Machine Learning Models for Point-of-care Ultrasound Education and Training: National Cost Savings and Expert Time Reduction
Files
Date
2024-01-03
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
3915
Ending Page
Alternative Title
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.
Description
Keywords
Technology, Machine Learning, and Bias in Emergency Care, artificial intelligence (ai), machine learning, medical imaging, point-of-care ultrasound (pocus), ultrasound education
Citation
Extent
7 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 57th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.