Extrapolative Preservation Management of Medical Equipment through IoT

The healthcare industry is primarily driven by technological innovations as they are a significant source of inspiration to provide the best patient care. To provide patients with prompt, high-quality care, medical device manufacturing has grown at an unprecedented rate. Hospitals should use the best maintenance techniques to increase the life of their equipment while trying to reduce maintenance costs and efforts as medical device usage increases. This paper proposes a predictive maintenance method (PdM) to assist in identifying critical equipment failures with diverse and frequent failure modes. The proposed method is based on the understanding of the physics of the incident, real-time data collection of relevant parameters using Internet of Things (IoT) technology, and application of machine learning algorithms to predict and classify the condition of the device as good and bad. To demonstrate the feasibility and effectiveness of moving from traditional maintenance to PdM, an economic analysis should be provided. The objective of this study was to determine the challenge of medical device maintenance using an Internet of Things (loT) enabled automated health monitoring system for devices that generate large amounts of data. real-time data, at scale, in healthcare organizations.