ASTRID: A Robotic Tutor for Nurse Training to Reduce Healthcare-Associated Infections

The central line dressing change is a life-critical procedure performed by nurses to provide patients with rapid infusion of fluids, such as blood and medications.Due to their complexity and the heavy workloads nurses face, dressing changes are prone to preventable errors that can result in central lineassociated bloodstream infections (CLABSIs), leading to serious health complications or, in the worst cases, patient death.In the post-COVID-19 era, CLABSI rates have increased, partly due to the heightened nursing workload caused by shortages of both registered nurses and nurse educators.To address this challenge, healthcare facilities are seeking innovative nurse training solutions to complement expert nurse educators.In response, we present the design, development and evaluation of a robotic tutoring system, ASTRID: the Automated Sterile Technique Review and Instruction Device.ASTRID, which is the outcome of a two-year participatory design process, is designed to aid in the training of nursing skills essential for CLABSI prevention.First, we describe insights gained from interviews with nurse educators and nurses, which revealed the gaps of current training methods and requirements for new training tools.Based on these findings, we outline the development of our robotic tutor, which interacts with nursing students, providing real-time interventions and summary feedback to support skill acquisition.Finally, we present evaluations of the system's performance and perceived usefulness, conducted in a simulated clinical setting with nurse participants.These evaluations demonstrate the potential of our robotic tutor in nursing education.Our work highlights the importance of participatory design for robotics systems, and motivates new avenues for foundational research in robotics.

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Publication Details

Published
2025-06-21
DOI
https://doi.org/10.15607/rss.2025.xxi.090
Citations
1
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
1.36
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ASTRID: A Robotic Tutor for Nurse Training to Reduce Healthcare-Associated Infections

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COVID-19 diagnosis using AI
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article

ASTRID: A Robotic Tutor for Nurse Training to Reduce Healthcare-Associated Infections

Vaibhav Unhelkar, Qing H. Meng, Shannan K. Hamlin, Lydia E. Kavraki, Peizhu Qian, Carlos Quintero, Filip Bajraktari
article en
1 citations

Abstract

The central line dressing change is a life-critical procedure performed by nurses to provide patients with rapid infusion of fluids, such as blood and medications.Due to their complexity and the heavy workloads nurses face, dressing changes are prone to preventable errors that can result in central lineassociated bloodstream infections (CLABSIs), leading to serious health complications or, in the worst cases, patient death.In the post-COVID-19 era, CLABSI rates have increased, partly due to the heightened nursing workload caused by shortages of both registered nurses and nurse educators.To address this challenge, healthcare facilities are seeking innovative nurse training solutions to complement expert nurse educators.In response, we present the design, development and evaluation of a robotic tutoring system, ASTRID: the Automated Sterile Technique Review and Instruction Device.ASTRID, which is the outcome of a two-year participatory design process, is designed to aid in the training of nursing skills essential for CLABSI prevention.First, we describe insights gained from interviews with nurse educators and nurses, which revealed the gaps of current training methods and requirements for new training tools.Based on these findings, we outline the development of our robotic tutor, which interacts with nursing students, providing real-time interventions and summary feedback to support skill acquisition.Finally, we present evaluations of the system's performance and perceived usefulness, conducted in a simulated clinical setting with nurse participants.These evaluations demonstrate the potential of our robotic tutor in nursing education.Our work highlights the importance of participatory design for robotics systems, and motivates new avenues for foundational research in robotics.

Openalex Percentile: Top 16%
COVID-19 diagnosis using AI
1.36
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