A novel algorithm-driven hybrid simulation learning method to improve acquisition of endotracheal intubation skills:...

Area of Science:

• Medical Education
• Clinical Skills Training
• Simulation-Based Learning

Background:

• Simulation-based training offers realistic, interactive learning for clinical skills.
• Endotracheal intubation is a critical procedure requiring effective training methods.

Purpose of the Study:

• To compare a novel hybrid learning method with conventional simulation learning for endotracheal intubation (ETI) training.
• To evaluate the effectiveness of a hybrid approach incorporating distance learning and peer-to-peer simulation.

Main Methods:

• 100 medical students and residents were randomized into control (conventional simulation) and experimental (hybrid learning) groups.
• The hybrid group utilized distance learning and remote-supervised peer-to-peer simulation.
• ETI performance was assessed on real patients using a standardized checklist.

Main Results:

• The hybrid group demonstrated significantly higher ETI performance (88%) compared to the control group (52%).
• 100% of hybrid group participants followed the correct procedural sequence, versus 32% in the control group.
• The hybrid method showed high learner acceptability and satisfaction.

Conclusions:

• The algorithm-driven hybrid simulation learning method effectively improves endotracheal intubation acquisition.
• This novel approach surpasses classic simulation-based training in skill development.
• The hybrid method is well-accepted and satisfying for learners.