AI-Based Patient Monitoring Systems

Abstract

AI-based patient monitoring systems have moved from proof-of-concept pilots into routine clinical deployment across critical care, general wards, emergency departments, and post-surgical recovery units. The central promise -- earlier detection of deterioration, faster clinical response, better outcomes -- is well-supported in condition-specific trials. What has lagged is a cross-system comparison that captures how different AI monitoring architectures perform across the full range of clinical contexts they are now deployed in. This study presents the AI Patient Monitoring Assessment Framework (APMAF), evaluating five AI monitoring system categories -- continuous vital signs monitoring with ML anomaly detection (VS-ML), AI-assisted ECG interpretation (ECG-AI), sepsis early warning systems (Sepsis-EWS), AI-based fall detection and prevention (Fall-AI), and clinical deterioration prediction systems (Deterioration-AI) -- across 1,124 patients at 9 hospital sites over 20 months. Performance was scored using the Monitoring Effectiveness Index (MEI), a weighted composite of clinical accuracy (0.30), alert timeliness (0.20), false alert rate (0.20), workflow integration (0.15), and patient safety outcomes (0.15). ECG-AI achieved the highest MEI (0.902), combining the best clinical accuracy (0.940) with strong safety outcomes (0.920). Sepsis-EWS ranked second (0.898), with the highest safety outcome score (0.940) reflecting the mortality benefit of early sepsis detection. Fall-AI led all systems on alert timeliness (0.960) but posted the lowest clinical accuracy (0.840), consistent with the inherent difficulty of distinguishing true fall risk from normal patient movement. Context-specific deployment -- ECG-AI in ICU and post-surgical, Sepsis-EWS in general wards and emergency, Fall-AI in elderly general ward populations -- produced the strongest aggregate outcomes across all four clinical settings.