AI-Enhanced Robotics for Efficient Hospital Facility Maintenance in Cloud Environments

This research presents a novel method for optimizing healthcare facility maintenance in cloud settings via AI-enhanced robots. Through interactions with hospital surroundings, robotic systems may automatically learn and improve maintenance procedures due to the incorporation of Reinforcement Learning (RL) algorithms. Cloud computing infrastructure makes Real-time data processing possible, making it easier to coordinate and make decisions for RL-driven robots that are spread around various medical settings. Key elements include RL algorithms for dynamic maintenance scheduling, adaptive response to facility conditions, and sensor-equipped robots that can perform tasks and navigate adaptively. This integrated framework aims to optimize resource utilization in healthcare facility management, improve safety standards, and reduce operational downtime. It may learn from their mistakes and become more proficient at maintenance duties. The solution provides strong monitoring and decision support by using cloud-based data analytics, which enables preemptive maintenance interventions and reduces interruptions to hospital operations. AI-based robotic system achieved a 32% decrease in maintenance expenses, a 45% enhancement in efficiency, and a 28% reduction in equipment failures, illustrating the efficacy of RL-based automation in healthcare facilities. This technique improves the general sustainability and dependability of managing healthcare facilities by challenging significant issues with operational continuity and maintenance effectiveness.