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Evaluation of optimal education level to implement structured reporting into ultrasound training.
Benjamin Philipp Ernst1, Sebastian Strieth2, Julian Künzel3
1Department of Otorhinolaryngology, University Medical Center Mainz, Mainz, Germany. benjamin.ernst@unimedizin-mainz.de.
Structured reporting significantly improves head and neck ultrasound (HNU) report quality and efficiency for trainees. Early implementation during medical school offers the greatest benefits for training.
Area of Science:
- Medical Imaging
- Ultrasound Training
- Medical Education
Background:
- Head and neck ultrasound (HNU) reporting lacks standardization, leading to variable report quality and potential miscommunication.
- This variability poses challenges in ultrasound training and impacts patient care.
- Free text reports (FTR) often lack structure, hindering consistent information delivery.
Purpose of the Study:
- To compare the quality of free text reports (FTR) versus structured reports (SR) in head and neck ultrasound (HNU).
- To evaluate the impact of structured reporting at different training levels (medical students, junior residents, senior residents).
- To determine the optimal educational stage for implementing structured reporting in HNU training.
Main Methods:
- Medical students and residents (junior and senior) generated HNU reports using both SR and FTR formats.
- Reports were assessed for overall quality, time efficiency, and readability.
- User satisfaction was measured via a questionnaire.
Main Results:
- Structured reports (SR) demonstrated significantly higher quality (93.1% vs. 45.6%) across all training levels.
- SR significantly improved time efficiency (89.4 s vs. 160.2 s), particularly in early training stages.
- User satisfaction was markedly higher with SR (VAS 8.6 vs. 3.9).
Conclusions:
- Structured reporting enhances HNU report quality effectively at all training stages.
- Implementing SR early in medical school training yields the most significant time efficiency gains.
- Early adoption of structured reporting for HNU is recommended to optimize training and report consistency.
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