ScholarWorks@성균관대학교

초록

Medication errors pose a significant threat to patient safety. Although Bar-Code Medication Administration (BCMA) has reduced error rates, it is constrained by handheld devices, workflow interruptions, and incomplete safeguards against wrong patients, wrong doses, or drug incompatibility. In this study, we developed and evaluated a next-generation BCMA system by integrating artificial intelligence and mixed reality technologies for real-time safety checks: Optical Character Recognition verifies medication-label concordance, facial recognition confirms patient identity, and a rules engine evaluates drug-diluent compatibility. Computer vision models achieved high recognition accuracy for drug vials (100%), medication labels (90%), QR codes (90%), and patient faces (90%), with slightly lower performance for intravenous fluids (80%). A mixed-methods evaluation was conducted in a simulated environment using the System Usability Scale (SUS), Reduced Instructional Materials Motivation Survey (RIMMS), Virtual Reality Sickness Questionnaire (VRSQ), and NASA Task Load Index (NASA-TLX). The results indicated excellent usability (median SUS = 82.5/100), strong user motivation (RIMMS = 3.7/5), minimal cybersickness (VRSQ = 0.4/6), and manageable cognitive workload (NASA-TLX = 31.7/100). Qualitative analysis highlighted the system's potential to streamline workflow and serve as a digital "second verifier." These findings suggest strong potential for clinical integration, enhancing medication safety at the point of care.

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