Efficient preparation of radiology reports is a cornerstone of modern diagnostic imaging workflows. Radiologists are under increasing pressure to manage high volumes of cases while ensuring accurate and detailed reporting [1,2] , which directly influences patient outcomes. However, traditional methods of report preparation, such as manual keyboard entry and voice dictation, come with significant limitations [3].

Manual typing is time-consuming and prone to errors, especially under high workloads. It requires radiologists to focus on repetitive tasks rather than diagnostic reasoning, leading to fatigue and potential delays in patient care. Voice dictation, while offering a hands-free alternative, is not without challenges. Dictation software often struggles with medical terminology, accents, and varying speech patterns, necessitating time-intensive edits and corrections. Additionally, reliance on these methods can exacerbate burnout among radiologists, a pressing issue highlighted in recent years [4].

To address these challenges, there is a growing interest in leveraging automation and artificial intelligence (AI) tools. One such approach involves pre-defined phrases, referred to as "triggers", that standardize and accelerate the documentation process, especially when adopted into structured reporting systems [5]. By automating repetitive descriptions of common or abnormal findings, triggers promise to reduce cognitive load, save time, and improve overall efficiency. 

Based on these challenges and advancements, this study hypothesizes that integrating pre-defined triggers into structured reporting radiology workflows will significantly improve efficiency, as measured by time saved, reduction in spoken words, and minimized keystroke inputs, compared to traditional methods. This study evaluates the practical benefits of using triggers, benchmarking them against established methods, i.e. traditional keyboard writing and voice dictation.