Application of failure mode and effects analysis to optimization of linac quality controls protocol

Research output: Contribution to journalArticle

Abstract

Purpose: To apply Failure Mode and Effects Analysis (FMEA) to optimize linac quality control (QC) protocol in order to ensure patient safety and treatment quality, taking maximum advantage of the available resources. Material and methods: Each parameter tested by the QC was considered as a potential failure mode (FM). For each FM, likelihood of occurrence (O), severity of effect (S), and lack of detectability (D) were evaluated and corresponding Risk Priority Number (RPN) was calculated from the product of three indexes. The scores were assigned using two methods: (a) A survey submitted to the medical physicists; (b) A semi-quantitative analysis (SQA) performed through: simulation of FMs in the treatment planning system; studies reported in literature; results obtained by the QC data analysis. A weighted RPN for all FMs was calculated taking into account both the methods. For each linac, the tests were then sorted by their frequency and the RPN value. Results: A high variability was found in the scores of the survey, although in many it was reduced in RPN values, highlighting the more relevant tests as on beam output and imaging system. Integrating these results with those obtained by SQA, the RPN-based ranking of tests has been provided considering the specific use of the accelerator: for example, more accurate tests on dose modulation and multileaf collimator speed were required in linacs where intensity-modulated treatment is performed, while, more specific tests on couch and jaw position indicators were necessary where treatments with multiple isocenters and/or junctions between adjacent fields were often delivered. Conclusions: Failure Mode and Effects Analysis is a useful tool to prioritize the linac QCs, taking into account the specific equipment and clinical practice. The integration of SQA and survey results reduces subjectivity of the FMEA scoring and allows to optimize linac QCs without “losing” the expertise and experience of medical physicists and clinical staff.

Original languageEnglish
Pages (from-to)2541-2555
Number of pages15
JournalMedical Physics
Volume46
Issue number6
DOIs
Publication statusPublished - Jun 2019

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Quality Control
Patient Safety
Jaw
Healthcare Failure Mode and Effect Analysis
Equipment and Supplies
Surveys and Questionnaires

Keywords

  • failure mode and effect analysis
  • linac quality control
  • linear accelerator
  • quality assurance

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

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title = "Application of failure mode and effects analysis to optimization of linac quality controls protocol",
abstract = "Purpose: To apply Failure Mode and Effects Analysis (FMEA) to optimize linac quality control (QC) protocol in order to ensure patient safety and treatment quality, taking maximum advantage of the available resources. Material and methods: Each parameter tested by the QC was considered as a potential failure mode (FM). For each FM, likelihood of occurrence (O), severity of effect (S), and lack of detectability (D) were evaluated and corresponding Risk Priority Number (RPN) was calculated from the product of three indexes. The scores were assigned using two methods: (a) A survey submitted to the medical physicists; (b) A semi-quantitative analysis (SQA) performed through: simulation of FMs in the treatment planning system; studies reported in literature; results obtained by the QC data analysis. A weighted RPN for all FMs was calculated taking into account both the methods. For each linac, the tests were then sorted by their frequency and the RPN value. Results: A high variability was found in the scores of the survey, although in many it was reduced in RPN values, highlighting the more relevant tests as on beam output and imaging system. Integrating these results with those obtained by SQA, the RPN-based ranking of tests has been provided considering the specific use of the accelerator: for example, more accurate tests on dose modulation and multileaf collimator speed were required in linacs where intensity-modulated treatment is performed, while, more specific tests on couch and jaw position indicators were necessary where treatments with multiple isocenters and/or junctions between adjacent fields were often delivered. Conclusions: Failure Mode and Effects Analysis is a useful tool to prioritize the linac QCs, taking into account the specific equipment and clinical practice. The integration of SQA and survey results reduces subjectivity of the FMEA scoring and allows to optimize linac QCs without “losing” the expertise and experience of medical physicists and clinical staff.",
keywords = "failure mode and effect analysis, linac quality control, linear accelerator, quality assurance",
author = "Francesca Bonfantini and Tommaso Giandini and Silvia Meroni and Anna Cavallo and Claudio Stucchi and Mauro Carrara and Valeria Mongioj and Ivan Veronese and Emanuele Pignoli",
year = "2019",
month = "6",
doi = "10.1002/mp.13538",
language = "English",
volume = "46",
pages = "2541--2555",
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T1 - Application of failure mode and effects analysis to optimization of linac quality controls protocol

