AI-Integrated Smart Clinical Infrastructure for Adaptive Patient Outcome Prediction and Healthcare Automation

Abstract

The healthcare industry is undergoing a quick digital revolution, and there is a significant demand for intelligent clinical infrastructure which can present enhanced patient care and effective operational efficiency while boosting healthcare automation. Hospital systems tend to have poor clinical decision making, lack of patient monitoring, poor predictive capabilities and poor resource utilization in traditional hospital systems. This paper outlines an AI-centric smart clinical infrastructure for adaptive patient outcome prediction and healthcare automation with the support of Artificial Intelligence, Internet of Things (IoT), cloud-edge computing and predictive analytics. It envisions a smart hospital environment that connects various components, such as wearable biosensors, real-time patient monitoring systems, AI-powered clinical decision support tools, and automated healthcare management modules. Hybrid deep learning models are applied to the analysis, monitoring and intelligent treatment recommendation of patients’ risks and diseases. Experimental analysis shows that it outperforms conventional approaches in terms of the prediction accuracy, response latency, reliability of the patient monitoring and efficient automation of healthcare. The proposed framework enables adaptive, scalable and intelligent healthcare delivery for next generation smart hospitals, and offers great opportunities for personalized medicine, for remote healthcare monitoring and for autonomous clinical management systems.