A Data Driven Framework for Hospital Management Using Machine Learning and IoT Integration

Healthcare systems is challenged due to increase patient volumes, limited resources, operational inefficiencies, and administrative complexity. Traditional hospital management method often struggles to asses big volume of clinical and operational datas, leading to delay, increased costs, and reduce quality of care. This paper find the applicability of Artificial Intelligence (AI) and Machine Learning (ML) can improve hospital management by analyze large empirical dataset efficiently and accurate. Various AI approach using Electronic Health Record (EHR), patient flow data, diagnostic image, and resource utilization informations is reviewed with a focus on advance algorithm such as neural networks, predictive analytic, and graph-based model that can interprete complex clinical relationship. Critical issue discussed include data privacy, algorithmic bias, transparency and regulatory compliances in healthcare. The use for explainable AI system which can be trusted by clinician and patients while protecting sensitive health informations are emphasized. A case study are presented demonstrating improve operational efficiency, reduce waiting time, enhance diagnostic accuracy, and cost saving. Future technology such as monitoring in real time, telemedicine integration and adaptive learning system for continuous improvement also is explored. Overall, this research underscore the potential of AI to transform hospital and medical management while balancing technological advance with ethical concern.