Stress Testing the Resuscitation Room: Latent Threats to Patient Safety Identified During Interprofessional In Situ Simulation in a Canadian Academic Emergency Department
Bibliographic record
Abstract
OBJECTIVES: Emergency department (ED) resuscitation is a complex, high-stakes procedure where positive outcomes depend on effective interactions between the health care team, the patient, and the environment. Resuscitation teams work in dynamic environments and strive to ensure the timely delivery of necessary treatments, equipment, and skill sets when required. However, systemic failures in this environment cannot always be adequately anticipated, which exposes patients to opportunities for harm. METHODS: As part of a new interprofessional education and quality improvement initiative, this prospective, observational study sought to characterize latent safety threats (LSTs) identified during the delivery of in situ, simulated resuscitations in our ED. In situ simulation (ISS) sessions were delivered on a monthly basis in the EDs at each campus of a large tertiary care academic hospital system, during which a variety of scenarios were run with teams of ED health care professionals. LSTs were identified by simulation facilitators and participants during the case and debriefing and then grouped thematically for analysis. RESULTS: During the study period, 22 ISS sessions were delivered, involving 58 cases and reaching 383 ED health care professionals. 196 latent safety threats were identified through these sessions (mean = 3.4 LSTs per case) of which 110 were determined to be "actionable" at a system level. LSTs identified included system/environmental design flaws, equipment problems, failures in department processes, and knowledge/skill gaps. Corrective mechanisms were initiated in 85% of actionable cases. CONCLUSIONS: Effective quality improvement and continuing education programs are essential to translate these findings into more resilient patient care. ISS, beyond its role as a training tool for developing intrinsic and crisis resource management skills, can be effectively used to identify system issues in the ED that could expose critically ill patients to harm.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".