Improving Fall Prevention Strategies in United States Hospitals: A Data-Driven Approach to Patient Safety and Cost Reduction While Supporting National Health Priorities

Hospital falls represent a critical public health challenge within the United States healthcare system, affecting approximately 700,000 to 1,000,000 patients annually in acute care settings, with 30–35% resulting in injury. These incidents negatively impact patient outcomes, hospital efficiency, and healthcare costs. The complexity of fall events necessitates a technology-enabled approach to prevention and risk reduction. Advanced predictive analytics and artificial intelligence (AI) offer promising solutions to this persistent issue. This study introduces an innovative data-driven approach that integrates predictive analytics, AI-based risk assessments, and evidence-based interventions. By combining machine learning algorithms with comprehensive risk assessment protocols, healthcare institutions can develop dynamic, personalized fall prevention strategies that enhance patient safety while reducing costs. This approach demonstrates potential for significant improvements, with estimated national savings of approximately $1.82 billion annually. Participating hospitals reported outcomes such as up to 98.9% accuracy in fall risk prediction and a 66.7% reduction in fall incidents, reinforcing the role of AI in improving safety. The framework is distinguished by its integration of real-time monitoring, machine learning, and clinical workflow adaptation, allowing for responsive, patient-specific interventions that evolve during hospitalization. Furthermore, it emphasizes multidisciplinary collaboration, technological integration, and continuous performance monitoring to support a scalable and adaptive fall prevention strategy.