Impact of simulation training for teamwork and communication intervention on patient safety in the emergency department
Bibliographic record
Abstract
Background: The emergency department (ED) is the hospital division that is most vulnerable to poor communication and teamwork-based medical errors that affect patient safety. One approach to prevent medical errors is by training and assessing healthcare workers using simulation-based (SB) teamwork and communication training. This study aimed to explore the extent to which SB training could improve teamwork and communication and reduce preventable medical errors in the ED. Method: The study was conducted at the Ministry of National Guard Health Affairs, a tertiary care center with a 39-bed ED, in Saudi Arabia. A total of 123 ED healthcare staff participated in the study. This study adopted a single-subject experimental design with the intervention of simulation training in ED cases. The methodology focused on three domains: 1) patient safety in the ED, 2) inter-professional and multidisciplinary simulation team training, and 3) team dynamic enhancement use. Staff perceptions regarding teamwork and communication pre-and post-intervention and the relationship between changes in their perception of performance in the ED and patient outcomes. The chi-square test was used for univariate analysis, and a generalized linear regression model was adjusted for demographics in multivariate analysis. Results: Staff perceptions were collected from all multidisciplinary team members before and after simulation training. The data revealed a significant improvement in overall staff perceptions toward teamwork and communication (8% to 34%, p-value = 0.001). Conclusion: SB communication and teamwork training in the ED resulted in a sustained and measurable improvement in ED staff perceptions toward teamwork and communication. The results suggest that simulation sessions may improve staff perceptions and multidisciplinary team communication during critical situations in the ED.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".