A comprehensive fuzzy risk-based maintenance framework for prioritization of medical devices
Graphical abstract
Introduction
Nowadays, safety of medical device and the hazards associated with utilization of them is one of the critical issues for healthcare organizations across the world [1]. Medical devices are instruments or machines that are used to diagnosis, monitor, treat, or prevent disease or other conditions. Degradation in the performance of critical medical devices and inadequately maintained medical equipment create an unacceptable risk of patient injury. In addition, there are risks of injury to clinical staff from simple, direct hazards, such as accidental contact with electrified parts or from mechanical failures within the device [3], for example defects in ultrasound machines, defective artificial cardiac valves, leakage of insulin pumps [4], and high number of errors in CT scans which leads to patients receiving 10 times the intended dose of radiation in some cases. Thus, the maintenance of medical devices is fundamental and it calls for an effective and efficient framework to prioritize medical devices for maintenance activities based on key criteria and choose the best maintenance policy for each device.
Clinical engineering departments in hospitals have been developing programs such as Medical Equipment Management Program (MEMP) to reduce risks associated to medical devices and to promote the safety of medical devices in support of patient care. Some risk based MEMP methods have been presented for assessment of devices and are currently in use. These models consider risk in terms of maintenance requirements of medical device, function of medical device, and physical harm/risk. However, other important criteria such as the number of patients served, economic loss, mean time to repair (MTTR), and use-related hazards, among others are overlooked. Rice [5] in his paper mentions that, “although these methods do reduce risks, they are not near optimal”. Besides, in most of the proposed models equal risk levels are assigned to similar devices and the operational and environmental conditions and independently of the hospital's mission statement are overlooked. This could lead to misclassifying devices, such as steam sterilizers, as low risk [6].
This paper presents a novel fuzzy multi-criteria decision making (FMCDM) approach to the medical device prioritization problem within a risk-based maintenance (RBM) framework. This comprehensive approach first prioritizes medical devices based on their criticality and then propose a diagram for selecting appropriate maintenance strategy in healthcare organizations. The two objectives of this research are (1) to revisit and reassess the major criteria and sub criteria that can affect medical devices risk scores, and (2) to propose a three steps approach for clinical engineers to prioritize medical devices and select the best maintenance strategy for them. The first step consists in applying FFMEA method to calculate the risk priority index (RPID) for each device. In the proposed FFMEA model, three criteria – severity (S), occurrence (O) and detection (D) – and eight sub-criteria have been considered. In the second step, seven miscellaneous dimensions are applied and total intensity (TI) score is calculated based on weighted sum of seven miscellaneous dimensions in order to take into account other aspects of hazards as well as S, O and D. Finally, in the third step, a maintenance planning diagram is proposed according to the scores produced by the previous steps. The proposed approach is illustrated by an academic example including five medical equipment.
The rest of this paper is organized as follows. Section 2 draws a literature review on the existing approaches to the medical device prioritization problem. Section 3 describes the proposed approach, while Section 4 illustrates its application on an academic numerical example. Conclusions and directions for future research are presented in Section 5.
Section snippets
Literature review
The prioritization of medical devices into risk management programs based on risk scores has become a capital task for healthcare organizations. The medical equipment standards presented by the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) have forced hospitals in US to use their own risk management tools in order to decide which equipment must be involved in the MEMP [2]. In 1989, Fennigkoh and Smith [10] proposed a device classification scheme based on three criteria:
A fuzzy FMEA based approach to the medical device prioritization
In agreement with traditional FMEA and RBM principles, the aim of the proposed approach is to assure high availability for critical medical devices. In brief, this approach is able to prioritize medical devices based on their criticality, taking into account the different criteria and dimensions. In addition, the proposed model is able to choose the best maintenance strategy for each medical device. The proposed approach is comprised of the three following steps.
Numerical example and discussion
In order to illustrate the proposed framework, this section presents an academic numerical example. We extracted multiple failure modes of five medical devices from the literature and assessed them, following the three proposed steps. The judgment of three different experts was considered.
Step 1. Construct RPI assessment table for each device: Table 13, Table 14, Table 15, Table 16, Table 17 illustrate the first step of the framework. Table 11 presents the five medical devices, their considered
Conclusion and future work
The two main contributions of this study are: (i) development of a comprehensive framework for prioritization of critical medical devices, and (ii) proposing a method to select the best maintenance strategy for each device. The risk based prioritization of medical devices is valuable to healthcare organizations in prioritizing maintenance activities and in budget allocation to maintenance works. In addition, the findings of this research are very beneficial both academically and to other
Acknowledgements
This research was partially financed by grants [OPG 0293307 and OPG 0118062] from the Canadian Natural Sciences and Engineering Research Council (NSERC). This support is gratefully acknowledged.
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