Optimizing Hospital Resource Management with IoT and Machine Learning: A Case Study in Predictive Maintenance
DOI:
https://doi.org/10.63682/jns.v14i24S.5910Keywords:
Predictive maintenance, hospital resource management, Internet of Things (IoT), machine learning, equipment downtime, healthcare optimization, Random Forest, real-time monitoring, smart hospitals, case studyAbstract
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.
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Gallab, M., Ahidar, I., Zrira, N., & Ngote, N. (2024). Towards a Digital Predictive Maintenance (DPM): Healthcare Case Study. Procedia Computer Science, 232, 3183–3194. https://doi.org/10.1016/j.procs.2024.02.134
Niyonambaza, I., Zennaro, M., & Uwitonze, A. (2020). Predictive Maintenance (PdM) Structure Using Internet of Things (IoT) for Mechanical Equipment Used into Hospitals in Rwanda. Future Internet, 12(12), 224. https://doi.org/10.3390/fi12120224
Shamayleh, A., et al. (2023). Integrated failure analysis using machine learning predictive system for hospital equipment. Engineering Applications of Artificial Intelligence, 122, 105-112. https://doi.org/10.1016/j.engappai.2023.105112
Singh, A., & Gupta, R. (2020). Predictive Maintenance in Industrial IoT: Applications and Challenges. IEEE Transactions on Industrial Informatics, 16(8), 5344-5353. https://doi.org/10.1109/TII.2019.2963462
Herasevich, V., et al. (2021). Artificial Intelligence in Early Sepsis Detection: A Machine Learning Approach. Critical Care Medicine, 49(1), 1-10. https://doi.org/10.1097/CCM.0000000000004718
Chen, I. Y., Joshi, S., Ghassemi, M., & Ranganath, R. (2020). Probabilistic Machine Learning for Healthcare. arXiv preprint arXiv:2009.11087. https://arxiv.org/abs/2009.11087
Gallab, M., et al. (2024). Towards a Digital Predictive Maintenance (DPM): Healthcare Case Study. Procedia Computer Science, 232, 3183–3194. https://doi.org/10.1016/j.procs.2024.02.134
Niyonambaza, I., et al. (2020). Predictive Maintenance (PdM) Structure Using Internet of Things (IoT) for Mechanical Equipment Used into Hospitals in Rwanda. Future Internet, 12(12), 224. https://doi.org/10.3390/fi12120224
Shamayleh, A., et al. (2023). Integrated failure analysis using machine learning predictive system for hospital equipment. Engineering Applications of Artificial Intelligence, 122, 105-112. https://doi.org/10.1016/j.engappai.2023.105112
Singh, A., & Gupta, R. (2020). Predictive Maintenance in Industrial IoT: Applications and Challenges. IEEE Transactions on Industrial Informatics, 16(8), 5344-5353. https://doi.org/10.1109/TII.2019.2963462
Herasevich, V., et al. (2021). Artificial Intelligence in Early Sepsis Detection: A Machine Learning Approach. Critical Care Medicine, 49(1), 1-10. https://doi.org/10.1097/CCM.0000000000004718
Chen, I. Y., et al. (2020). Probabilistic Machine Learning for Healthcare. arXiv preprint arXiv:2009.11087. https://arxiv.org/abs/2009.11087
Gallab, M., et al. (2024). Towards a Digital Predictive Maintenance (DPM): Healthcare Case Study. Procedia Computer Science, 232, 3183–3194. https://doi.org/10.1016/j.procs.2024.02.134
Niyonambaza, I., et al. (2020). Predictive Maintenance (PdM) Structure Using Internet of Things (IoT) for Mechanical Equipment Used into Hospitals in Rwanda. Future Internet, 12(12), 224. https://doi.org/10.3390/fi12120224
Shamayleh, A., et al. (2023). Integrated failure analysis using machine learning predictive system for hospital equipment. Engineering Applications of Artificial Intelligence, 122, 105-112. https://doi.org/10.1016/j.engappai.2023.105112
Singh, A., & Gupta, R. (2020). Predictive Maintenance in Industrial IoT: Applications and Challenges. IEEE Transactions on Industrial Informatics, 16(8), 5344-5353. https://doi.org/10.1109/TII.2019.2963462
Herasevich, V., et al. (2021). Artificial Intelligence in Early Sepsis Detection: A Machine Learning Approach. Critical Care Medicine, 49(1), 1-10. https://doi.org/10.1097/CCM.0000000000004718
Chen, I. Y., et al. (2020). Probabilistic Machine Learning for Healthcare. arXiv preprint arXiv:2009.11087. https://arxiv.org/abs/2009.11087
Gallab, M., et al. (2024). Towards a Digital Predictive Maintenance (DPM): Healthcare Case Study. Procedia Computer Science, 232, 3183–3194. https://doi.org/10.1016/j.procs.2024.02.134
Niyonambaza, I., et al. (2020). Predictive Maintenance (PdM) Structure Using Internet of Things (IoT) for Mechanical Equipment Used into Hospitals in Rwanda. Future Internet, 12(12), 224. https://doi.org/10.3390/fi12120224
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