The impact of a fast track area on quality and effectiveness outcomes: A Middle Eastern emergency department perspective
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) overcrowding is a ubiquitous problem with serious public health implications. The fast track area is a novel method which aims to reduce waiting time, patient dissatisfaction and morbidity. |The study objective was to determine the impact of a fast track area (FTA) on both effectiveness measures (i.e. waiting times [WT] and length of stay [LOS]) and quality measures (i.e. LWBS rates and mortality rates) in non-urgent patients. The secondary objective was to assess if a FTA negatively impacted on urgent patients entering the ED. METHODS: The study took place in a 500 bed, urban, tertiary care hospital in Abu Dhabi, United Arab Emirates. This was a quasi-experimental, which examined the impact of a FTA on a pre-intervention control group (January 2005) (n = 4,779) versus a post-intervention study group (January 2006) (n = 5,706). RESULTS: Mean WTs of Canadian Triage Acuity Scale (CTAS) 4 patients decreased by 22 min (95% CI 21 min to 24 min, P < 0.001). Similarly, mean WTs of CTAS 5 patients decreased by 28 min (95% CI 19 min to 37 min, P < 0.001) post FTA. The mean WTs of urgent patients (CTAS 2/3) were also significantly reduced after the FTA was opened (P < 0.001). The LWBS rate was reduced from 4.7% to 0.7% (95% CI 3.37 to 4.64; P < 0.001). Opening a FTA had no significant impact on mortality rates (P = 0.88). CONCLUSION: The FTA improved ED effectiveness (WTs and LOS) and quality measures (LWBS rates) whereas mortality rate remained unchanged.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".