A Systematic Review of Antimicrobial Stewardship Interventions in the Emergency Department
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: To improve antimicrobial utilization, development and implementation of antimicrobial stewardship programs in the emergency department (ED) has been recommended. The primary objective of this review was to characterize antimicrobial stewardship (AMS) in the ED and to identify interventions that improve patient outcomes or process of care and/or reduce consequences of antimicrobial use. METHODS: This study was completed as a systematic review. The following databases were searched from inception through November, 2016: MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature, Scopus, and Web of Science. Randomized controlled trials, nonrandomized controlled trials, controlled and uncontrolled before-and-after studies, interrupted time series studies, and repeated-measures studies evaluating AMS interventions in the ED were included in the review. Studies published in languages other than English were excluded. RESULTS: A total of 43 studies meeting inclusion criteria were identified from our search. Patient or provider education and guideline or clinical pathway implementation were the most commonly reported interventions. Few studies reported on audit and feedback, and no study evaluated preauthorization. Impact of interventions showed variable results. Where identified, benefits of AMS interventions primarily included improvement in delivery of care or a decrease in antimicrobial utilization; however, most studies were rated as having unclear or high risk of bias. CONCLUSION: AMS interventions in the ED may improve patient care. However, the optimal combination of interventions is unclear. Additional studies with more rigorous design evaluating core components of AMS programs, including prospective audit and feedback are needed.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".