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Risk Management of Clinical Reference Dosimetry of a Large Hospital Network Using Statistical Process Control
Managing TG-51 reference dosimetry in a large hospital network can be a challenging task. The objectives of this study are to investigate the effectiveness of using Statistical Process Control (SPC) to manage TG-51 workflow in such a network. All the sites in the network performed the annual referen...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360384/ https://www.ncbi.nlm.nih.gov/pubmed/34395105 http://dx.doi.org/10.4236/ijmpcero.2021.103011 |
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author | Lim, Seng-Boh LoSasso, Thomas Chan, Maria Cervino, Laura Lovelock, Dale Michael |
author_facet | Lim, Seng-Boh LoSasso, Thomas Chan, Maria Cervino, Laura Lovelock, Dale Michael |
author_sort | Lim, Seng-Boh |
collection | PubMed |
description | Managing TG-51 reference dosimetry in a large hospital network can be a challenging task. The objectives of this study are to investigate the effectiveness of using Statistical Process Control (SPC) to manage TG-51 workflow in such a network. All the sites in the network performed the annual reference dosimetry in water according to TG-51. These data were used to cross-calibrate the same ion chambers in plastic phantoms for monthly QA output measurements. An energy-specific dimensionless beam quality cross-calibration factor, [Formula: see text] , was derived to monitor the process across multiple sites. The SPC analysis was then performed to obtain the mean, [Formula: see text] , standard deviation, σ(k), the Upper Control Limit (UCL) and Lower Control Limit (LCL) in each beam. This process was first applied to 15 years of historical data at the main campus to assess the effectiveness of the process. A two-year prospective study including all 30 linear accelerators spread over the main campus and seven satellites in the network followed. The ranges of the control limits (±3σ) were found to be in the range of 1.7% – 2.6% and 3.3% – 4.2% for the main campus and the satellite sites respectively. The wider range in the satellite sites was attributed to variations in the workflow. Standardization of workflow was also found to be effective in narrowing the control limits. The SPC is effective in identifying variations in the workflow and was shown to be an effective tool in managing large network reference dosimetry. |
format | Online Article Text |
id | pubmed-8360384 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-83603842021-08-12 Risk Management of Clinical Reference Dosimetry of a Large Hospital Network Using Statistical Process Control Lim, Seng-Boh LoSasso, Thomas Chan, Maria Cervino, Laura Lovelock, Dale Michael Int J Med Phys Clin Eng Radiat Oncol Article Managing TG-51 reference dosimetry in a large hospital network can be a challenging task. The objectives of this study are to investigate the effectiveness of using Statistical Process Control (SPC) to manage TG-51 workflow in such a network. All the sites in the network performed the annual reference dosimetry in water according to TG-51. These data were used to cross-calibrate the same ion chambers in plastic phantoms for monthly QA output measurements. An energy-specific dimensionless beam quality cross-calibration factor, [Formula: see text] , was derived to monitor the process across multiple sites. The SPC analysis was then performed to obtain the mean, [Formula: see text] , standard deviation, σ(k), the Upper Control Limit (UCL) and Lower Control Limit (LCL) in each beam. This process was first applied to 15 years of historical data at the main campus to assess the effectiveness of the process. A two-year prospective study including all 30 linear accelerators spread over the main campus and seven satellites in the network followed. The ranges of the control limits (±3σ) were found to be in the range of 1.7% – 2.6% and 3.3% – 4.2% for the main campus and the satellite sites respectively. The wider range in the satellite sites was attributed to variations in the workflow. Standardization of workflow was also found to be effective in narrowing the control limits. The SPC is effective in identifying variations in the workflow and was shown to be an effective tool in managing large network reference dosimetry. 2021-07-30 2021-08 /pmc/articles/PMC8360384/ /pubmed/34395105 http://dx.doi.org/10.4236/ijmpcero.2021.103011 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) |
spellingShingle | Article Lim, Seng-Boh LoSasso, Thomas Chan, Maria Cervino, Laura Lovelock, Dale Michael Risk Management of Clinical Reference Dosimetry of a Large Hospital Network Using Statistical Process Control |
title | Risk Management of Clinical Reference Dosimetry of a Large Hospital Network Using Statistical Process Control |
title_full | Risk Management of Clinical Reference Dosimetry of a Large Hospital Network Using Statistical Process Control |
title_fullStr | Risk Management of Clinical Reference Dosimetry of a Large Hospital Network Using Statistical Process Control |
title_full_unstemmed | Risk Management of Clinical Reference Dosimetry of a Large Hospital Network Using Statistical Process Control |
title_short | Risk Management of Clinical Reference Dosimetry of a Large Hospital Network Using Statistical Process Control |
title_sort | risk management of clinical reference dosimetry of a large hospital network using statistical process control |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360384/ https://www.ncbi.nlm.nih.gov/pubmed/34395105 http://dx.doi.org/10.4236/ijmpcero.2021.103011 |
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