Development of a Smartphone-Based Inventory Management System for Emergency Carts

Inventory management for emergency carts is one of the routine tasks in hospitals. It is highly desirable to simplify the workflow of the inventory task since healthcare staffs always work under high pressure and heavy workload. In this study, we exploit computer vision technology to develop an auto...

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Veröffentlicht in:Journal of medical systems Jg. 49; H. 1; S. 133
Hauptverfasser: Liu, Chia-Hui, Wu, Nian-Yin, Liu, Che-Chia, Kuo, Wen-yin, Lin, Tzu-Chia, Lin, Wei-Yang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 10.10.2025
Springer Nature B.V
Schlagworte:
Algorithms
Automation
Carts
Computer vision
Continuity of care
Deep learning
Digitization
Drugs
Emergency management
Emergency medical care
Health care
Health care industry
Health Informatics
Health Sciences
High pressure
Hospitals
Humans
Inventories, Hospital - organization & administration
Inventory
Inventory management
Medical personnel
Medical supplies
Medicine
Medicine & Public Health
Nurses
Nursing
Object recognition
Pediatrics
Radio frequency identification
Smartphone
Smartphones
Statistics for Life Sciences
Workflow
Working conditions
Workload
Workloads
Deep learning
Healthcare materials management
Computer vision
Emergency carts
Medication inventory management
ISSN:1573-689X, 0148-5598, 1573-689X
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Zusammenfassung:Inventory management for emergency carts is one of the routine tasks in hospitals. It is highly desirable to simplify the workflow of the inventory task since healthcare staffs always work under high pressure and heavy workload. In this study, we exploit computer vision technology to develop an automated inventory management system for emergency carts. We have conducted a user study evaluate our proposed system. The subjects are 37 nurses from the internal medicine department and the pediatrics department of Chia-Yi Christian Hospital. The time spending on the inventory task before and after using our proposed system are compared. We also evaluate several state-of-the-art object detection algorithms (YOLOv4, YOLOv5, YOLOv7, and YOLOv5-OBB) for the task of automatic drug counting. We collect 500 images to train these object detection models and evaluate their accuracy. After using the proposed system, the time spent on the inventory task is reduced from 9.59 min to 5.32 min in average. The user study also indicates that the average workload score of the nurses is reduced from 3.81 to 2.70. For the evaluation of automatic drug counting, our proposed hybrid approach, which combines YOLOv5-OBB and YOLOv7, yields the highest accuracy. The resulting over detection rate and missed detection rate are 1.29% and 3.27%, respectively. In this study, we demonstrate that smartphone-based inventory management system using computer vision technology, specifically YOLO series object detectors, can effectively streamline inventory workflow and reduce the workload for healthcare personnel.
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ISSN:1573-689X
0148-5598
1573-689X
DOI:10.1007/s10916-025-02268-y