Wong Lawrence M, Pawlicki Todd
Department of Radiation Medicine & Applied Sciences, University of California San Diego, La Jolla, California, USA.
J Appl Clin Med Phys. 2025 Mar;26(3):e14623. doi: 10.1002/acm2.14623. Epub 2025 Feb 2.
This review article aims to provide an overview of accident models and incident analysis techniques in the context of radiation oncology. Accident models conceptualize the mechanisms through which accidents occur. Chain-of-event models and systemic models are two main categories of accident models and differ in how accident causation is portrayed. Chain-of-event models focus on the linear sequence of events leading up to an accident, whereas systemic models emphasize the nonlinear relationships between the components in a complex system. The article then introduces various incident analysis techniques, including root cause analysis (RCA), London Protocol, AcciMap, and Causal Analysis Based on Systems Theory (CAST), which are based on these accident models. The techniques based on the chain-of-event model can be effective in identifying causal factors, safety interventions, and improving safety. The other techniques based on the systemic models inherently facilitate an examination of how the influence of personal conditions, environmental conditions, and information exchange between different aspects of a system contributed to an accident. To improve incident analysis, it is essential to translate unsafe human behavior into decision-making flaws and the underlying contextual factors. Where resources allow, it is also crucial to systematically link frontline contributions to organizational and societal aspects of the system and incorporate expertise in safety science and human factors into the analysis team. The article also touches on related concepts such as Perrow's Normal Accident Theory (NAT), Functional Resonance Analysis Method (FRAM), and Bowtie Analysis, which are not based on specific accident models but have been used for safety improvement in radiation oncology. Overall, different incident analysis techniques have strengths and weaknesses. Taking a systems approach to incident analysis requires a shift from linear thinking to a more nuanced understanding of complex systems. However, the approach also brings unique value and can help improve safety as radiation oncology further gains complexity.
这篇综述文章旨在概述放射肿瘤学领域中的事故模型和事件分析技术。事故模型将事故发生的机制概念化。事件链模型和系统模型是事故模型的两个主要类别，它们在事故因果关系的描述方式上有所不同。事件链模型关注导致事故的事件线性序列，而系统模型强调复杂系统中各组件之间的非线性关系。然后，本文介绍了各种事件分析技术，包括基于这些事故模型的根本原因分析（RCA）、伦敦协议、事故地图（AcciMap）和基于系统理论的因果分析（CAST）。基于事件链模型的技术在识别因果因素、安全干预措施和提高安全性方面可能有效。基于系统模型的其他技术本质上有助于考察个人条件、环境条件以及系统不同方面之间的信息交换如何对事故产生影响。为了改进事件分析，将不安全的人类行为转化为决策缺陷和潜在的背景因素至关重要。在资源允许的情况下，将一线贡献与系统的组织和社会层面进行系统关联，并将安全科学和人为因素方面的专业知识纳入分析团队也至关重要。本文还涉及了相关概念，如佩罗的正常事故理论（NAT）、功能共振分析方法（FRAM）和蝴蝶结分析，这些并非基于特定的事故模型，但已用于放射肿瘤学的安全改进。总体而言，不同的事件分析技术各有优缺点。采用系统方法进行事件分析需要从线性思维转向对复杂系统更细致入微的理解。然而，这种方法也带来了独特的价值，并且随着放射肿瘤学的进一步复杂化，有助于提高安全性。
