Program Information
Tolerance Design for Site-Specific Range in Proton Patient QA Process Using the Six Sigma Model
J LAH1*, D Shin2 , G Kim3 , (1) Myongji Hospital, Goyang, Gyeonggi-do, Korea, (2) National Cancer Center, Goyang-si,Gyeonggi-do, Korea,(3) University of California, San Diego, La Jolla, CA
Presentations
SU-E-T-760 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose:To show how tolerance design and tolerancing approaches can be used to predict and improve the site-specific range in patient QA process in implementing the Six Sigma.
Methods:In this study, patient QA plans were selected according to 6 site-treatment groups: head &neck (94 cases), spine (76 cases), lung (89 cases), liver (53 cases), pancreas (55 cases), and prostate (121 cases), treated between 2007 and 2013. We evaluated a model of the Six Sigma that determines allowable deviations in design parameters and process variables in patient-specific QA, where possible, tolerance may be loosened, then customized if it necessary to meet the functional requirements. A Six Sigma problem-solving methodology is known as DMAIC phases, which are used stand for: Define a problem or improvement opportunity, Measure process performance, Analyze the process to determine the root causes of poor performance, Improve the process by fixing root causes, Control the improved process to hold the gains.
Results:The process capability for patient-specific range QA is 0.65 with only ±1 mm of tolerance criteria.
Our results suggested the tolerance level of ±2-3 mm for prostate and liver cases and ±5 mm for lung cases. We found that customized tolerance between calculated and measured range reduce that patient QA plan failure and almost all sites had failure rates less than 1%. The average QA time also improved from 2 hr to less than 1 hr for all including planning and converting process, depth-dose measurement and evaluation.
Conclusion:The objective of tolerance design is to achieve optimization beyond that obtained through QA process improvement and statistical analysis function detailing to implement a Six Sigma capable design.
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