International Journal of Science, Engineering and TechnologyAuthors: Wasiur Rahman, Mohd Rahman
Abstract: Healthcare systems are rapidly evolving with the integration of Artificial Intelligence and intelligent automation technologies. Traditional hospital management systems often face challenges such as excessive paperwork, delayed patient services, inefficient resource utilization, data management issues, and lack of real-time decision-making capabilities. To overcome these limitations, this research proposes a Gen AI Driven Hospital Automation Framework that utilizes Generative Artificial Intelligence to automate and optimize hospital operations efficiently and securely. The proposed framework integrates advanced technologies such as Generative AI, Machine Learning (ML), Natural Language Processing (NLP), Cloud Computing, Internet of Things (IoT), predictive analytics, and database management systems to create an intelligent healthcare ecosystem. The system automates key hospital functions including patient registration, appointment scheduling, electronic health record management, AI-assisted diagnosis, pharmacy and inventory management, billing systems, emergency response services, and healthcare monitoring. The framework employs AI-powered virtual assistants and chatbots to provide real-time patient communication, symptom analysis, appointment support, and automated healthcare guidance. Machine Learning algorithms analyze patient data, medical history, and clinical reports to assist doctors in accurate diagnosis and treatment recommendations. Additionally, cloud-based healthcare infrastructure enables secure data storage, real-time accessibility, remote healthcare services, and efficient coordination between hospital departments. The proposed system also focuses on healthcare security and privacy by implementing encrypted cloud storage, role-based access control, authentication mechanisms, and secure patient record management. Automation of repetitive administrative tasks reduces human errors, minimizes operational costs, improves healthcare efficiency, and allows medical staff to focus more on patient care. Experimental analysis and system evaluation demonstrate that the proposed framework significantly improves hospital performance by reducing patient waiting time, increasing diagnosis accuracy, enhancing communication efficiency, optimizing resource management, and improving patient satisfaction. The intelligent automation capabilities of the system contribute toward building smart hospitals capable of delivering faster, safer, scalable, and personalized healthcare services. This research highlights the transformative potential of Generative AI in modern healthcare and contributes toward the development of intelligent next-generation hospital automation systems.
