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Development of an Integrated Catheter Insertion Training Simulator and Performance Monitoring System
Paul Stone, Subhashini Ganapathy
Pages - 16 - 28     |    Revised - 31-10-2021     |    Published - 01-12-2021
Volume - 10   Issue - 1    |    Publication Date - December 2021  Table of Contents
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KEYWORDS
Catheterization, Training, Artificial Intelligence, Medical Simulation.
ABSTRACT
Catheters are used in a wide range of procedures such as insertion of stents or drains and are increasingly utilized. Currently experience or judgement is used in intravenous catheter selection and, while this can be a reasonably successful approach, it is felt that improvements could be made by utilizing a combination of historical data analysis and machine learning algorithms and Artificial Intelligence (AI) to improve catheter selection performance and assessment in earlystage catheterization training.

Current training lacks consistency, is expensive, and requires access to a both surgeons and test cadavers. There is therefore a requirement for research to cover means to improve and standardize catheter selection and catheterizationassessment methods, especially in emergency situations. An system with automated wall-hit detection and evidence-based catheter selection could provide additional practice time to medical students in their initial training. Combining this performance tracking to give consistent, qualitative feedback to students and instructors can potentially reduce training times and subsequently improve catheterization performance and patient outcomes.

This study covers the conceptualization, initial modelling, and requirements definition for such an application. Key to this is establishing performance metrics and a means to assess them. There are two critical performance measures in catheter insertion: ‘wall-hits’ or the number of times the catheter tip hits the side of the vein and procedure time. Establishing feedback loops in the training system reinforces learning by enabling real-time awareness and faster correction of mistakes.

While the application would initially be aimed at monitoring performance during training, this could be expanded to monitor performance throughout medical use of intravenous catheters. Several risks and challenges remain in the development of a solution, and are subject to ongoing research.
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Mr. Paul Stone
Department of Biomedical, Industrial and Human Factors Engineering, Wright State University, Dayton, OH,45435 - United States of America
stone.123@wright.edu
Dr. Subhashini Ganapathy
Department of Biomedical, Industrial and Human Factors Engineering, Wright State University, Dayton, OH,45435 - United States of America