Strategies for effective physics plan and chart review in radiation therapy: Report of AAPM Task Group 275

Ford, Eric; Conroy, Leigh; Dong, Lei; Santos, L Fong de los; Greener, Anne; Kim, Grace Gwe-Ya; Johnson, Jennifer L.; Johnson, Perry B.; Mechalakos, James; Napolitano, Brian; Parker, Stephanie; Schofield, Deborah; Smith, Kevin C.; Yorke, Ellen; Wells, Michelle

doi:10.1002/mp.14030

Cited by 80 publications

(123 citation statements)

References 43 publications

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“…4,[12][13][14] Based on FMEA, resources can be concentrated on the most significant effect. In fact, the American Association of Physicists in Medicine's (AAPM) Task Group (TG) 275 15 recommends that each practice should assess local processes and identify key high-risk failure modes.…”

Section: Failure Mode and Effects Analysis (Fmea) Is A Widely Used Toolmentioning

confidence: 99%

Using failure mode and effects analysis (FMEA) to generate an initial plan check checklist for improved safety in radiation treatment

Rassiah

Huang

et al. 2020

J Applied Clin Med Phys

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To apply failure mode and effect analysis (FMEA) to generate an effective and efficient initial physics plan checklist. Methods: A team of physicists, dosimetrists, and therapists was setup to reconstruct the workflow processes involved in the generation of a treatment plan beginning from simulation. The team then identified possible failure modes in each of the processes. For each failure mode, the severity (S), frequency of occurrence (O), and the probability of detection (D) was assigned a value and the risk priority number (RPN) was calculated. The values assigned were based on TG 100. Prior to assigning a value, the team discussed the values in the scoring system to minimize randomness in scoring. A local database of errors was used to help guide the scoring of frequency. Results: Twenty-seven process steps and 50 possible failure modes were identified starting from simulation to the final approved plan ready for treatment at the machine. Any failure mode that scored an average RPN value of 20 or greater was deemed "eligible" to be placed on the second checklist. In addition, any failure mode with a severity score value of 4 or greater was also considered for inclusion in the checklist. As a by-product of this procedure, safety improvement methods such as automation and standardization of certain processes (e.g., dose constraint checking, check tools), removal of manual transcription of treatment-related information as well as staff education were implemented, although this was not the team's original objective. Prior to the implementation of the new FMEA-based checklist, an in-service for all the second checkers was organized to ensure further standardization of the process. Conclusion: The FMEA proved to be a valuable tool for identifying vulnerabilities in our workflow and processes in generating a treatment plan and subsequently a new, more effective initial plan checklist was created.

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Section: Failure Mode and Effects Analysis (Fmea) Is A Widely Used Toolmentioning

confidence: 99%

Using failure mode and effects analysis (FMEA) to generate an initial plan check checklist for improved safety in radiation treatment

Rassiah

Huang

et al. 2020

J Applied Clin Med Phys

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“…1,2 "Wrong or inaccurate" contours drawn by physicians and dosimetrists constitute the highest and seventh-highest risk factors for failure of photon/ electron external beam radiation treatment, respectively. 3 Most of these errors could be avoided if an accurate and reliable auto-contouring tool were available. In the past, various algorithms have been evaluated for the development of autocontouring tools, with mixed success.…”

Section: Introductionmentioning

confidence: 99%

Automatic contouring system for cervical cancer using convolutional neural networks

et al. 2020

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To develop a tool for the automatic contouring of clinical treatment volumes (CTVs) and normal tissues for radiotherapy treatment planning in cervical cancer patients. Methods: An auto-contouring tool based on convolutional neural networks (CNN) was developed to delineate three cervical CTVs and 11 normal structures (seven OARs, four bony structures) in cervical cancer treatment for use with the Radiation Planning Assistant, a web-based automatic plan generation system. A total of 2254 retrospective clinical computed tomography (CT) scans from a single cancer center and 210 CT scans from a segmentation challenge were used to train and validate the CNN-based auto-contouring tool. The accuracy of the tool was evaluated by calculating the Sørensen-dice similarity coefficient (DSC) and mean surface and Hausdorff distances between the automatically generated contours and physician-drawn contours on 140 internal CT scans. A radiation oncologist scored the automatically generated contours on 30 external CT scans from three South African hospitals.

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“…A median of 2 problematic plans were presented weekly (range, 1-4). In 75% of problematic plans, the severity score 19 was >7 corresponding to expected potentially serious toxicity or tumor underdose (range, 4-10), and in 75% of problematic plans the detectability score 19 was <7 corresponding to a !95% expected likelihood of detection (range, [3][4][5][6][7][8][9][10].…”

Section: Resultsmentioning

confidence: 99%

“…The types of errors considered in peer review (inclusion of nodal volumes at risk, choice of prescribed dose) often do not have other quality assurance (QA) measures in place, which makes them inherently high-risk and critical for review. 6 Additionally, even errors with a high likelihood of detection upstream of physician chart rounds in the QA workflow, such as planning on the wrong scan, can pass through QA to the point of treatment delivery. 7,8 Physician peer review is often one of the final quality assurance steps for assessing plan quality and plan safety 9 and can provide a final "stop the line" moment for some of these previously reviewed treatment planning details.…”

Section: Introductionmentioning

confidence: 99%

A Blinded, Prospective Study of Error Detection During Physician Chart Rounds in Radiation Oncology

Talcott

Lincoln

Kelly

et al. 2020

Practical Radiation Oncology

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Peer review during physician chart rounds is a major quality assurance and patient safety step in radiation oncology. However, the effectiveness of chart rounds in detecting problematic treatment plans is unknown. We performed a prospective blinded study of error detection at chart rounds to clarify the effectiveness of this quality assurance step. Methods and Materials: Radiation Oncology Incident Learning System publications were queried for problematic plans approved for treatment that would be detectable at chart rounds. A resident physician, physicist, and dosimetrist collaboratively generated 20 treatment plans with simulated errors identical in nature to those reported to the Radiation Oncology Incident Learning System. These were inserted randomly into weekly chart rounds over 9 weeks, with a median of 2 problematic plans presented per chart rounds (range, 1-4). Data were collected on detection, attendance, length, and number of cases presented at chart rounds. Data were analyzed using descriptive statistics and univariable logistic regression with odds ratios. Results: The median length of chart rounds over the study period was 60 minutes (range, 42-79); median number of cases presented per chart rounds was 45 (range, 38-50). The overall detection rate was 55% (11 of 20). Detection rates were higher for cases presented earlier in chart rounds: 75% versus 25% of problematic plans were detected within 30 minutes of start of chart rounds versus after 30 minutes (odds Sources of support: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Disclosures: Dr Roy Decker reports grants and personal fees from Merck, personal fees from Astra Zeneca, and personal fees from Regeneron outside the submitted work. Research data are stored in an institutional repository and select data may be shared upon request to the corresponding author as per institutional guidelines.

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Strategies for effective physics plan and chart review in radiation therapy: Report of AAPM Task Group 275

Cited by 80 publications

References 43 publications

Using failure mode and effects analysis (FMEA) to generate an initial plan check checklist for improved safety in radiation treatment

Using failure mode and effects analysis (FMEA) to generate an initial plan check checklist for improved safety in radiation treatment

Automatic contouring system for cervical cancer using convolutional neural networks

A Blinded, Prospective Study of Error Detection During Physician Chart Rounds in Radiation Oncology

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