Developing in situ large‐scale simulation strategies for enhanced patient safety
Bibliographic record
Abstract
The transition to a new emergency department (ED) facility can pose significant challenges to patient safety. This study utilized Colman et al.'s simulation-based clinical systems testing approach to identify latent safety threats (LSTs), ensure operational readiness, and enhance staff confidence in a newly constructed ED at an urban hospital. A three-stage framework comprising development, implementation, and evaluation phases was employed. A large-scale "day in a life" in situ simulation was conducted to test system integration and identify LSTs. Data from participants, observers, and facilitators were collected and analyzed to develop action plans. The simulation included 63 scenarios over 4 h, engaging 125 participants and 50 standardized patients. A total of 113 LSTs were identified, leading to the development of a detailed action plan. Feedback from staff was positive, with participants reporting increased confidence in providing safe patient care in the new facility. This approach successfully identified safety threats and enhanced staff preparedness, potentially informing future operational plans for transitions in healthcare facilities. The methodology and findings are generalizable to other healthcare facilities undergoing similar transitions, where system integration, safety evaluation, and staff readiness are key concerns.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".