The Role of Triage Nurse Ordering on Mitigating Overcrowding in Emergency Departments: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: The objective was to examine the effectiveness of triage nurse ordering (TNO) on mitigating the effect of emergency department (ED) overcrowding. METHODS: Electronic databases (Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, CINAHL, SCOPUS, Web of Science, HealthSTAR, Dissertation Abstracts, ABI/INFORM Global), controlled trial registry websites, conference proceedings, study references, experts in the field, and correspondence with authors were used to identify potentially relevant studies. Interventional studies in which TNO was used to influence ED overcrowding metrics (length of stay [LOS] and physician initial assessment [PIA]) were included in the review. Two reviewers independently assessed study eligibility and methodologic quality. Mean differences were calculated and reported with corresponding 95% confidence intervals (CIs). RESULTS: From more than 14,000 potentially relevant studies, 14 were included in the systematic review. Most were single-center ED studies; the overall quality was rated as weak, due to methodologic deficiencies and variable outcome reporting. TNO was associated with a 37-minute mean reduction (95% CI = -44.10 to -30.30 minutes) in the overall ED LOS in one randomized clinical trial (RCT); a 51-minute mean reduction (95% CI = -56.3 to -45.5 minutes) was observed in non-RCTs. When applied to injured subjects with suspected fractures, TNO interventions reduced ED LOS by 20 minutes (95% CI = -37.5 to -1.9 minutes) in three RCTs and by 18 minutes (95% CI = -23.2 to -13.2) in two non-RCTs. No significant reduction in PIA was observed in two RCTs. CONCLUSIONS: Overall, TNO appears to be an effective intervention to reduce ED LOS, especially in injury and/or suspected fracture cases. The available evidence is limited by small numbers of studies, weak methodologic quality, and incomplete reporting. Future studies should focus on a better description of the contextual factors surrounding these interventions and exploring the impact of TNO on other indicators of productivity and satisfaction with health care delivery.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".