Design and Optimization of Smart Biomedical Equipment Maintenance Systems Using Predictive Analytics

Efficient maintenance of biomedical equipment is critical for ensuring patient safety, device reliability, and cost-effective healthcare delivery. Conventional maintenance methods, such as preventive and corrective maintenance, sometimes fail to anticipate unforeseen breakdowns. In order to include predictive analytics into biomedical equipment management systems, this study suggests a smart maintenance framework. The system may anticipate possible faults before they happen by using sensor data, past maintenance records, and machine learning algorithms. System architecture, data processing methods, and implementation difficulties in hospital settings are all included in the study. The suggested strategy lowers operating expenses, increases equipment uptime, and raises the standard of healthcare services.