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

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dc.contributor.authorDriver, Lachlan
dc.contributor.authorDuggan, Nicole M.
dc.contributor.authorBrower, Chares
dc.contributor.authorEbnali, Mahdi
dc.contributor.authorBaymon, Da’Marcus
dc.contributor.authorWagner, Alexei
dc.contributor.authorDias, Roger
dc.contributor.authorSamir, Anthony E.
dc.contributor.authorKapur, Tina
dc.contributor.authorGoldsmith, Andrew J.
dc.contributor.authorBaugh, Christopher W.
dc.date.accessioned2023-12-26T18:42:28Z
dc.date.available2023-12-26T18:42:28Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.474
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other659c3497-7b93-4cd5-b23f-58fc3f401303
dc.identifier.urihttps://hdl.handle.net/10125/106857
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectTechnology, Machine Learning, and Bias in Emergency Care
dc.subjectartificial intelligence (ai)
dc.subjectmachine learning
dc.subjectmedical imaging
dc.subjectpoint-of-care ultrasound (pocus)
dc.subjectultrasound education
dc.titleMachine Learning Models for Point-of-care Ultrasound Education and Training: National Cost Savings and Expert Time Reduction
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractPoint-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.
dcterms.extent7 pages
prism.startingpage3915

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Technology, Machine Learning, and Bias in Emergency Care