How AI Predicts Patient Falls Before They Happen

More than one million patient falls occur in U.S. hospitals each year, leading to fractures, longer stays, and liability costs exceeding $50 billion annually. Traditional risk scores like the Morse scale rely on manual assessments that miss 40% of fallers. In this episode, Lucas and Luna examine how Mount Sinai and Stanford Medicine are deploying computer vision and electronic health record algorithms that predict falls up to two hours in advance. They dig into a 2025 JAMA Internal Medicine study showing a 23% reduction in fall rates when nurses receive AI-generated alerts, and they debate the privacy trade-offs of using overhead depth sensors versus wearable tags. Fresh angle: the shift from reactive fall prevention (bed alarms, sitters) to proactive risk stratification—and why health systems are starting to buy this technology even without reimbursement codes. #AIHealthcare #PatientSafety #FallPrevention #ComputerVision #PredictiveAnalytics #MountSinai #StanfordMedicine #JAMAInternalMedicine #HealthTech #HospitalInnovation #MorseScale #DepthSensors #MedicalAI #NursingAI #RiskStratification #BusinessAndTechnology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo