For decades, reliability engineering techniques have been successfully applied in many industries to improve the performance of equipment maintenance management. Numerous inspection and optimization models are developed and widely used to achieve maintenance excellence, i.e. the balance of performance, risk, resources and cost to reach to an optimal solution. However, the application of all these techniques and models to medical devices is new. Hospitals, due to possessing a large number of difference devices, can benefit significantly if the optimization techniques are used properly in the equipment management processes. Most research in the area of reliability engineering for medical equipment mainly considers the devices in their design or manufacturing stage and suggests some techniques to improve the reliability. To this point, best maintenance strategies for medical equipment in their operating context have not been considered. We aim to address this gap and propose methods to improve current maintenance strategies in the healthcare industry. More specifically, we first identify or propose the criteria which are important to assess the criticality of medical devices, and propose a model for the prioritization of medical equipment for maintenance decisions. The model is a novel application of multi-criteria decision making methodology to prioritize medical devices in a hospital according to their criticality. The devices with high level of criticality should be included in the hospital’s maintenance management program. Then, we propose a method to statistically analyze maintenance data for complex medical devices with censoring and missing information. We present a classification of failure types and establish policies for analyzing data at different levels of the device. Moreover, a new method for trend analysis of censored failure data is proposed. A novel feature of this work is that it considers dependent failure histories which are censored by inspection intervals. Trend analysis of this type of data has not been discussed in the literature. Finally, we introduce some assumptions based on the results of the analysis, and develop several new models to find the optimal inspection interval for a system subject to hard and soft failures. Hard failures are instantaneously revealed and fixed. Soft failures are only rectified at inspections. They do not halt the system, although they reduce its performance or productivity. The models are constructed for two main cases with the assumption of periodic inspections, and periodic and opportunistic inspections, respectively. All numerical examples and case studies presented in the dissertation are adapted from the maintenance data received from a Canadian hospital.

For decades, reliability engineering techniques have been successfully applied in many industries to improve the performance of equipment maintenance management. Numerous inspection and optimization models are developed and widely used to achieve maintenance excellence, i.e. the balance of performance, risk, resources and cost to reach to an optimal solution. However, the application of all these techniques and models to medical devices is new. Hospitals, due to possessing a large number of difference devices, can benefit significantly if the optimization techniques are used properly in the equipment management processes. Most research in the area of reliability engineering for medical equipment mainly considers the devices in their design or manufacturing stage and suggests some techniques to improve the reliability. To this point, best maintenance strategies for medical equipment in their operating context have not been considered. We aim to address this gap and propose methods to improve current maintenance strategies in the healthcare industry. More specifically, we first identify or propose the criteria which are important to assess the criticality of medical devices, and propose a model for the prioritization of medical equipment for maintenance decisions. The model is a novel application of multi-criteria decision making methodology to prioritize medical devices in a hospital according to their criticality. The devices with high level of criticality should be included in the hospital’s maintenance management program. Then, we propose a method to statistically analyze maintenance data for complex medical devices with censoring and missing information. We present a classification of failure types and establish policies for analyzing data at different levels of the device. Moreover, a new method for trend analysis of censored failure data is proposed. A novel feature of this work is that it considers dependent failure histories which are censored by inspection intervals. Trend analysis of this type of data has not been discussed in the literature. Finally, we introduce some assumptions based on the results of the analysis, and develop several new models to find the optimal inspection interval for a system subject to hard and soft failures. Hard failures are instantaneously revealed and fixed. Soft failures are only rectified at inspections. They do not halt the system, although they reduce its performance or productivity. The models are constructed for two main cases with the assumption of periodic inspections, and periodic and opportunistic inspections, respectively. All numerical examples and case studies presented in the dissertation are adapted from the maintenance data received from a Canadian hospital.