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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".