Optimizing Hospital Resource Management with IoT and Machine Learning: A Case Study in Predictive Maintenance

In particular in critical care settings, effective hospital resource management is essential to guarantee continuous healthcare services. An IoT and machine learning (ML)-driven framework for predictive maintenance in hospital infrastructures is presented in this work. Real-time data on operational parameters is gathered and examined by means of IoT sensors installed on medical equipment and building tools. To foresee equipment breakdown before occurrence, a Random Forest-based predictive maintenance model was put in use. Our case study done in a tertiary care hospital showed a 20% increase in maintenance cost efficiency and a 27% decrease in unanticipated equipment downtime. The results highlight how well smart predictive systems might improve operational resilience, lower costs, and guarantee patient safety.