IoT-Driven Predictive Maintenance in Healthcare Facilities Using Machine Learning
Abstract
Predictive maintenance (PdM) in healthcare facilities can prevent equipment failures, reduce downtime, and ensure continuous patient care. This paper explores the application of IoT and machine learning for predictive maintenance in healthcare environments. We propose a PdM framework that utilizes IoT sensors to collect real-time data from medical equipment and machine learning algorithms to predict potential failures. A case study conducted in a hospital setting demonstrates the effectiveness of the framework in reducing maintenance costs and improving equipment reliability. The study also discusses the challenges of implementing PdM in healthcare, including data integration and model accuracy.
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