Predictive Analytics in Device Failure Prevention

Abstract

Medical device failures cause preventable patient harm, unplanned clinical interventions, and in life-critical applications, death. Reactive maintenance -- responding to failure after it occurs -- is the dominant paradigm in device fleet management, but it is poorly suited to devices where failure consequences are severe and warning time is short. Predictive analytics offers an alternative: identify devices approaching failure before clinical impact, enabling planned intervention rather than emergency response. This study presents the Predictive Device Failure Analytics Framework (PDFAF), evaluating five analytics approaches -- time-series anomaly detection (TS-AD), machine learning survival analysis (ML-SA), digital twin-based predictive maintenance (DT-PM), multivariate sensor fusion with ensemble models (SF-EM), and federated learning for cross-site failure prediction (FL-FP) -- across four device failure contexts: implantable cardiac devices, orthopaedic implants, infusion and insulin pump systems, and ventilator and life support equipment. Performance was scored using the Failure Prevention Effectiveness Score (FPES), a weighted composite of prediction accuracy (0.30), early warning lead time (0.20), false alarm rate (0.20), implementation feasibility (0.15), and regulatory compliance (0.15). Multivariate sensor fusion with ensemble models achieved the highest FPES (0.887), driven by the best early warning lead time (0.940) and strong prediction accuracy (0.920). Time-series anomaly detection ranked second (0.880) with the highest lead time for cardiac implant failure specifically (median 18.4 days before clinical failure). Digital twin-based approaches achieved the best prediction accuracy (0.940) but were constrained by implementation feasibility (0.780). Federated learning demonstrated unique value for rare failure modes, achieving 0.900 on false alarm rate by pooling failure data across sites unavailable to single-institution models.