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[en] Radiation therapy has undergone considerable changes in the past two decades with a surge of new technology and treatment delivery methods. The complexity of radiation therapy treatments has increased and there has been increased awareness and publicity about the associated risks. In response, there has been proliferation of guidelines for medical physicists to adopt to ensure that treatments are delivered safely. Task Group recommendations are copious, and clinical physicists' hours are longer, stretched to various degrees between site planning and management, IT support, physics QA, and treatment planning responsibilities.Radiation oncology has many quality control practices in place to ensure the delivery of high-quality, safe treatments. Incident reporting systems have been developed to collect statistics about near miss events at many radiation oncology centers. However, tools are lacking to assess the impact of these various control measures. A recent effort to address this shortcoming is the work of Ford et al (2012) who recently published a methodology enumerating quality control quantification for measuring the effectiveness of safety barriers. Over 4000 near-miss incidents reported from 2 academic radiation oncology clinics were analyzed using quality control quantification, and a profile of the most effective quality control measures (metrics) was identified.There is a critical need to identify a QA metric to help the busy clinical physicists to focus their limited time and resources most effectively in order to minimize or eliminate errors in the radiation treatment delivery processes. In this symposium the usefulness of workflows and QA metrics to assure safe and high quality patient care will be explored.Two presentations will be given:Quality Metrics and Risk Management with High Risk Radiation Oncology ProceduresStrategies and metrics for quality management in the TG-100 Era Learning Objectives: Provide an overview and the need for QA usability metrics: Different cultures/practices affecting the effectiveness of methods and metrics. Show examples of quality assurance workflows, Statistical process control, that monitor the treatment planning and delivery process to identify errors. To learn to identify and prioritize risks and QA procedures in radiation oncology. Try to answer the question: Can a quality assurance program aided by quality assurance metrics help minimize errors and ensure safe treatment delivery. Should such metrics be institution specific
[en] In this article the writer has described the definition of process industry, expounded the fact classifying nuclear industry as process industry, compared the differences between process industry and discrete industry, analysed process industry properties in nuclear industry and their important impact, and proposed enhancing research work on regularity of process industry in nuclear industry. (authors)
[en] Alternative analytical methods were validated for the process control of 500 mg florouacil, 50 mg doxorrubicin and 50 mg methotrexate by spectrophotometry because of they are more simple and economic allowint to control the drugs quality in process analysis control. Calibration curves of fluorouracil, doxorrubicin and methotrexate were plotted in interval from 60 to 140%, where there were linear with correlation coefficients similar to 0.9998, 0.9999 and 0.9999, respectively; statistical text for intercept and slope were considered as non-significant. Recoveries of 99.97, 99.98 and 99.35% were achieved, respectively in study concentration interval and Cochran and t-Student tests were also non-significant. Methods were specific, linear, precises and exacts in interval of study concentrations
[en] Complete text of publication follows. Low energy electron irradiation (80-300 keV) is used increasingly for sterilization or decontamination in connection with isolators for aseptic filling lines in the pharmaceutical industry. It is not defined how validation for this process shall be carried out. A method can be derived from the medical device standard for radiation sterilization, ISO 11137, because the principles described in this standard can be applied to almost any industrial irradiation process. The validations elements are: Process definition, concerning specification of the dose required for the process and the maximum acceptable dose for the product. Installation qualification, concerning acceptance the irradiation facility. Operational qualification, concerning characterization of the facility. Performance qualification, concerning setting up the process. Process control, concerning routine monitoring. The limited penetration of the low energy electrons leads to problems with respect to executing these validation steps. This paper discusses these problems, and shows with examples how they can be solved.
[en] The possibility of using statistical process control methods for detection of an abnormal condition of the process equipment at early stages of an emergency is shown in the paper. The authors of the paper has concluded that with the use of Shewhart charts it is possible to monitor the real dynamics of the process equipment condition and make decisions on its maintenance and repair (paper)
[en] Purpose: To develop an integrated statistical process control (SPC) framework using digital performance and component data accumulated within the accelerator system that can detect dysfunction prior to unscheduled downtime. Methods: Seven digital accelerators were monitored for twelve to 18 months. The accelerators were operated in a ‘run to failure mode’ with the individual institutions determining when service would be initiated. Institutions were required to submit detailed service reports. Trajectory and text log files resulting from a robust daily VMAT QA delivery were decoded and evaluated using Individual and Moving Range (I/MR) control charts. The SPC evaluation was presented in a customized dashboard interface that allows the user to review 525 monitored parameters (480 MLC parameters). Chart limits were calculated using a hybrid technique that includes the standard SPC 3σ limits and an empirical factor based on the parameter/system specification. The individual (I) grand mean values and control limit ranges of the I/MR charts of all accelerators were compared using statistical (ranked analysis of variance (ANOVA)) and graphical analyses to determine consistency of operating parameters. Results: When an alarm or warning was directly connected to field service, process control charts predicted dysfunction consistently on beam generation related parameters (BGP)– RF Driver Voltage, Gun Grid Voltage, and Forward Power (W); beam uniformity parameters – angle and position steering coil currents; and Gantry position accuracy parameter: cross correlation max-value. Control charts for individual MLC – cross correlation max-value/position detected 50% to 60% of MLCs serviced prior to dysfunction or failure. In general, non-random changes were detected 5 to 80 days prior to a service intervention. The ANOVA comparison of BGP determined that each accelerator parameter operated at a distinct value. Conclusion: The SPC framework shows promise. Long term monitoring coordinated with service will be required to definitively determine the effectiveness of the model. Varian Medical System, Inc. provided funding in support of the research presented.