Morning report decreases length of stay in emergency general surgery patients
Bibliographic record
Abstract
Objective: Communication in the hospital setting is an easy target for quality improvement. Capturing this change via communication between providers during hand-offs is necessary to reduce delays and errors. While this process has been more widely characterized in medical specialties, we designed this study to address the knowledge gap in surgical specialties.Methods: Our institution’s division of Acute Care Surgery (ACS) implemented Morning Report (MR) in October of 2015. At MR, all admissions and service transfers were discussed from Trauma, Emergency General Surgery (EGS), and Surgical Critical Care services from the previous 24 hours. This study compared patients who underwent a surgical procedure during their hospital stay before and after protocol implementation.Results: 974 patients were included in this study. The average patient was 50.3 years of age, 65.4% were white, and 51.7% were male. The average length of stay (LOS) was 8.3 days with 1.75 days to procedure. The post-MR cohort LOS was 2.7 shorter and had 0.85 fewer days to procedure. In an adjusted regression analysis, days to procedure and LOS decreased by 33% (p < .01) and 17% (p < .01) respectively.Conclusions: Implementation of MR led to a decrease in the overall LOS and days to procedure for operative patients. Our results advocate for the standard use of structured hand-offs in surgical units.
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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 | 0.001 | 0.017 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".