Machine Learning Models for Point-of-care Ultrasound Education and Training: National Cost Savings and Expert Time Reduction

Abstract

Point-of-care ultrasound (POCUS) serves as a valuable diagnostic tool for healthcare providers. It enhances diagnostic accuracy and patient outcomes while reducing Emergency department (ED) length-of-stay and expenses. Nonetheless, barriers such as access to instructors and the costs of training novices impede widespread POCUS implementation. One alternative is artificial intelligence (AI) guided image acquisition tools. This study explores the potential national cost savings of employing AI acquisition software to teach POCUS to residents. A Monte Carlo simulation estimated the hours and costs of attending physician time needed for traditional versus AI-guided ultrasound education. The findings suggest that incorporating AI-guidance in ED resident ultrasound education could save $5.3 million annually in costs nation-wide. This cost-effective method holds the potential to maintain or even enhance quality of education while alleviating financial constraints. Investing in AI technology for medical education has the potential for improved patient care and streamlined workflows in healthcare environments.