The findings of this study demonstrate that using pre-defined phrases (triggers) significantly accelerated the report preparation process compared to traditional methods such as manual keyboard entry and voice dictation. The significant reduction in spoken words and keystrokes further highlights the ergonomic advantages of this approach. By decreasing the physical and cognitive workload associated with repetitive tasks, triggers may alleviate some of the factors contributing to radiologist burnout. Additionally, these results suggest that adopting trigger-based methods can streamline workflows in busy clinical settings, allowing radiologists to allocate more time to diagnostic interpretation and patient care.These results underscore the potential of trigger-based workflows to address key inefficiencies in radiology reporting.

Triggers likely improve efficiency due to their ability to standardize and automate repetitive descriptions. For example, commonly observed abnormal findings (e.g., "consolidation in the perihilar regions of both lungs”) can be pre-defined (e.g., trigger named “perihilar”), eliminating the need for manual entry or dictation of the full phrase. This approach also minimizes variability in phrasing, improving report clarity and consistency.

Another factor contributing to the observed efficiency gains is the reduction in post-editing time. Both manual entry and voice dictation often require extensive corrections, especially in the case of complex medical terminology [3]. Triggers, by contrast, provide pre-tested phrases that eliminate the need for such corrections.

Implementing triggers in radiology reporting workflows has several clinical implications. 

First, faster report preparation can improve workflow efficiency, reduce patient waiting times and increase throughput in busy departments. 

Second, pre-defined phrases are designed to ensure accuracy, clarity, and consistency in describing findings, which reduces variability in language and mitigates the risk of miscommunication with referring physicians. Triggers help standardize terminology, ensuring that all reports adhere to professional guidelines and institutional standards. This structured approach not only enhances the readability of reports but also minimizes the likelihood of critical details being overlooked, contributing to better diagnostic precision and overall patient care. 

Third, while this study focuses on radiology, the trigger-based approach could be extended to other fields of medicine that rely on detailed documentation. Pathology, cardiology, and oncology, for instance, often require the standardized description of findings, which could benefit from pre-defined phrases. By customizing triggers for specific specialties, the medical community could achieve broader improvements in reporting efficiency and consistency across disciplines.

These findings align with previous research on structured reporting and automation in radiology. Previous studies highlighted the benefits of structured reporting in enhancing clarity and reducing variability [5,6], a goal that triggers also achieve. However, our study extends that knowledge by quantifying the time and effort saved by automation.

The efficiency gains observed in this study also reflect the potential of AI-based tools [7]. While AI has primarily been used for image analysis, this study demonstrates its complementary role in language generation and workflow optimization.

This study has several limitations. First, the use of triggers requires initial training and adaptation by radiologists, and a learning curve was not analyzed in this study. Second, profits may vary depending on the analyzed modality, such as x-ray, ultrasound, MRI or CT, which results from the length of the radiological report. Third, the results may vary across institutions, as workflows, caseloads, and individual preferences differ.

In conclusion, trigger-based reporting represents a significant step forward in optimizing radiology workflows. With further refinement and broader adoption, this approach has the potential to significantly improve the creation of medical documentation, ultimately enhancing patient care and clinical outcomes. However, the successful implementation of trigger-based workflows will require careful consideration of training, customization, and integration into existing systems. As radiology continues to adopt AI-driven technologies, the synergy between triggers and large language models holds promise for further advancements in efficiency and quality.