Predicting medical device failure: a promise to reduce healthcare facilities cost through smart healthcare management

Abstract

Background The advancement of biomedical research generates myriad healthcare-relevant data, including medical records and medical device maintenance information. The COVID-19 pandemic significantly affects the global mortality rate, creating an enormous demand for medical devices. As information technology has advanced, the concept of intelligent healthcare has steadily gained prominence. Smart healthcare utilises a new generation of information technologies, such as the Internet of Things (loT), big data, cloud computing, and artificial intelligence, to completely transform the traditional medical system. With the intention of presenting the concept of smart healthcare, a predictive model is proposed to predict medical device failure for intelligent management of healthcare services. Methods Present healthcare device management can be improved by proposing a predictive machine learning model that prognosticates the tendency of medical device failures toward smart healthcare. The predictive model is developed based on 8,294 critical medical devices from 44 different types of equipment extracted from 15 healthcare facilities in Malaysia. The model classifies the device into three classes; (i) class 1, where the device is unlikely to fail within the first 3 years of purchase, (ii) class 2, where the device is likely to fail within 3 years from purchase date, and (iii) class 3 where the device is likely to fail more than 3 years after purchase. The goal is to establish a precise maintenance schedule and reduce maintenance and resource costs based on the time to the first failure event. A machine learning and deep learning technique were compared, and the best robust model for smart healthcare was proposed. Results This study compares five algorithms in machine learning and three optimizers in deep learning techniques. The best optimized predictive model is based on ensemble classifier and SGDM optimizer, respectively. An ensemble classifier model produces 77.90%, 87.60%, and 75.39% for accuracy, specificity, and precision compared to 70.30%, 83.71%, and 67.15% for deep learning models. The ensemble classifier model improves to 79.50%, 88.36%, and 77.43% for accuracy, specificity, and precision after significant features are identified. The result concludes although machine learning has better accuracy than deep learning, more training time is required, which is 11.49 min instead of 1 min 5 s when deep learning is applied. The model accuracy shall be improved by introducing unstructured data from maintenance notes and is considered the author’s future work because dealing with text data is time-consuming. The proposed model has proven to improve the devices’ maintenance strategy with a Malaysian Ringgit (MYR) cost reduction of approximately MYR 326,330.88 per year. Therefore, the maintenance cost would drastically decrease if this smart predictive model is included in the healthcare management system.

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

“…A comparative analysis was performed, revealing ML’s superiority over DL in accuracy, precision, specificity, and F1 score. Although DL has a shorter training time, its accuracy is lower than that of ML ( Rahman et al, 2023 ). However, according to the preliminary studies by Zhai & Qiao (2020) , DL’s excellent performance is primarily due to a massive amount of training data and a deep network topology.…”

Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms

“…Moreover, the Levenberg-Marquardt neural network-based lifetime extension approach optimizes the lifetime of devices and systems [21]. The neural network algorithm improves the maintenance and performance monitoring of medical devices used in healthcare, reducing costs and increasing service quality [22]. These literature studies demonstrate how machine learning innovations can be applied in healthcare and the potential benefits of these applications.…”

MediLog: A Pilot Study of Online Management System for Medical Device Status and Loan

“…The utilization of aging medical assets raises concerns, as they are more prone to breakdowns compared to newer equipment. Hence, the disposal phase, as stipulated in the biomedical engineering life cycle, becomes crucial, marking the point when medical devices are no longer safe, beyond economical repair, have unattainable parts, significant damage, or are otherwise in poor condition (11). Regular maintenance of medical devices is paramount, particularly considering the complexity of certain machines found exclusively in intensive care units (ICUs) with crucial electrical connections linked to patient care.…”