Hazard testing to reduce risk in the development of automated planning tools

• Published in: Journal of applied clinical medical physics 2023-08, Vol.24 (8), p.e13995-n/a
• Main Authors: Nealon, Kelly A., Douglas, Raphael J., Han, Eun Young, Kry, Stephen F., Reed, Valerie K., Simiele, Samantha J., Court, Laurence E.
• Format: Article
• Language: English
• Subjects: Automation; Cervix; Failure; Human error; IM/TH‐ formal quality management tools: general (most aspects); Medical imaging; Patient safety; Performance evaluation; Physicists; Planning; Radiation Oncology Physics; Radiation therapy; Risk assessment; Software; Therapists; TH‐ External beam‐ photons: Development (new technology and techniques); Tomography; Usability
• ISSN: 1526-9914; 1526-9914
• DOI: 10.1002/acm2.13995

Purpose Hazard 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. Methods Automated 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. Results Initially, 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. Conclusions Hazard 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.