Emergency Room Safer Transfer of Patients (ER-STOP): a quality improvement initiative at a community-based hospital to improve the safety of emergency room patient handovers
Bibliographic record
Abstract
OBJECTIVES: Ensure early identification and timely management of patient deterioration as essential components of safe effective healthcare. Prompted by analyses of incident reports and deterioration events, a multicomponent organisational rescue from danger system was redesigned to decrease unexpected inpatient deterioration. DESIGN: Quality improvement before-after unblinded trial. SETTING: 430-bed Canadian community teaching hospital. PARTICIPANTS: All admitted adult medical-surgical patients in a before-after 12-month interventional study. INTERVENTION: Locally validated checklist (Modified Early Warning Score+urinary catheter in situ+nurse concern) with an intentional pause and explicit management options was deployed as a modification of an existing ward transfer of accountability fax report in the emergency department (ED). RESULTS: Following deployment of Emergency Room Safer Transfer of Patients (ER-STOP), the risk of an unexpected CCRT (critical care response team) response within 24 hours of admission from ED to adult medical and surgical wards was significantly decreased (OR 4.1, 95% CI 2.17 to 7.77). Mean (±SD) ED wait times (5.66±1.54vs 5.74±1.04 hours, p=0.30), intensive care unit admission rate (3.84%, n=233vs 4.61%, n=278, p=0.06) and cardiac care unit admission rate (9.51%, n=577vs 9.60%, n=579, p=0.198) were unchanged. CONCLUSIONS: ER-STOP improvement was out of proportion to the predictive value of the checklist component suggesting that effectiveness of this low-cost sustainable tool was related to increased situational awareness, empowering a culture of patient safety and repurposing of an adjacent ED medical short-stay unit use. Local adaptation within existing processes is essential to successful safety outcomes.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".