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Implementation of a knowledge‐based decision support system for treatment plan auditing through automation.

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Title: Implementation of a knowledge‐based decision support system for treatment plan auditing through automation.
Authors: Liu, Shi1 (AUTHOR) lius4@mskcc.org, Chapman, Katherine L.1 (AUTHOR), Berry, Sean L.1 (AUTHOR), Bertini, Julian2 (AUTHOR), Ma, Rongtao1 (AUTHOR), Fu, Yabo1 (AUTHOR), Yang, Deshan3 (AUTHOR), Moran, Jean M.1 (AUTHOR), Della‐Biancia, Cesar1 (AUTHOR)
Source: Medical Physics. Nov2023, Vol. 50 Issue 11, p6978-6989. 12p.
Subjects: Computerized auditing, Decision support systems, Auditing procedures, External beam radiotherapy, Internal auditing, Standardization, Mustard gas
Abstract: Background: Independent auditing is a necessary component of a comprehensive quality assurance (QA) program and can also be utilized for continuous quality improvement (QI) in various radiotherapy processes. Two senior physicists at our institution have been performing a time intensive manual audit of cross‐campus treatment plans annually, with the aim of further standardizing our planning procedures, updating policies and guidelines, and providing training opportunities of all staff members. Purpose: A knowledge‐based automated anomaly‐detection algorithm to provide decision support and strengthen our manual retrospective plan auditing process was developed. This standardized and improved the efficiency of the assessment of our external beam radiotherapy (EBRT) treatment planning across all eight campuses of our institution. Methods: A total of 843 external beam radiotherapy plans for 721 lung patients from January 2020 to March 2021 were automatically acquired from our clinical treatment planning and management systems. From each plan, 44 parameters were automatically extracted and pre‐processed. A knowledge‐based anomaly detection algorithm, namely, "isolation forest" (iForest), was then applied to the plan dataset. An anomaly score was determined for each plan using recursive partitioning mechanism. Top 20 plans ranked with the highest anomaly scores for each treatment technique (2D/3D/IMRT/VMAT/SBRT) including auto‐populated parameters were used to guide the manual auditing process and validated by two plan auditors. Results: The two auditors verified that 75.6% plans with the highest iForest anomaly scores have similar concerning qualities that may lead to actionable recommendations for our planning procedures and staff training materials. The time to audit a chart was approximately 20.8 min on average when done manually and 14.0 min when done with the iForest guidance. Approximately 6.8 min were saved per chart with the iForest method. For our typical internal audit review of 250 charts annually, the total time savings are approximately 30 hr per year. Conclusion: iForest effectively detects anomalous plans and strengthens our cross‐campus manual plan auditing procedure by adding decision support and further improve standardization. Due to the use of automation, this method was efficient and will be used to establish a standard plan auditing procedure, which could occur more frequently. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Implementation of a knowledge‐based decision support system for treatment plan auditing through automation.
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  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Nov2023, Vol. 50 Issue 11, p6978-6989. 12p.
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  Data: Background: Independent auditing is a necessary component of a comprehensive quality assurance (QA) program and can also be utilized for continuous quality improvement (QI) in various radiotherapy processes. Two senior physicists at our institution have been performing a time intensive manual audit of cross‐campus treatment plans annually, with the aim of further standardizing our planning procedures, updating policies and guidelines, and providing training opportunities of all staff members. Purpose: A knowledge‐based automated anomaly‐detection algorithm to provide decision support and strengthen our manual retrospective plan auditing process was developed. This standardized and improved the efficiency of the assessment of our external beam radiotherapy (EBRT) treatment planning across all eight campuses of our institution. Methods: A total of 843 external beam radiotherapy plans for 721 lung patients from January 2020 to March 2021 were automatically acquired from our clinical treatment planning and management systems. From each plan, 44 parameters were automatically extracted and pre‐processed. A knowledge‐based anomaly detection algorithm, namely, "isolation forest" (iForest), was then applied to the plan dataset. An anomaly score was determined for each plan using recursive partitioning mechanism. Top 20 plans ranked with the highest anomaly scores for each treatment technique (2D/3D/IMRT/VMAT/SBRT) including auto‐populated parameters were used to guide the manual auditing process and validated by two plan auditors. Results: The two auditors verified that 75.6% plans with the highest iForest anomaly scores have similar concerning qualities that may lead to actionable recommendations for our planning procedures and staff training materials. The time to audit a chart was approximately 20.8 min on average when done manually and 14.0 min when done with the iForest guidance. Approximately 6.8 min were saved per chart with the iForest method. For our typical internal audit review of 250 charts annually, the total time savings are approximately 30 hr per year. Conclusion: iForest effectively detects anomalous plans and strengthens our cross‐campus manual plan auditing procedure by adding decision support and further improve standardization. Due to the use of automation, this method was efficient and will be used to establish a standard plan auditing procedure, which could occur more frequently. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/mp.16472
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        Text: English
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        PageCount: 12
        StartPage: 6978
    Subjects:
      – SubjectFull: Computerized auditing
        Type: general
      – SubjectFull: Decision support systems
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      – SubjectFull: Auditing procedures
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      – SubjectFull: Standardization
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      – SubjectFull: Mustard gas
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              Text: Nov2023
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