Matching Capacity to Demand: A Regional Dashboard Reduces Ambulance Avoidance and Improves Accessibility of Receiving Hospitals
Bibliographic record
Abstract
OBJECTIVES: ambulance diversion is a dangerous repercussion of emergency department (ED) crowding and can reflect fragmentation and a lack of coordination in designating optimal patient offload sites for prehospital providers. The objective of this study was to evaluate whether proactive destination selection through the Regional Emergency Patient Access and Coordination (REPAC) program would enhance capacity and ED flow management. METHODS: the REPAC system provides a dashboard that synthesizes real-time capacity and acuity data for all three adult EDs in the city of Calgary, assigning a color code to reflect receiving status. It assigns destination for the next patient transported by emergency medical services (EMS) by categorizing ED sites as having either a favorable (green/yellow) status or unfavorable (orange/red) status. Three time windows were analyzed: a 6-month window prior to REPAC implementation (pre), the first 6-month window immediately following (post1), and the second 6-month period following (post2). Primary outcomes of interest were the proportion of time spent in favorable versus unfavorable status and EMS avoidances for all adult ED sites in the region (percentage of total time with any center on EMS bypass). Information on total number of ED visits, percentage of patients arriving by EMS transports, admission rates, patient acuity (Canadian Triage and Acuity Score), age, and length of stay (LOS) for admitted and discharged patients was collected. The Kruskal-Wallis test was employed for primary outcome analysis. RESULTS: implementation of the REPAC system resulted in an increase in the proportion of total time region hospitals reported favorable status (57.5% vs. 64.1%) pre versus post1, an effect that was accentuated at 1 year (post2, 78.7%; p < 0.001 for both comparisons). There was a concomitant decrease in EMS avoidances as a result of the REPAC system, 4.4% to 1.8% (pre vs. post1), also further improved at 1 year to 0.6% (p < 0.001 for both comparisons). CONCLUSIONS: proactive EMS destination selection through a real-time integrated electronic surveillance system enhances regional capacity and flow management while significantly reducing ambulance diversions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".