Web-Based Intravenous Fluid Treatment Monitoring Platform in Nursing Station

Kim, Yoonji; Seol, Jaehwang; Lee, Hyomin; Lee, Jaekyeong; Park, Jeong-Yun; Kim, Kwang Gi

doi:10.1177/15533506211065849

Cited by 1 publication

(1 citation statement)

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“…Another analysis shows that there is a 60-80% probability that IV therapy will be selected as the infusion therapy for hospitalized patients [6], and 17.71% of nursing work includes walking in the ward and visually managing IV administration. In the end, managing personal IVs requires a heavy workload and time commitment from nurses, which can lead to potential errors [39]. Therefore, we believe that our proposed system can create a synergy between safe treatment for patients and reduced workload for nurses.…”

Section: Discussionmentioning

confidence: 99%

Automatic Control System for Precise Intravenous Therapy Using Computer Vision Based on Deep Learning

Seol,

Lee,

Park

et al. 2023

IEEE Access

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Intravenous (IV) therapy provides a rapid therapeutic effect. However, errors in infusion rate can lead to over-dosing and under-dosing, potentially causing severe side effects for patients. To prevent incidents, medical staff visually monitors medication injection information (MIF) such as current flow rates (CFR) and injection volume. In this study, we propose an internet of things (IoT) system that automatically controls the CFR and remotely monitors the MIF. First, a peristaltic pump is designed to infuse parenteral fluids based on the ''drop-by-drop'' phenomenon. Second, a computer vision-based on deep learning algorithm provides real-time video and counts the fluid dropping to derive the MIF. Finally, the CFR is automatically applied as a feedback signal to regulate the infusion cycle of the peristaltic pump. After embedding all systems in our prototype, we evaluate the system performance according to IV therapy protocols used in clinical practice. In our experiments, the mean accuracy of the fluid injection using the peristaltic pump was 99.23% and the dropping count was the highest at 98.25%. Furthermore, the average accuracy of the CFR using the feedback system was 99.3%. Based on our results, we confirmed that automated control of infusion rate is possible in IV therapy and computer vision-based on deep learning can be utilized for feedback sensors and monitoring system. Therefore, we believe that our method is the practical solution that can increase patient safety and work efficiency of clinical staffs. INDEX TERMSIntravenous therapy, infusion flow rate, feedback control, computer vision-based on deep learning, Internet of Things, vision monitoring.

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Section: Discussionmentioning

confidence: 99%