How does fast track affect quality of care in the emergency department?
Bibliographic record
Abstract
STUDY OBJECTIVES: Use of fast track has been shown to improve the emergency department flow of less urgent patients. It has been speculated, however, that this could negatively affect the care of urgent patients. The objective of this study was to determine whether a dedicated fast track for less urgent patients [Canadian Triage and Acuity scale category 4/5 (CTAS 4/5)] affected (1) the time to assessment for urgent patients (CTAS 3), (2) the length of stay for less urgent patients (CTAS 4 and 5), and (3) the left-without-being-seen rate. METHODS: In June 2003, fast track was opened in our emergency department from 13:00 to 19:00 h. A before-after intervention comparison analysis was completed for 1 week in Aug 2002 and the same week in Aug 2003. Data collected included (1) time to assessment of CTAS 3 patients, (2) the length of stay for CTAS 4/5 patients, and (3) percentage of patients who left without being seen. RESULTS: A total of 368 patients were reviewed for 2002 and 380 patients were reviewed for 2003. Median time to assessment of CTAS 3 patients presenting from 13:00 to 19:00 h was reduced from 66 min (Interquartile range: 40, 94 min) in 2002 to 60 min (IQR: 38, 108 min) after fast track was open in 2003 (P = 0.95). Median length of stay of CTAS 4 and 5 patients was reduced from 170 min (IQR: 111, 256 min) to 110 min (IQR: 69, 185 min) (P < 0.001). The overall left-without-being-seen rate decreased from 5% (20/368) to 2% (9/380). CONCLUSION: A dedicated fast track for CTAS 4/5 patients can reduce the length of stay and the left-without-being-seen rate with no impact on CTAS 3 patients seen in the main emergency department.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".