Five Topics Health Care Simulation Can Address to Improve Patient Safety: Results From a Consensus Process
Bibliographic record
Abstract
OBJECTIVES: There is little knowledge about which elements of health care simulation are most effective in improving patient safety. When empirical evidence is lacking, a consensus statement can help define priorities in, for example, education and research. A consensus process was therefore initiated to define priorities in health care simulation that contribute the most to improve patient safety. METHODS: An international group of experts took part in a 4-stage consensus process based on a modified nominal group technique. Stages 1 to 3 were based on electronic communication; stage 4 was a 2-day consensus meeting at the Utstein Abbey in Norway. The goals of stage 4 were to agree on the top 5 topics in health care simulation that contribute the most to patient safety, identify the patient safety problems they relate to, and suggest solutions with implementation strategies for these problems. RESULTS: The expert group agreed on the following topics: technical skills, nontechnical skills, system probing, assessment, and effectiveness. For each topic, 5 patient safety problems were suggested that each topic might contribute to solve. Solutions to these problems and implementation strategies for these solutions were identified for technical skills, nontechnical skills, and system probing. In the case of assessment and effectiveness, the expert group found it difficult to suggest solutions and implementation strategies mainly because of lacking consensus on metrics and methodology. CONCLUSIONS: The expert group recommends that the 5 topics identified in this consensus process should be the main focus when health care simulation is implemented in patient safety curricula.
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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 | 0.337 | 0.414 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".