The silent build-up of micro-failures—momentary glitches, signal distortion, or subtle calibration drifts—creates a hidden risk for patient care and hospital operations.

Staff may operate under the false premise of reliability, with life-critical decisions resting on compromised results.

Maintenance teams are then forced into reactive fire-fighting, undermining trust and efficiency, as no clear way exists to detect or predict these subtle hardware anomalies proactively.

Existing device monitoring solutions focus on binary states (working/failure), missing low-level anomaly patterns due to lack of highly granular, real-time data and AI-powered pattern recognition.

Hospitals lack the technical tools to continuously monitor device health at the micro-event level, largely due to proprietary hardware limits, insufficient sensor integration, and the absence of unified analytics platforms.