Machine learning and failure mode and effects analysis for sustainable medical equipment spare parts management

Abstract

Spare parts for essential medical equipment are vital to ensure uninterrupted healthcare provision, especially in areas where resources are limited. This paper presents an integrative approach that integrates failure mode and effects analysis (FMEA) with supervised machine learning (ML) models in order to allow better predictive maintenance and optimize management of spare parts. In order to increase the accuracy of the forecast, FMEA was first used to compute risk priority numbers (RPNs) of the components, quantifying the risk of each component, and then using it as an important input feature to the ML algorithms. Data from six hospitals and ten critical medical devices, such as ventilators, defibrillators, dialysis machines, and computed tomography (CT) scanners, were analyzed to identify high-risk failure modes and prioritize maintenance tasks. Three different supervised learning models (random forest (RF), artificial neural network (ANN), and support vector machine (SVM)) was built and tested based on four metrics: accuracy, precision, recall, and F1-score. The precision of RF and ANN was 1.00 and SVM showed an excellent recall value of 0.94, indicating promising results for practical applications. The solution proposed consists of a hierarchical decision system, connecting risk assessment carried out by experts with data-driven forecasts, to create proactive and sustainable management of spare parts. This approach helps healthcare entities lessen the risk of equipment breakdowns, adhere to international healthcare standards, and increase system reliability and efficiency.