The Role of Triage Liaison Physicians on Mitigating Overcrowding in Emergency Departments: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: The objective was to examine the effectiveness of triage liaison physicians (TLPs) on mitigating the effects of emergency department (ED) overcrowding. METHODS: Electronic databases (Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, Web of Science, HealthSTAR, Dissertation Abstracts, and ABI/INFORM Global), controlled trial registry websites, conference proceedings, study references, contact with experts in the field, and correspondence with authors were used to identify potentially relevant TLP studies. Intervention studies in which a TLP was used to influence ED overcrowding metrics (length of stay [LOS] in minutes, physician initial assessment [PIA], and left without being seen [LWBS]) were included in the review. Two reviewers independently conducted data extraction and assessed the citation relevance, inclusion, and study quality. For continuous outcomes, weighted mean differences (WMD) were calculated and reported with corresponding 95% confidence intervals (CIs). For dichotomous variables, individual and pooled statistics were calculated as relative risk (RR) with 95% CI. RESULTS: From 14,446 potentially relevant studies, 28 were included in the systematic review. Thirteen were journal publications, 12 were abstracts, and three were Web-based articles. Most studies employed before-after designs; 23 of the 28 studies were considered of weak quality. Based on the statistical pooling of data from two randomized controlled trials (RCTs), TLP resulted in shorter ED LOS compared to nurse-led triage (WMD = -36.85 min; 95% CI = -51.11 to -22.58). One of these RCTs showed a significant reduction in the PIA associated to TLP presence (WMD = -30.00 min; 95% CI = -56.91 to -3.09); the other RCT showed no change in LWBS due to a CI that included unity (RR = 0.82; 95% CI = 0.67 to 1.00). CONCLUSIONS: While the evidence summarized here suggests that to have a TLP is an effective intervention to mitigate the effects of ED overcrowding, due to the weak research methods identified, more research is required before its widespread implementation.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".