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Updated: Sep 12, 2025

Non-fluoroscopic Catheter Tracking for Fluoroscopy Reduction in Interventional Electrophysiology
Published on: May 26, 2015
GS1 and RFID Integration for Enhanced Medical Device Traceability in Catheterisation Laboratories
Shozo Konishi1, Yuma Tanaka2, Kento Sugimoto1
1Department of Medical Informatics, Osaka University Graduate School of Medicine.
None:
This study presents a novel system that integrates GS1 barcodes and radio frequency identification (RFID) technology with the objective of enhancing the medical device traceability in catheterization laboratories. The necessity for precise tracking of medical devices, particularly in the context of cardiovascular procedures is underscored by the occurrence of sporadic product recalls. Despite the existence of traceability systems, no solution has effectively combined GS1 and RFID technologies to ensure precise tracking throughout the clinical process. In this study, an apparatus for reading and collecting RFID tags was developed and integrated with the radiology information system and medical device master database. The medical devices delivered to the hospital were linked to RFID tags by means of scanning the GS1 barcodes on their packaging. During the catheterization procedures, the tags of each medical device used were scanned to record the usage in real-time. The new system has already been employed in more than 500 catheterization procedures. In an analysis of 16 percutaneous coronary intervention procedures, the system demonstrated high accuracy in capturing the chronological order of device usage, with a mean Kendall's rank correlation coefficient of 0.95±0.12. While some discrepancies were observed when non-stock devices were used, the system demonstrated an overall robust reliability. The system, which combines GS1 and RFID technologies, enabled real-time recording of medical devices used in catheterization laboratories, thereby enhancing the traceability of medical devices. Potential future applications include the use of generative artificial intelligence to draft preliminary percutaneous coronary intervention reports, enable real-time detection of complications by comparing device usage patterns with an operator's past cases, and support retrospective analyses for educational and procedural improvements.
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