Radiation Therapists as a Human Safety Barrier in Radiotherapy: Detecting, Reporting, and Learning from Near-Miss Events
Hanan Alwusydi, Ma’zza Zainalabdin Omer, Aya Hassabelrasol Farah, Esra Jamal Mohamed Hassan
Abstract
Background:
Radiotherapy is a complex process involving many professions and many handovers of information, with error opportunities at every step. Most errors are intercepted before they reach the patient, and radiation therapists (RTTs), who carry out the final verification and delivery steps, are the professional group that most often detects them.
Objective:
To review the evidence on how RTTs detect, report and learn from near-miss events, and to propose a framework for strengthening this human safety barrier.
Methods:
A narrative mini-review of peer-reviewed literature, incident learning system reports (ROSIS, SAFRON, RO-ILS), professional guidance (AAPM, ASTRO, ICRP, IAEA, WHO, UK safer-radiotherapy publications) and human-factors research.
Results:
Errors arise most often in planning, simulation and documentation, but therapists are the largest single group of discoverers in multi-institutional and international databases. RTTs detect errors through independent verification, image-guided review (CBCT, kV/MV, surface guidance), observation in the treatment room and communication with the team. Detection is shaped by training, workload, situation awareness and the design of technology. Reporting is limited by fear of reprimand, hierarchy, slow systems and cognitive biases that make intercepted events seem not worth reporting. Psychological safety and just-culture principles increase reporting. Learning requires analysis at the level of the system, corrective action and feedback. Automation reduces some errors but creates new failure modes and automation bias.
Conclusions:
RTTs are active safety barriers, not passive operators. We propose a Detect-Intercept-Report-Analyze-Learn-Improve-Reassess framework with RTT competencies defined for each stage, and we call for standard definitions, RTT-led investigation, safety competencies in RTT education, and AI that supports rather than replaces the vigilance of RTTs.