Specialized fast track: a sustainable model to improve emergency department patient flow
Bibliographic record
Abstract
Objective: Emergency department (ED) overcrowding is a hospital-wide problem that demands a whole-hospital solution. We developed and implemented a fast track model for streaming ED patients with low-acuity illness or injury to specialized care areas (gynecology-obstetrics, orthopedics-trauma, pediatrics, and primary care) staffed by existing specialist resources with access to general ED services. The study aim was to determine whether streaming of ED visits into specialized fast track areas increased operational efficiency and improved patient flow in a mixed adult and pediatric ED without incurring extra costs.Methods: We retrospectively reviewed the ED discharge records of patients who were mainstreamed or fast tracked during the 3-year period from 1 January 2010 through 31 December 2012. ED visits were identified according to a five-level triage scheme; performance indicators were compared for: wait time, length of stay, leave before being seen and revisit rates.Results: A reduction in wait time, length of stay, and leave before being seen rate was seen with fast track streaming (p < .01). These improvements were achieved without additional medical and nurse staffing.Conclusions: Specialized fast track streaming helped us meet patients’ care needs and contain costs. Lower-acuity patients were seen quickly by a specialist and safely discharged or admitted to the hospital without diverting resources from patients with high-acuity illness or injury. Involvement of all stakeholders in seeking a sustainable solution to ED crowding as a hospital-wide problem was key to enhancing cooperation between the ED and the hospital units.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".