Neonatal endotracheal intubation (ETI) is a time-sensitive resuscitation procedure! essential for ventilation of newborns. It requires an unusually high level of skill due to the narrow airways, relatively large tongue, anterior glottic position, and low respiratory reserve of neonates (Bercic, Pocajt et al. 1978). Given the difficulty of the procedure and the high rate of complications in untrained hands, effective training is crucial. However, intubation success rates for pediatric residents are low under current resuscitation training programs and show little improvement between years 1-3 of residency (23-25%) (O'Donnell, Kamlin et al. 2006; Haubner, Barry et al. 2013). There is a pressing need to understand the factors that lead to poor training results and for innovative training modalities that can bridge the gap left by traditional training and thereby allow rapid skill acquisition. We hypothesize that current training and assessment methods suffer from 4 key weaknesses: (1) Poor realism: manikin and simulator-based training typically provide little variation in anatomy or difficulty level?key requirements for developing expertise (Dreyfus, Athanasiou et al. 1986)?and do not realistically model the look, feel, and motions of real tissue. (2) Subjective, highly variable, and resource-intensive assessment methods: training opportunities are limited by the availability of expert instructors. (3) Poor visualization: learners have poor knowledge about what went wrong and how to improve; they cannot see exactly what is going on inside the manikin or the patient and cannot directly monitor their actions relative to idealized, expert performance. (4) Assessment under artificially ideal conditions: assessments of ETI performance in classroom settings likely overestimate trainees' skill level because they do not mimic the stressors and distractions that are inherent in the real clinical environment. Technology-enhanced ETI simulators can resolve all of these key weaknesses: We have conducted preliminary work (Hahn, Li et al. 2016; Soghier, Li et al. 2014) on an augmented reality (AR (Azuma 1997)) manikin simulator driven by the motions of the trainee and physical manikin in real time that 1) provides a quantitative assessment of ETI technique and 2) allows the trainee to visualize the motion of the laryngoscope inside the manikin. The assessment score can provide feedback during the performance, as well as constitute part of the evaluation of the trainee's skill. Work under this proposal will build on this preliminary work.
The specific aims are to: extend the current augmented reality (AR) manikin simulator to a virtual reality (VR) computer simulator and validate, extend and validate automated assessment and visualization algorithm for ETI, study training effectiveness by testing groups of pediatric residents across 3 years to quantify the effect of technology-enhanced methods relative to the current training regimen in terms of both intubation performance on simulators and clinical outcomes in patients, and assess performance under more realistic conditions.
The current training and assessment of neonatal endotracheal intubation (ETI) suffers from key weaknesses of 1) poor realism of task simulators, 2) subjective, highly variable, and resource-intensive assessment methods, 3) poor visualization during simulation, and 4) assessment methods under artificially ideal conditions. This high-yield proposal will bridge the gap between training and clinical practice by using quantitative assessment tools and technology-enhanced simulation to improve ETI performance prior to patient care.