Probabilistic fault tree analysis of a radiation treatment system.
Inappropriate administration of radiation for cancer treatment can result in severe consequences such as premature death or appreciably impaired quality of life. There has been little study of vulnerable treatment process components and their contribution to the risk of radiation treatment (RT). In this article, we describe the application of probabilistic fault tree methods to assess the probability of radiation misadministration to patients at a large cancer treatment center. We conducted a systematic analysis of the RT process that identified four process domains: Assessment, Preparation, Treatment, and Follow-up. For the Preparation domain, we analyzed possible incident scenarios via fault trees. For each task, we also identified existing quality control measures. To populate the fault trees we used subjective probabilities from experts and compared results with incident report data. Both the fault tree and the incident report analysis revealed simulation tasks to be most prone to incidents, and the treatment prescription task to be least prone to incidents. The probability of a Preparation domain incident was estimated to be in the range of 0.1-0.7% based on incident reports, which is comparable to the mean value of 0.4% from the fault tree analysis using probabilities from the expert elicitation exercise. In conclusion, an analysis of part of the RT system using a fault tree populated with subjective probabilities from experts was useful in identifying vulnerable components of the system, and provided quantitative data for risk management.
Ekaette
Edidiong
E
University of Calgary, Department of Community Health Sciences, Calgary, Alberta, Canada.
Lee
Robert C
RC
Cooke
David L
DL
Iftody
Sandra
S
Craighead
Peter
P
D000416:Alberta; D003663:Decision Trees; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000532:radiotherapy; D011786:Quality Control; D011878:Radiotherapy / Q000009:adverse effects* / Q000592:standards; D018570:Risk Assessment / Q000379:methods* / Q000706:statistics & numerical data; D012308:Risk Management