Comparing Artificial Intelligence-Based Versus Conventional Endotracheal Tube Monitoring Systems in Clinical Practice.

ABSTRACT

Endotracheal tube dislodgement is a common patient safety incident in clinical settings. Current clinical practices, primarily relying on bedside visual inspections and equipment checks, often fail to detect endotracheal tube displacement or dislodgement promptly. This study involved the development of a deep learning, artificial intelligence (AI)-based system for monitoring tube displacement. We also propose a randomized crossover experiment to evaluate the effectiveness of this AI-based monitoring system compared to conventional methods. The assessment will focus on immediacy in detecting and handling of tube anomalies, the completeness and accuracy of shift transitions, and the degree of innovation diffusion. The findings from this research are expected to offer valuable insights into the development and integration of AI in enhancing care provision and facilitating innovation diffusion in medical and nursing research.