Skrisovska Tamara, Schwarz Daniel, Kosinova Martina, Stourac Petr
Faculty of Medicine, Department of Simulation Medicine, Masaryk University, Brno, Czech Republic.
University Hospital Brno and Faculty of Medicine, Department of Pediatric Anesthesiology and Intensive Care Medicine, Masaryk University, Brno, Czech Republic.
PLoS One. 2025 Jan 17;20(1):e0317128. doi: 10.1371/journal.pone.0317128. eCollection 2025.
This study aims to provide an updated overview of medical error taxonomies by building on a robust review conducted in 2011. It seeks to identify the key characteristics of the most suitable taxonomy for use in high-fidelity simulation-based postgraduate courses in Critical Care. While many taxonomies are available, none seem to be explicitly designed for the unique context of healthcare simulation-based education, in which errors are regarded as essential learning opportunities. Rather than creating a new classification system, this study proposes integrating existing taxonomies to enhance their applicability in simulation training. Through data from surveys of participants and tutors in postgraduate simulation-based courses, this study provides an exploratory analysis of whether a generic or domain-specific taxonomy is more suitable for healthcare education. While a generic classification may cover a broad spectrum of errors, a domain-specific approach could be more relatable and practical for healthcare professionals in a given domain, potentially improving error-reporting rates. Seven strong links were identified in the reviewed classification systems. These correlations allowed the authors to propose various simulation training strategies to address the errors identified in both the classification systems. This approach focuses on error management and fostering a safety culture, aiming to reduce communication-related errors by introducing the principles of Crisis Resource Management, effective communication methods, and overall teamwork improvement. The gathered data contributes to a better understanding and training of the most prevalent medical errors, with significant correlations found between different medical error taxonomies, suggesting that addressing one can positively impact others. The study highlights the importance of simulation-based education in healthcare for error management and analysis.
本研究旨在以2011年进行的一项全面综述为基础，提供医学错误分类法的最新概述。它试图确定最适合用于重症监护研究生高保真模拟课程的分类法的关键特征。虽然有许多分类法可供使用，但似乎没有一种是专门为基于医疗模拟的教育这一独特背景设计的，在这种背景下，错误被视为重要的学习机会。本研究不是创建一个新的分类系统，而是提议整合现有分类法，以提高其在模拟训练中的适用性。通过对基于模拟的研究生课程参与者和导师的调查数据，本研究对通用分类法或特定领域分类法是否更适合医疗教育进行了探索性分析。虽然通用分类法可能涵盖广泛的错误，但特定领域的方法对于特定领域的医疗专业人员可能更具相关性和实用性，有可能提高错误报告率。在所审查的分类系统中确定了七个强关联。这些相关性使作者能够提出各种模拟训练策略，以解决分类系统中识别出的错误。这种方法侧重于错误管理和培养安全文化，旨在通过引入危机资源管理原则、有效的沟通方法和整体团队合作改进来减少与沟通相关的错误。收集到的数据有助于更好地理解和训练最常见的医疗错误，不同医学错误分类法之间存在显著相关性，这表明解决一个问题可以对其他问题产生积极影响。该研究强调了基于模拟的教育在医疗保健中进行错误管理和分析的重要性。
