The Impact of an Acute Care Emergency Surgical Service on Timely Surgical Decision-Making and Emergency Department Overcrowding
Bibliographic record
Abstract
BACKGROUND: This study evaluated how implementation of an acute care emergency surgery service (ACCESS) affected key determinants of emergency department (ED) length of stay, and particularly, surgical decision time. Also, we analyzed how ACCESS affected ED overcrowding. STUDY DESIGN: We conducted a before and after study of all ED patients referred to ACCESS from January 1, 2007 to June 30, 2009. ACCESS was implemented on July 1, 2008. The primary outcome was surgical decision time; the secondary outcome was a measure of overall ED overcrowding: "time-to-stretcher" for all ED patients. The control groups were patients referred to internal medicine or urology. Patients with appendicitis were studied in order to analyze the impact on patient outcomes and to determine barriers to efficient ED patient flow. RESULTS: Of 2,510 patients, 1,448 patients were pre-ACCESS, and 1,062 were after ACCESS implementation. Implementation of ACCESS was associated with a 15% reduction in surgical decision time (12.6 hours vs 10.8 hours, p < 0.01). During the same period, there were no significant changes in decision time for our control groups. Also, the mean time-to-stretcher for all ED patients decreased by 20%. In patients with appendicitis, we found that patient flow could be further improved by a timely request for surgical consultation and expedited imaging. Finally, we found that patients with nonperforated appendicitis with a fecalith on CT imaging were more likely to suffer perforation while waiting for surgery. CONCLUSIONS: ACCESS reduced surgical decision time for surgical patients. Also, ACCESS improved overall ED crowding, as measured by time-to-stretcher for ED patients. Further improvements could be made by improving time to imaging. Patients referred for nonperforated appendicitis with a fecalith on CT should have expedited surgery.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".