Impact of a regional acute care surgery model on patient access and outcomes
Bibliographic record
Abstract
BACKGROUND: The consolidation of acute care surgery (ACS) services at 3 of 6 hospitals in a Canadian health region sought to alleviate a relative shortage of surgeons able to take emergency call. We examined how this affected patient access and outcomes. METHODS: Using the generalized linear model and statistical process control, we analyzed ACS-related episodes that occurred between 39 months prior to and 17 months after the model's implementation (n = 14,713). RESULTS: Time to surgery increased after the consolidation. Wait times increased primarily for patients presenting at nonreferral hospitals who were likely to require transfer to a referral hospital. Although ACS teams enabled referral hospitals to handle a much higher volume of patients without increasing within-hospital wait times, overall system wait times were lengthened by the growing frequency of patient transfers. Wait times for inpatient admission were difficult to interpret because there was a trend toward admitting patients directly to the ACS service, bypassing the emergency department (ED). For patients who did go through the ED, wait times for inpatient admission increased after the consolidation; however, this trend was cancelled out by the apparently zero waits of patients who bypassed the ED. Regionalization showed no impact on length of stay, readmissions, mortality or complications. CONCLUSION: Consolidation enabled the region to ensure adequate surgical coverage without harming patients. The need to transfer patients who presented at nonreferral hospitals led to longer waits.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".