Goals, Recommendations, and the How-To Strategies for Developing and Facilitating Patient Safety and System Integration Simulations
Bibliographic record
Abstract
PURPOSE: The aim of this article is to outline overall goals, recommendations, and provide practical How-To strategies for developing and facilitating patient safety and system integration (PSSI) simulations for healthcare team members and organizations. BACKGROUND: Simulation is increasingly being used as a quality improvement tool to better understand the tasks, environments, and processes that support the delivery of healthcare services. These PSSI simulations paired with system-focused debriefing can occur prior to implementing a new process or workflow to proactively identify system issues. They occur as part of a continuous cycle of quality improvement and have unique considerations for planning, implementation, and delivery of healthcare. METHOD: The Delphi technique was used to develop the recommendations and How-To strategies to guide those interested in conducting a PSSI simulations. The Delphi technique is a structured communication technique and systematic process of gathering information from a group of identified experts through a series of questionnaires to gain consensus regarding judgments on complex processes, where precise information is not available in the literature. The Delphi technique permitted an iterative and multistaged approach to transform expert opinions into group consensus. RESULTS: The goals, recommendations, and How-To strategies include a focus on project management, stakeholder engagement, sponsorship, scenario design, prebriefing and debriefing, and evaluation metrics. The intent is to proactively identify system issues and disseminate actionable findings. CONCLUSIONS: This article highlights salient features to consider when using simulation as a strategy and tool for patient safety and quality improvement.
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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 | 0.097 | 0.121 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".