A comparative study of irrigation techniques and the development of a self-serve training model for ophthalmology residents

Background

Lacrimal irrigation is an important skill for diagnosing and treating eye conditions related to tear drainage. This study looks at two different techniques for lacrimal irrigation and introduces a new self-training model to help new ophthalmology residents learn these skills better.

Methods

In this study, 92 ophthalmology residents were divided into two groups. Both groups underwent an 8-hour training program that included lessons, demonstrations, and hands-on practice. Group A provided feedback to improve the training model, which was then used for Group B1, while Group B2 did not receive the enhanced training. The effectiveness of the training was measured through skill assessments and surveys on confidence and perceived effectiveness.

Results

In Group A, most participants (70.2%) preferred Technique 1 because it was easier to use, while 29.8% liked Technique 2 for its effectiveness in specific situations. Some challenges faced included a lack of proper training resources and anxiety. In Group B, those who used the new training model (Group B1) had higher confidence scores than those who did not (8.4 vs. 6.1). Although Group B1 showed a trend towards better skill assessment scores, the difference was not statistically significant.

Conclusion

Both irrigation techniques are accessible for beginners, with Technique 2 being better for certain situations. The new self-training model boosts confidence and helps overcome major training challenges. Future research should test these findings with more participants and improve the training model to include better simulations and feedback.

Opportunities Based on Trial Data

Measurable Outcomes: Establish clear goals for training effectiveness and skill acquisition in clinics.
AI Tools Selection: Choose AI solutions that meet specific clinical needs for training and skill assessment.
Implementation: Start with a pilot project using AI tools, monitor results, and expand based on the findings from this study.