The Impact of Ambulance Diversion on EMS Resource Availability
Bibliographic record
Abstract
OBJECTIVE: Ambulance diversion has been proposed as a solution to emergency department overcrowding and waiting room deaths. For ethical and legal reasons, it remains highly controversial. The impact on EMS resources is not known. This study seeks to determine how diversion impacts the availability of ambulance resources, specifically transport time, hospital turnaround, and total out-of-service time. METHODS: All emergency ambulance responses in 2002 while one of the city's hospitals was on diversion were collected, including those responses during the hour of the diversion and 30 minutes before and after. The time intervals for these responses were time and date matched to 2001, if no hospital was on diversion. Total out-of-service time (911 to availability for another call), time from departure from scene to arrival at hospital (transport interval), and time from arrival at hospital to availability for another call (turnaround time) were compared by using a t-test. RESULTS: The 1,563 instances of diversion were included, with 1,403 controls. Interim analysis allowed calculation of a sample size of 1,049 in each group to show a 2-minute difference in turnaround time and 330 calls in each for a 5-minute difference in total out-of-service time (0.25 SD). Transport, hospital turnaround, and total out-of-service times were not different between diversion and control time periods. This relies on the accuracy of the status button system and may not generalize to systems with different geography, diversion policy, number of hospitals, or handling of interfacility transfers. CONCLUSION: The availability of EMS resources is maintained during times of ambulance diversion. Diversion avoids potential delays associated with sending ambulances to overwhelmed emergency departments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".