[Analysis of Near-miss Incidents and Their Causes during Clinical Training for Radiological Technologist Students]

Nihon Hoshasen Gijutsu Gakkai Zasshi. 2026;82(3). doi: 10.6009/jjrt.26-1605.
[Article in Japanese]

Abstract

Purpose: This study aimed to clarify the actual conditions of near-miss incidents experienced by radiological technology students during clinical training and to analyze the contributing factors, to provide suggestions for future safety education and curriculum development.

Methods: An anonymous self-administered questionnaire was conducted among students and graduates of a radiological technology training program. The survey items included the specific nature of the incidents, background factors at the time of occurrence, the modality in which the incidents occurred, and the discoverer of the incidents. Quantitative data were analyzed using descriptive statistics and cross-tabulation, while qualitative responses were analyzed using content analysis.

Results: A total of 75 valid responses were obtained. The reported near-miss incidents were diverse, with many related to basic confirmation procedures such as patient misidentification, entanglement of tubes and cables, and failure to remove metallic items. Most of the incidents occurred in general radiography and computed tomography, accounting for approximately 70% of all reports. In terms of discoverers, the majority of incidents were noticed by the students themselves (58.3%), followed by clinical instructors (30.5%).

Conclusion: Near-miss experiences serve as valuable educational resources in student training. Redesigning safety education by focusing on common patterns shared with cases reported by licensed professionals, and systematizing these experiences into structured learning modules, may help enhance both the quality of education and safety awareness in radiological practice.

Keywords: clinical training; curriculum development; near-miss incident; radiological technologist students; safety education.

Publication types

  • English Abstract

MeSH terms

  • Female
  • Humans
  • Male
  • Near Miss, Healthcare* / statistics & numerical data
  • Surveys and Questionnaires
  • Technology, Radiologic* / education