ArticleRadiation oncology (London, England)2026
Enhancing Radiation Therapy Technologists' (RTTs) autonomy in IGRT through structured training: a Protocol for a Quality Improvement Project.
Article in Radiation oncology (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
Funding
Abstract
backgroundIGRT has become an essential component in delivering precise and effective radiotherapy (RT) treatment. However, in many healthcare institutions, the responsibility for online image matching remains within the exclusive domain of Radiation Oncologists (ROs). This can result in workflow inefficiencies and delays in patient treatment. In light of increasing technological capabilities and evolving professional roles, there is growing interest in expanding the scope of Radiation Therapy Technologists (RTTs) to take on more autonomous responsibilities within the IGRT process.
objectiveThis study’s protocol integrates educational content, performance metrics and integration into institutional development plans, aiming to evaluate RTT’s autonomy and to assess the feasibility of a standard training model. Structured training is expected to empower RTTs to contribute autonomously to IGRT processes, improving workflow and potentially reducing treatment times.
methodsA quality improvement initiative will assess RTT autonomy in IGRT. Twenty RTTs will complete 33 h of training, including theory and practice, under RO supervision. Competence will be evaluated using pre- and post-course assessments, as well as a pilot phase in which selected RTTs will perform CBCT image matching independently: concordance with RO offline reviews will be used as the benchmark. EXPECTED OUTCOMES: The project is designed to explore three key challenges: the need for clear protocols, staff engagement and interprofessional collaboration. Structured training could empower RTTs to perform IGRT image matching independently, reducing their reliance on ROs and streamlining workflows, promoting more efficient resource allocation. Wider adoption of this approach could improve the quality and efficiency of RT delivery.
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