Markov chain optimization of repair and replacement decisions of medical equipment

Abstract

• The study determines the optimal repair or replacement decision based on the product lifecycle data. • A Markov Decision Process (MDP) framework is proposed for optimizing repair decisions. • A dataset of medical device repair and maintenance is used to show the application of the model. • Product lifecycle data is essential for lowering repair and replacement costs. The cost of repair and maintenance of medical devices can be fairly burdensome to the healthcare industry. Healthcare providers often consider plans such as increasing in-house repairs, using multiple repair service providers, or timely replacement of devices to overcome this issue. This study aims to develop a data-driven Markov Decision Process (MDP) framework based on Discrete-Time Markov Chain (DTMC) model to optimize medical equipment repair and replacement decisions. Effective decision-making on whether to repair or replace is crucial to managing the product lifecycle cost and the costs to healthcare facilities. The study determines the optimal repair or replacement decision based on the product lifecycle data and current product failure status. It utilizes a net present value model to maximize expected value over an infinite time horizon. The study uses a dataset of 24,516 repair and maintenance records of 5,171 individual medical devices of a particular type to extract parameters needed for the optimization model. The dataset provides a rich baseline for analyzing different failures and event modes during product lifespan such as battery-related issues, random failure, preventive maintenance, and physical damage. It further quantifies the chance of moving from one product status to another. The model outcomes are discussed for a particular case. The findings reveal the most frequent reasons for failures and the most economically viable repair and replacement decision for each end-of-use device based on their current condition. Several sensitivity analyses are conducted to clarify the impact of operating revenue and warranty time on the repair or replacement.