Introduction Eye tracking technology, checklists and search strategies have been demonstrated as useful aids in image interpretation. A training tool was developed, by the research team, which included these features. This study aimed to evaluate the effect of the training tool on participant image interpretation performance. Methods The study was carried out with reporting radiographers who had either commenced training in chest image interpretation (n = 12) or were trained in musculoskeletal image interpretation (n = 23) (total n = 35). Participants were allocated to a control or intervention group. Participants completed an initial assessment at recruitment and re-attended nine months later for a follow-up assessment. The intervention group were given unlimited access to a digital training tool. During assessments participants interpreted 20 chest images whilst using eye tracking technology (total of 1400 images were interpreted). A confidence level was obtained from participants on their diagnosis and a questionnaire, to obtain demographic data, was completed following the assessment. Results Improvements were seen in the confidence of intervention group participants (p < 0.05). False Positive (FP) scores decreased for both the control and intervention group (p < 0.05), this decrease was from 4.20 to 3.20 for the control group and from 5.87 to 3.27 for the intervention group. True Negative (TN) scores increased, from 5.13 to 6.73 for the intervention group (p < 0.05). Mean decision time decreased for both the control and intervention group. Conclusion The tool led to positive effects on participant performance and could be a useful aid in chest image interpretation learning. Implications for practice Improvements in performance were observed with a digital tool. The tool could improve image interpretation methods and training.
Bibliographical noteFunding Information:
This work formed part of a PhD project which was supported by the Department for Employment and Learning.
A sincere thank you to the participants of the study. The time and commitment you gave to the study is greatly appreciated and allowed it to be possible. Thank you to those who helped in the creation of the tool, in particular Dr Nicholas Woznitza and Dr Ayman Elsayed.
- Digital training tool
- Image interpretation