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Principles for Developing a Large-Scale Point-of-Care Ultrasound Education Program: Insights from a Tertiary University Medical Center in Israel

delete2025-05-22
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OA
AI
R
Roy Rafael Dayan
B
Ben Shitrit, Itamar
G
Gaufberg, Rachel
I
Ilan, Karny
F
Fuchs, Lior
DOI:10.5334/pme.1613delete
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Abstract

Abstract

En 中文
Background & Need for Innovation: Point-of-care ultrasound (POCUS) has transformed bedside diagnostics, yet its operator-dependent nature and lack of structured training remain significant barriers. To address these challenges, Ben Gurion University (BGU) developed a longitudinal six-year POCUS curriculum, emphasizing early integration, competency-based training, and scalable educational strategies to enhance medical education and patient care. Goal of Innovation: To implement a structured and scalable POCUS curriculum that progressively builds technical proficiency, clinical judgment, and diagnostic accuracy, ensuring medical students effectively integrate POCUS into clinical practice. Steps Taken for Development and Implementation: The curriculum incorporates handson training, self-directed learning, a structured spiral approach, and peer-led instruction. Early exposure in physics and anatomy courses establishes a foundation, progressing to bedside applications in clinical years. Advanced technologies, including AI-driven feedback and telemedicine, enhance skill retention and address faculty shortages by providing scalable solutions for ongoing assessment and feedback. Evaluation of Innovation: Since its implementation in 2014, the program has trained hundreds of students, with longitudinal proficiency data from over 700 students. Internal studies have demonstrated that self-directed learning modules match or exceed in-person instruction for ultrasound skill acquisition, AI-driven feedback enhances image acquisition, and early clinical integration of POCUS positively influences patient care. Preliminary findings suggest that telemedicine-based instructor feedback improves cardiac ultrasound proficiency overtime, and AI-assisted probe manipulation and self-learning with ultrasound simulators may further optimize training without requiring in-person instruction.
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Journal

P
Perspectives on Medical Education
IF:
3.9
Papers:
501
Citations:
2.5K

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