Refining mass casualty plans with simulation-based iterative learning

doi:10.1016/j.bja.2021.10.004

Refining mass casualty plans with simulation-based iterative learning

2022-02-01

7

OA

AI

DOI：10.1016/j.bja.2021.10.004

原文链接

查看原文

摘要

En 中文

Background: Preparatory, written plans for mass casualty incidents are designed to help hospitals deliver an effective response. However, addressing the frequently observed mismatch between planning and delivery of effective responses to mass casualty incidents is a key challenge. We aimed to use simulation-based iterative learning to bridge this gap. Methods: We used Normalisation Process Theory as the framework for iterative learning from mass casualty incident simulations. Five small-scale 'focused response' simulations generated learning points that were fed into two large-scale whole-hospital response simulations. Debrief notes were used to improve the written plans iteratively. Anonymised individual online staff surveys tracked learning. The primary outcome was system safety and latent errors identified from group debriefs. The secondary outcomes were the proportion of completed surveys, confirmation of reporting location, and respective roles for mass casualty incidents. Results: Seven simulation exercises involving more than 700 staff and multidisciplinary responses were completed with debriefs. Usual emergency care was not affected by simulations. Each simulation identified latent errors and system safety issues, including overly complex processes, utilisation of space, and the need for clarifying roles. After the second whole hospital simulation, participants were more likely to return completed surveys (odds ratio=2.7; 95% confidence interval [CI], 1.7-4.3). Repeated exercises resulted in respondents being more likely to know where to report (odds ratio=4.3; 95% CI, 2.5-7.3) and their respective roles (odds ratio=3.7; 95% CI, 2.2-6.1) after a simulated mass casualty incident was declared. Conclusion: Simulation exercises are a useful tool to improve mass casualty incident plans iteratively and continuously through hospital-wide engagement of staff.

Keyword:

iterative improvement

low fidelity

mass casualty plans

normalisation process theory

simulation

staff engagement

Refining mass casualty plans with simulation-based iterative learning

摘要

AI总结

期刊

机构

引用论文