Evolution of Intravenous Medication Errors and Preventive Systemic Defenses in Hospital Settings—A Narrative Review of Recent Evidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Intravenous drug administration has been associated with severe medication errors in hospitals. The present narrative review is based on a systematic literature search, and aimed to describe the recent evolution in research on systemic causes and defenses in intravenous medication errors in hospitals. METHODS: This narrative review was based on Reason's theory of systems-based risk management. A systematic literature search covering the period from June 2016 to October 2021 was conducted on Medline (Ovid). We used the search strategy and selection criteria developed for our previous systematic reviews. The included articles were analyzed and compared to our previous reviews. RESULTS: The updated search found 435 articles. Of the 63 included articles, 16 focused on systemic causes of intravenous medication errors, and 47 on systemic defenses. A high proportion (n = 24, 38%) of the studies were conducted in the United States or Canada. Most of the studies focused on drug administration (n = 21/63, 33%) and preparation (n = 19/63, 30%). Compared to our previous review of error causes, more studies (n = 5/16, 31%) utilized research designs with a prospective risk management approach. Within articles related to systemic defenses, smart infusion pumps remained most widely studied (n = 10/47, 21%), while those related to preparation technologies (n = 7/47, 15%) had increased. CONCLUSIONS: This narrative review demonstrates a growing interest in systems-based risk management for intravenous drug therapy and in introducing new technology, particularly smart infusion pumps and preparation systems, as systemic defenses. When introducing new technologies, prospective assessment and continuous monitoring of emerging safety risks should be conducted.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 it