Exploring medical error taxonomies and human factors in simulation-based healthcare education - Tamara Skrisovska; Daniel Schwarz; Martina Kosinova; Petr Stourac (PLoS ONE, 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.