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. 2024 May 30:12:1351367.
doi: 10.3389/fpubh.2024.1351367. eCollection 2024.

Causal analysis of radiotherapy safety incidents based on a hybrid model of HFACS and Bayesian network

Haiping He #  1   2 , Xudong Peng #  1   2 , Dashuang Luo  2 , Weige Wei  2 , Jing Li  2 , Qiang Wang  1   2 , Qing Xiao  1   2 , Guangjun Li  1   2 , Sen Bai  1   2
Affiliations

Causal analysis of radiotherapy safety incidents based on a hybrid model of HFACS and Bayesian network

Haiping He et al. Front Public Health. .

Abstract

Objective: This research investigates the role of human factors of all hierarchical levels in radiotherapy safety incidents and examines their interconnections.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Bayesian network; human factors; human factors analysis and classification system; patient safety; radiotherapy incidents.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Human factors analysis and classification system framework.
Figure 2
Figure 2
Results of qualitative analysis of incidents based on HFACS. (A) Frequency distribution of individual human factor subcategories involved in 650 incidents. (B) Frequency distribution of the number of human factors contained in a single incident.
Figure 3
Figure 3
BN-HFACS network for analyzing human factors after the case-learning process. Based on this network, we can know the prior probability of different levels of human factors in radiotherapy safety incidents. For each factor, the greater the proportion of “yes,” the greater the a priori probability of that human factor.
Figure 4
Figure 4
Change in probability of each node when there is a change in the state of resource management. (A) Assume a 100% probability of resource management issues being present in radiotherapy incidents. (B) Assume radiotherapy incidents are not at all relevant to resource management.
Figure 5
Figure 5
Sensitivity of overall errors and violations to upper-level factors, columns indicate cause factors, and rows indicate the degree of sensitivity. Size of the area of the circle reflects the degree of sensitivity.
See this image and copyright information in PMC

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Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (grant numbers 81972848 and 12205209) and the Sichuan Science and Technology Program (grant number 2021YFS0143).