Developing Smart Hospital Management Systems with IoT and Big Data

Abstract

As integration of IoT and big data analytics in hospital management, healthcare has been revolutionized by the improvement in patient monitoring, resources optimization, and making of predictive decisions. This research proposes a new conceptualized Smart Hospital Management System (SHMS) based on Artificial Intelligence deployed algorithms such as Random Forest, K-Means clustering, Long Short Term Memory (LSTM), and Genetic Algorithm from the perspective of analyzing real time healthcare data to enhance hospital work flows. To test the system, it was applied to 500,000 patient records and real time IoT sensor data. It was shown that Random Forest has 94.3% accuracy in ICU admission prediction, K-Means clustering maximized hospital bed utilization by 87%, LSTM improved patient deterioration forecasting by 92.1%, and the Genetic Algorithm reduced emergency response time by 35%. The proposed AI powered model of the hospital management system is found to reduce cost of operations as well as efficiency in contrast to the traditional hospital management system. The superiority of the proposed approach to real time decision making and automation of the hospital is then compared with already existing methods. This research lays a foundation for the development of scalable, AI driven smart healthcare infrastructures while challenges of data privacy and interoperability remain. In future work we will seek to increase security, model scalability, and deployment for real world application to make hospitals more efficient and streamline patient care.