The impact of senior doctor assessment at triage on emergency department performance measures: systematic review and meta-analysis of comparative studies
Bibliographic record
Abstract
STUDY QUESTION: To determine if placing a senior doctor at triage versus standard single nurse in a hospital emergency department (ED) improves ED performance by reviewing evidence from comparative design studies using several quality indicators. DESIGN: Systematic review. DATA SOURCES: Cochrane Library, MEDLINE, EMBASE, CINAHL, Cochrane Effective Practice and Organisation of Care (EPOC), Web of Science, Clinical Trials Registry website. In addition, references from included studies and citation searches were used to identify relevant studies. REVIEW METHODS: Databases were searched for comparative studies examining the role of senior doctor triage (SDT), published from 1994 to 2014. Senior doctor was defined as a qualified medical doctor who completed high specialty training in emergency medicine. Articles with a primary aim to investigate the effect of SDT on ED quality indicators such as waiting time (WT), length of stay (LOS), left without being seen (LWBS) and left without treatment complete (LWTC) were included. Articles examining the adverse events and cost associated with SDT were also included. Only studies with a control group, either in a randomised controlled trial (RCT) or in an observational study with historical controls, were included. The systematic literature search was followed by assessment of relevance and risk of bias in each individual study fulfilling the inclusion criteria using the Effective Public Health Practice Project (EPHPP) bias tool. Data extraction was based on a form designed and piloted by the authors for dichotomous and continuous data. DATA SYNTHESIS: Narrative synthesis and meta-analysis of homogenous data were performed. RESULTS: Of 4506 articles identified, 25 relevant studies were retrieved; 12 were of the weak pre-post study design, 9 were of moderate quality and 4 were of strong quality. The majority of the studies revealed improvements in ED performance measures favouring SDT. Pooled results from two Canadian RCTs showed a significant reduction in LOS of medium acuity patients (weighted means difference (WMD) -26.26 min, 95% CI -38.50 to -14.01). Another two RCTs revealed a significant reduction in WT (WMD -26.17 min, 95% CI -31.68 to -20.65). LWBS was reduced in two Canadian RCTs (risk ratio (RR)=0.79, 95% CI 0.66 to 0.94). This was echoed by the majority of pre-post study designs. SDT did not change the occurrence of adverse events. No clear benefit of SDT in terms of patient satisfaction or cost effectiveness could be identified. CONCLUSIONS: This review demonstrates that SDT can be an effective measure to enhance ED performance, although cost versus benefit analysis is needed. The potential high risk of bias in the evidence identified, however, mandates more robust multicentred studies to confirm these findings.
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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 | 0.038 | 0.113 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.035 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".