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How does AI improve patient monitoring and care in hospitals?

AI transforms hospital monitoring from reactive alarms to proactive intervention. Continuous vital sign analysis uses machine learning on heart rate, blood pressure, oxygen saturation, and respiratory rate to predict patient deterioration 6-48 hours before clinical crisis. Sepsis prediction algorithms achieve 80-90% accuracy, triggering interventions that reduce mortality by 20-30% and save $15K-$40K per prevented case. ICU monitoring integrates data from ventilators, infusion pumps, and monitors, using AI to optimize treatment settings, predict extubation readiness, and identify subtle decompensation patterns. Smart alarm systems reduce false alarms by 50-70% through contextual analysis, addressing alarm fatigue while maintaining safety. Fall risk assessment analyzes patient movement patterns, gait stability, and clinical factors to predict which patients need enhanced precautions. Pressure ulcer prevention uses computer vision monitoring patient positioning, alerting nurses when repositioning is needed. Medication management AI checks for drug interactions, dosing errors, and allergy conflicts in real-time, preventing adverse drug events worth $4K-$25K each. Nursing workflow optimization predicts patient needs (pain medication, bathroom assistance), enabling proactive care before call lights. Bed management and discharge planning AI forecasts admissions, discharges, and transfers, optimizing patient flow and reducing boarding in emergency departments by 15-25%. The overall impact: earlier intervention, reduced complications, shorter lengths of stay, and improved safety through intelligent prediction and prioritization.