PDF(Pubmed)
Abstract：
UNASSIGNED: This research investigates the role of human factors of all hierarchical levels in radiotherapy safety incidents and examines their interconnections.
UNASSIGNED: Utilizing the human factor analysis and classification system (HFACS) and Bayesian network (BN) methodologies, we created a BN-HFACS model to comprehensively analyze human factors, integrating the hierarchical structure. We examined 81 radiotherapy incidents from the radiation oncology incident learning system (RO-ILS), conducting a qualitative analysis using HFACS. Subsequently, parametric learning was applied to the derived data, and the prior probabilities of human factors were calculated at each BN-HFACS model level. Finally, a sensitivity analysis was conducted to identify the human factors with the greatest influence on unsafe acts.
UNASSIGNED: The majority of safety incidents reported on RO-ILS were traced back to the treatment planning phase, with skill errors and habitual violations being the primary unsafe acts causing these incidents. The sensitivity analysis highlighted that the condition of the operators, personnel factors, and environmental factors significantly influenced the occurrence of incidents. Additionally, it underscored the importance of organizational climate and organizational process in triggering unsafe acts.
UNASSIGNED: Our findings suggest a strong association between upper-level human factors and unsafe acts among radiotherapy incidents in RO-ILS. To enhance radiation therapy safety and reduce incidents, interventions targeting these key factors are recommended.
UNASSIGNED: Utilizing the human factor analysis and classification system (HFACS) and Bayesian network (BN) methodologies, we created a BN-HFACS model to comprehensively analyze human factors, integrating the hierarchical structure. We examined 81 radiotherapy incidents from the radiation oncology incident learning system (RO-ILS), conducting a qualitative analysis using HFACS. Subsequently, parametric learning was applied to the derived data, and the prior probabilities of human factors were calculated at each BN-HFACS model level. Finally, a sensitivity analysis was conducted to identify the human factors with the greatest influence on unsafe acts.
UNASSIGNED: The majority of safety incidents reported on RO-ILS were traced back to the treatment planning phase, with skill errors and habitual violations being the primary unsafe acts causing these incidents. The sensitivity analysis highlighted that the condition of the operators, personnel factors, and environmental factors significantly influenced the occurrence of incidents. Additionally, it underscored the importance of organizational climate and organizational process in triggering unsafe acts.
UNASSIGNED: Our findings suggest a strong association between upper-level human factors and unsafe acts among radiotherapy incidents in RO-ILS. To enhance radiation therapy safety and reduce incidents, interventions targeting these key factors are recommended.
摘要：
■这项研究调查了所有等级级别的人为因素在放射治疗安全事件中的作用，并检查了它们之间的相互联系。
■利用人为因素分析和分类系统（HFACS）和贝叶斯网络（BN）方法，我们创建了一个BN-HFACS模型来全面分析人为因素，整合层次结构。我们检查了来自放射肿瘤学事件学习系统（RO-ILS）的81个放射治疗事件，使用HFACS进行定性分析。随后，将参数学习应用于派生数据，在每个BN-HFACS模型水平上计算人为因素的先验概率。最后,进行了敏感性分析，以确定对不安全行为影响最大的人为因素。
■RO-ILS报告的大多数安全事件可以追溯到治疗计划阶段，技能错误和习惯性违规是导致这些事件的主要不安全行为。敏感性分析强调了操作者的状况，人员因素,环境因素对事件的发生有显著影响。此外,它强调了组织气氛和组织过程在引发不安全行为方面的重要性。
■我们的研究结果表明，在RO-ILS放疗事件中，高层人为因素与不安全行为之间存在很强的关联。为了提高放射治疗的安全性和减少事故，建议采取针对这些关键因素的干预措施。
■利用人为因素分析和分类系统（HFACS）和贝叶斯网络（BN）方法，我们创建了一个BN-HFACS模型来全面分析人为因素，整合层次结构。我们检查了来自放射肿瘤学事件学习系统（RO-ILS）的81个放射治疗事件，使用HFACS进行定性分析。随后，将参数学习应用于派生数据，在每个BN-HFACS模型水平上计算人为因素的先验概率。最后,进行了敏感性分析，以确定对不安全行为影响最大的人为因素。
■RO-ILS报告的大多数安全事件可以追溯到治疗计划阶段，技能错误和习惯性违规是导致这些事件的主要不安全行为。敏感性分析强调了操作者的状况，人员因素,环境因素对事件的发生有显著影响。此外,它强调了组织气氛和组织过程在引发不安全行为方面的重要性。
■我们的研究结果表明，在RO-ILS放疗事件中，高层人为因素与不安全行为之间存在很强的关联。为了提高放射治疗的安全性和减少事故，建议采取针对这些关键因素的干预措施。
