ArticleJournal of applied clinical medical physics2023
Hazard testing to reduce risk in the development of automated planning tools.
Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Leveraging large language models for heuristic usability assessment of medical software: Insights with the Radiation Planning Assistant.Journal of applied clinical medical physics · 2026Article
- Utilizing process mining in quality management: A case study in radiation oncology.PLOS digital health · 2025Article
- Multicentre prospective risk analysis of a fully automated radiotherapy workflow.Physics and imaging in radiation oncology · 2025Article
- Risk assessment and quality management in AIO based on CT-linac for nasopharyngeal carcinoma: An improved FMEA and FTA approach.Medical physics · 2025Article
- Artificial Intelligence-Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care.JCO global oncology · 2024Article
- Hazard testing to reduce risk in the development of automated planning tools.Journal of applied clinical medical physics · 2023Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeHazard scenarios were created to assess and reduce the risk of planning errors in automated planning processes. This was accomplished through iterative testing and improvement of examined user interfaces.
methodsAutomated planning requires three user inputs: a computed tomography (CT), a prescription document, known as the service request, and contours. We investigated the ability of users to catch errors that were intentionally introduced into each of these three stages, according to an FMEA analysis. Five radiation therapists each reviewed 15 patient CTs, containing three errors: inappropriate field of view, incorrect superior border, and incorrect identification of isocenter. Four radiation oncology residents reviewed 10 service requests, containing two errors: incorrect prescription and treatment site. Four physicists reviewed 10 contour sets, containing two errors: missing contour slices and inaccurate target contour. Reviewers underwent video training prior to reviewing and providing feedback for various mock plans.
resultsInitially, 75% of hazard scenarios were detected in the service request approval. The visual display of prescription information was then updated to improve the detectability of errors based on user feedback. The change was then validated with five new radiation oncology residents who detected 100% of errors present. 83% of the hazard scenarios were detected in the CT approval portion of the workflow. For the contour approval portion of the workflow none of the errors were detected by physicists, indicating this step will not be used for quality assurance of contours. To mitigate the risk from errors that could occur at this step, radiation oncologists must perform a thorough review of contour quality prior to final plan approval.
conclusionsHazard testing was used to pinpoint the weaknesses of an automated planning tool and as a result, subsequent improvements were made. This study identified that not all workflow steps should be used for quality assurance and demonstrated the importance of performing hazard testing to identify points of risk in automated planning tools.
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