Implementation of an acute care emergency surgical service: a cost analysis from the surgeon’s perspective
Bibliographic record
Abstract
BACKGROUND: Acute care surgical services provide comprehensive emergency general surgical care while potentially using health care resources more efficiently. We assessed the volume and distribution of emergency general surgery (EGS) procedures before and after the implementation of the Acute Care and Emergency Surgery Service (ACCESS) at a Canadian tertiary care hospital and its effect on surgeon billings. METHODS: This single-centre retrospective case-control study compared adult patients who underwent EGS procedures between July and December 2009 (pre-ACCESS), to those who had surgery between July and December 2010 (post-ACCESS). Case distribution was compared between day (7 am to 3 pm), evening (3 pm to 11 pm) and night (11 pm to 7 am). Frequencies were compared using the χ(2) test. RESULTS: Pre-ACCESS, 366 EGS procedures were performed: 24% during the day, 55% in the evening and 21% at night. Post-ACCESS, 463 operations were performed: 55% during the day, 36% in the evening and 9% at night. Reductions in night-time and evening EGS were 57% and 36% respectively (p < 0.001). Total surgeon billings for operations pre- and post-ACCESS were $281 066 and $287 075, respectively: remuneration was $6008 higher post-ACCESS for an additional 97 cases (p = 0.003). Using cost-modelling analysis, post-ACCESS surgeon billing for appendectomies, segmental colectomies, laparotomies and cholecystectomies all declined by $67 190, $125 215, $66 362, and $84 913, respectively (p < 0.001). CONCLUSION: Acute care surgical services have dramatically shifted EGS from nighttime to daytime. Cost-modelling analysis demonstrates that these services have cost-savings potential for the health care system without reducing overall surgeon billing.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".