Simulation-based Ultrasound Curriculum for Novice Clinicians to Assess Neonatal Endotracheal Tube Position.
J Med Ultrasound
; 31(1): 40-47, 2023.
Article
em En
| MEDLINE
| ID: mdl-37180626
ABSTRACT
Background:
To evaluate the efficacy of a simulation-based mastery curriculum to train clinicians with limited-to-no sonography experience how to use ultrasound (US) to assess neonatal endotracheal tube (ETT) positioning.Methods:
In a single-centered, prospective, educational study, 29 neonatology clinicians participated in a simulation-based mastery curriculum composed of a didactic lecture, followed by a one-on-one simulation session using a newly designed, three-dimensional (3D) printed US phantom model of the neonatal trachea and aorta. After mastery training, clinicians were evaluated with a performance checklist on their skills obtaining US images and assessing ETT positioning in the US phantom model. They also completed pre- and postcurriculum knowledge assessment tests and self-assessment surveys. The data were analyzed using Wilcoxon signed rank tests and repeated measures analysis of variance.Results:
The mean checklist score improved significantly during three attempts (mean difference 2.6552; 95% confidence interval [CI] 2.2578-3.0525; P < 0.0001). The mean time to perform US decreased significantly from the first to third attempt (mean difference -1.8276 min; 95% CI -3.3391 to - 0.3161; P = 0.0196). In addition, there was a significant improvement in median knowledge assessment scores (50% vs. 80%; P < 0.0001) and survey ratings on knowledge and self-efficacy (P < 0.0001).Conclusion:
Clinicians with limited-to-no sonography experience demonstrated improved knowledge and skill acquisition in using US to assess ETT positioning through simulation-based mastery training. The use of 3D modeling enhances simulation experiences and optimizes the quality of training during limited opportunities to achieve procedural competency in a controlled environment before further application into the clinical setting.
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Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article