Fuzzy Inference Based FMEA for MR Image Based HDR Brachytherapy

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Purpose/Objective(s)MR image based HDR brachytherapy is a complex, time-constrained and resource-intensive procedure requiring significant coordination and cooperation of multi-departmental efforts. Failure modes and effects analysis (FMEA) is an effective risk evaluation tool for identifying all possible failure modes (FMs) that could potentially impact negatively on the patient's treatment. Given the complexity of the MR image based HDR brachytherapy clinical workflow, FMEA can play a critical role in improving quality measures for each task. However, assigning a value between 1 to 10 to each of risk factor in traditional FMEA is a subjective and challenging task for the relevant multi-disciplinary team and potentially can cause considerable discrepancy and inconsistency. In this study, we introduced fuzzy inference based FMEA to provide better understanding of FMs to improve the quality assurance program for an efficient workflow and improved patient safety.Materials/MethodsA total of four major sub-processes and forty-five FMs were identified from “applicator insertion” to “treatment delivery”. The fuzzy inference rule-based model uses three inputs of risk factors (severity, occurrence, and detectability) and single output of fuzzy risk priority number (RPN) consisting of five fuzzy linguistic terms (very low, low, medium, high, and very high) of triangular membership functions. This rule-based model is generated based on a set of fuzzy if-then rules by decision makers that relate input to output variables. Then, the defuzzification method was used to obtain a fuzzy RPN value.ResultsThe team identified top three FMs of traditional FMEA. These were incorrect insertion of applicators by wrong identification ultrasound track, applicator moves, and incorrect channel length. Different rankings were produced depending on the rules of decision maker in fuzzy inference FMEA. Top 3 FMs of fuzzy FMEA were identified as missed/not detecting errors during physics second check, wrong applicator reconstruction, and poor quality of MR image set. The differences between the two RPN rankings are due to rules of decision making considering the probability of detecting errors in later process and severity.ConclusionThis is the first report for fuzzy inference based FMEA on MRI based HDR brachytherapy. The fuzzy FMEA model exhibits desirable properties that help overcome the drawbacks of the traditional FMEA and RPN, especially when dealing with multi-disciplinary team practice where their priorities on FMs are different. The results have been shared with team members, and mitigation plans are to be implemented for enhancing quality of care and improving patient safety. MR image based HDR brachytherapy is a complex, time-constrained and resource-intensive procedure requiring significant coordination and cooperation of multi-departmental efforts. Failure modes and effects analysis (FMEA) is an effective risk evaluation tool for identifying all possible failure modes (FMs) that could potentially impact negatively on the patient's treatment. Given the complexity of the MR image based HDR brachytherapy clinical workflow, FMEA can play a critical role in improving quality measures for each task. However, assigning a value between 1 to 10 to each of risk factor in traditional FMEA is a subjective and challenging task for the relevant multi-disciplinary team and potentially can cause considerable discrepancy and inconsistency. In this study, we introduced fuzzy inference based FMEA to provide better understanding of FMs to improve the quality assurance program for an efficient workflow and improved patient safety. A total of four major sub-processes and forty-five FMs were identified from “applicator insertion” to “treatment delivery”. The fuzzy inference rule-based model uses three inputs of risk factors (severity, occurrence, and detectability) and single output of fuzzy risk priority number (RPN) consisting of five fuzzy linguistic terms (very low, low, medium, high, and very high) of triangular membership functions. This rule-based model is generated based on a set of fuzzy if-then rules by decision makers that relate input to output variables. Then, the defuzzification method was used to obtain a fuzzy RPN value. The team identified top three FMs of traditional FMEA. These were incorrect insertion of applicators by wrong identification ultrasound track, applicator moves, and incorrect channel length. Different rankings were produced depending on the rules of decision maker in fuzzy inference FMEA. Top 3 FMs of fuzzy FMEA were identified as missed/not detecting errors during physics second check, wrong applicator reconstruction, and poor quality of MR image set. The differences between the two RPN rankings are due to rules of decision making considering the probability of detecting errors in later process and severity. This is the first report for fuzzy inference based FMEA on MRI based HDR brachytherapy. The fuzzy FMEA model exhibits desirable properties that help overcome the drawbacks of the traditional FMEA and RPN, especially when dealing with multi-disciplinary team practice where their priorities on FMs are different. The results have been shared with team members, and mitigation plans are to be implemented for enhancing quality of care and improving patient safety.