AU - Bonfantini, Francesca

AU - Giandini, Tommaso

AU - Meroni, Silvia

AU - Cavallo, Anna

AU - Stucchi, Claudio

AU - Carrara, Mauro

AU - Mongioj, Valeria

AU - Veronese, Ivan

AU - Pignoli, Emanuele

PY - 2019/6

Y1 - 2019/6

N2 - Purpose: To apply Failure Mode and Effects Analysis (FMEA) to optimize linac quality control (QC) protocol in order to ensure patient safety and treatment quality, taking maximum advantage of the available resources. Material and methods: Each parameter tested by the QC was considered as a potential failure mode (FM). For each FM, likelihood of occurrence (O), severity of effect (S), and lack of detectability (D) were evaluated and corresponding Risk Priority Number (RPN) was calculated from the product of three indexes. The scores were assigned using two methods: (a) A survey submitted to the medical physicists; (b) A semi-quantitative analysis (SQA) performed through: simulation of FMs in the treatment planning system; studies reported in literature; results obtained by the QC data analysis. A weighted RPN for all FMs was calculated taking into account both the methods. For each linac, the tests were then sorted by their frequency and the RPN value. Results: A high variability was found in the scores of the survey, although in many it was reduced in RPN values, highlighting the more relevant tests as on beam output and imaging system. Integrating these results with those obtained by SQA, the RPN-based ranking of tests has been provided considering the specific use of the accelerator: for example, more accurate tests on dose modulation and multileaf collimator speed were required in linacs where intensity-modulated treatment is performed, while, more specific tests on couch and jaw position indicators were necessary where treatments with multiple isocenters and/or junctions between adjacent fields were often delivered. Conclusions: Failure Mode and Effects Analysis is a useful tool to prioritize the linac QCs, taking into account the specific equipment and clinical practice. The integration of SQA and survey results reduces subjectivity of the FMEA scoring and allows to optimize linac QCs without “losing” the expertise and experience of medical physicists and clinical staff.

AB - Purpose: To apply Failure Mode and Effects Analysis (FMEA) to optimize linac quality control (QC) protocol in order to ensure patient safety and treatment quality, taking maximum advantage of the available resources. Material and methods: Each parameter tested by the QC was considered as a potential failure mode (FM). For each FM, likelihood of occurrence (O), severity of effect (S), and lack of detectability (D) were evaluated and corresponding Risk Priority Number (RPN) was calculated from the product of three indexes. The scores were assigned using two methods: (a) A survey submitted to the medical physicists; (b) A semi-quantitative analysis (SQA) performed through: simulation of FMs in the treatment planning system; studies reported in literature; results obtained by the QC data analysis. A weighted RPN for all FMs was calculated taking into account both the methods. For each linac, the tests were then sorted by their frequency and the RPN value. Results: A high variability was found in the scores of the survey, although in many it was reduced in RPN values, highlighting the more relevant tests as on beam output and imaging system. Integrating these results with those obtained by SQA, the RPN-based ranking of tests has been provided considering the specific use of the accelerator: for example, more accurate tests on dose modulation and multileaf collimator speed were required in linacs where intensity-modulated treatment is performed, while, more specific tests on couch and jaw position indicators were necessary where treatments with multiple isocenters and/or junctions between adjacent fields were often delivered. Conclusions: Failure Mode and Effects Analysis is a useful tool to prioritize the linac QCs, taking into account the specific equipment and clinical practice. The integration of SQA and survey results reduces subjectivity of the FMEA scoring and allows to optimize linac QCs without “losing” the expertise and experience of medical physicists and clinical staff.

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