Deep learning-based monitoring technique for real-time intravenous medication bag status

Abstract

Accidents related to the administration of intravenous (IV) medication, such as drug overdose/underdose, drug/patient mis-identification, and delayed bag exchange, occur consistently in clinical fields. Several previous studies have suggested various contact-sensing and image-processing methodologies; however, most of them can increase the workload of nursing staffs during the long-term, continuous monitoring. In this study, we proposed a smart IV pole that can monitor the infusion status of up to four IV medications (patient/drug identification, and liquid residue) with various sizes and hanging positions to reduce IV-related accidents and improve patient safety with the least additional workload; the system consists of 12 cameras, one code scanner, and four controllers. Two types of deep learning models for automated camera selection (CNN-1) and liquid residue monitoring (CNN-2), and three drug residue estimation equations were implemented. The experimental results demonstrated that the accuracy of identification code-checking (60 tests) was 100%. The classification accuracy and the mean inference time of CNN-1 (1200 tests) were 100% and 140 ms. The mean average precision and the mean inference time of CNN-2 (300 tests) were 0.94 and 144 ms. The average error rates between the alarm setting (20, 30, and 40 mL) and the actual drug residue when the alarm first generated were 4.00%, 7.33%, and 4.50% for a 1,000 mL bag; 6.00%, 4.67%, and 2.50% for a 500 mL bag; and 3.00%, 6.00%, and 3.50% for a 100 mL bag, respectively. Our results suggest that the implemented AI-based prototype IV pole is a potential tool for reducing IV-related accidents and improving in-hospital patient safety. Supplementary Information The online version contains supplementary material available at 10.1007/s13534-023-00292-w.

Influence of nursing time and staffing on medical errors: A cross-sectional analysis of administrative data

“…32 Recent studies focused on arti cial intelligence (AI) or computerized technologies to reduce medical errors by using systems such as computerized physician order entry, automated drug distribution system, AI-based prototype intravenous pole, and vital sign data within an AI framework. [33][34][35] Technologies may help reduce medical errors; hence, the most commonly identi ed issues to medication incidents were digital communication. 36 This study was conducted based on the hypothesis that for the same patient pro le, the risk of adverse events may increase with increase in ward activity.…”

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