, only articles published in English and human subjects were included. The quality of the studies was appraised using the Newcastle-Ottawa quality assessment scale (NOS). This systematic review was registered with PROSPERO (CRD42023469352). RESULT: Database searches yielded 2177 articles; after duplicate removal, 1717 underwent title and abstract screening, and 23 were included after full-text review. The studies were from 12 countries, and the multicentre study included countries from Europe, Africa, the Americas, Asia, and Australia. The majority of the studies were conducted in either teaching hospitals (n = 11) or university-affiliated hospitals (n = 7), with most involving direct observation (n = 21). IV administration errors exhibit a broad prevalence range of 5.0%-62.9%, involving various types such as wrong diluent, dose, route, rate, technique, omission, and timing. Studies lack uniformity in reporting, with some not specifying prevalence. The highest prevalence of specific errors varies across settings. CONCLUSION: Our review highlights that IV medication error rates vary based on study design, setting, and population. Standardised definitions, reporting procedures, and reliable tracking methods are needed. Human factors, system issues, and environmental stressors influence medication errors. Future research must improve our understanding and address these factors to enhance patient safety and healthcare quality.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".