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Decision Support Procedure for Medical Equipment Maintenance Management
Abstract
Hospitals outsource several activities of the service support in order to focus on the core healthcare production as maintenance service. Recently, faced to the sophistication and the costs of medical equipment that continue to escalate, governments have implemented new reforms to control costs and improve the efficiency and the quality. Hospitals become interested in minimizing the total operational cost, by optimizing healthcare production planning and their support activities. Reorganizing the medical equipment maintenance service becomes a priority for the hospital managers to reduce the cost and the dependency on external parties while ensuring that the medical devices are safe, accurate, and operating at the required level of performance. In this article, we propose an efficient procedure to take the appropriate decisions for medical equipment maintenance such as the selection of maintenance strategy, the insourcing/outsourcing, and the selection of contracts_ type and content. A practical application of this procedure in the Tunisian context is considered. Nevertheless, our procedure is general and can be tailored to hospitals in both developed and developing countries.
... Corrective maintenance is performed once a failure is discovered to return devices to their working state. In contrast, preventative maintenance is performed in a predefined interval as a preventive action before failure, lowering the risk of medical device breakdown or deterioration [1]. In order to minimize future failure, predictive maintenance is performed and is a proactive task for future failure predictions. ...
... Thus, the LDA fusion and a perplexity modelling is generated, where 1 is the pool of D documents which is known as a topic mixture, and over K, number of topics. It is depicted by the vectors of word probabilities 1 to . ...
Medical device failure and maintenance records are essential information, as some nations lack dedicated systems for capturing this valuable data. In addition to making healthcare more intelligent and individualized, machine learning has the potential to transform the conventional healthcare system. Optimizing AI models in decision-making could mitigate the effects of three research issues: malfunctioning medical devices, high maintenance costs, and the lack of a strategic maintenance framework. This study proposes a data-driven machine-learning model for predicting medical device failure. The proposed predictive model is developed using multimodal data of structured maintenance and unstructured text narrative of maintenance reports to predict the failure of 8,294 critical medical devices. In developing the model, 44 varieties of essential medical devices from 15 healthcare institutions in Malaysia are utilized. A classification problem is addressed by classifying failure into three prediction classes: (i) class 1, unlikely to fail within the first three years, (ii) class 2, likely to fail within three years; and (iii) class 3, likely to fail after three years from the date of commissioning. The topic modelling and synthesis strategy: Latent Dirichlet Allocation is applied to unstructured data in order to uncover concealed patterns in maintenance notes captured during failures. In addition, sensitivity analysis is performed to select only the most significant parameters affecting the failure performance of the medical device. Then, four machine learning algorithms and three deep learning networks are evaluated to determine the best predictive model. Based on the performance evaluation, the Ensemble Classifier is further optimized and demonstrates improved accuracy of 88.80%, specificity of 94.41%, recall of 88.82%, precision of 88.46%, and F1 Score of 88.84%. The study proves a reduction in intervention from 18 to 8 features and a reduction in training time from 1660.5 to 901.66 seconds for comprehensive model development.
... Of the 29 studies were identified, (n=7, 24.13% were case study, (n=14, 48.27%) cross sectional, (n=2, 6.89%) literature review and 20.68% of them not reported. All included studies were conducted in a hospital setting.The majority of factors mentioned in three studies (Cohen T CN and Cram NBC (2001), Al-Bashir A et al., (2012) and Masmoudi M et al., (2016) The three studies including Cohen and Cram (2001), Al-Bashir A et al., (2012) and Masmoudi M et al., (2016) were reported the highest number of factors [13][14][15]. In total, 12 studies cited to user training, seventeen to PM, nine to record or documentation and six to CMMS. ...
... Of the 29 studies were identified, (n=7, 24.13% were case study, (n=14, 48.27%) cross sectional, (n=2, 6.89%) literature review and 20.68% of them not reported. All included studies were conducted in a hospital setting.The majority of factors mentioned in three studies (Cohen T CN and Cram NBC (2001), Al-Bashir A et al., (2012) and Masmoudi M et al., (2016) The three studies including Cohen and Cram (2001), Al-Bashir A et al., (2012) and Masmoudi M et al., (2016) were reported the highest number of factors [13][14][15]. In total, 12 studies cited to user training, seventeen to PM, nine to record or documentation and six to CMMS. ...
Purpose
Effective maintenance management of medical equipment is one of the major issues for quality of care and cost-effectiveness especially in modern hospitals. An effective medical equipment maintenance management (MEMM) consists of adequate planning, management and implementation. This is essential for providing good health services and saving scarce resources. Considering the importance of the subject, the purpose of this paper is to extract the influential factors on MEMM using a qualitative approach.
Design/methodology/approach
Documents review and interviews were main methods for data collection. Semi structured interviews were conducted with a purposive sample of 14 clinical engineers with different degree of education and job levels. Interviews were voice recorded and transcribed verbatim. Qualitative data were analyzed using a content analysis approach (inductive and deductive) to identify the underlying themes and sub-themes.
Findings
Factors influencing an effective and efficient MEMM system categorized in seven themes and 19 sub-themes emerged. The themes included: “resources,” “quality control,” “information bank,” “education,” “service,” “inspection and preventive maintenance” and “design and implementation.”
Originality/value
The proposed framework provides a basis for a comprehensive and accurate assessment of medical equipment maintenance. The findings of this study could be used to improve the profitability of healthcare facilities and the reliability of medical equipment.
... Of the 29 studies were identified, (n=7, 24.13% were case study, (n=14, 48.27%) cross sectional, (n=2, 6.89%) literature review and 20.68% of them not reported. All included studies were conducted in a hospital setting.The majority of factors mentioned in three studies (Cohen T CN and Cram NBC (2001), Al-Bashir A et al., (2012) and Masmoudi M et al., (2016) The three studies including Cohen and Cram (2001), Al-Bashir A et al., (2012) and Masmoudi M et al., (2016) were reported the highest number of factors [13][14][15]. In total, 12 studies cited to user training, seventeen to PM, nine to record or documentation and six to CMMS. ...
... Of the 29 studies were identified, (n=7, 24.13% were case study, (n=14, 48.27%) cross sectional, (n=2, 6.89%) literature review and 20.68% of them not reported. All included studies were conducted in a hospital setting.The majority of factors mentioned in three studies (Cohen T CN and Cram NBC (2001), Al-Bashir A et al., (2012) and Masmoudi M et al., (2016) The three studies including Cohen and Cram (2001), Al-Bashir A et al., (2012) and Masmoudi M et al., (2016) were reported the highest number of factors [13][14][15]. In total, 12 studies cited to user training, seventeen to PM, nine to record or documentation and six to CMMS. ...
Introduction: The medical equipment maintenance management is a pivotal issue for the safety and cost of medical devices in order to improve medical devices system targets. We need a comprehensive assessment tool that covers all aspects of medical equipment maintenance management in hospitals. In this regard, identification of influential factors is essential. Aim: The aim of the present systematic review was to extract the factors affecting the medical equipment maintenance management. Materials and Methods: We conducted a comprehensive search in databases including OVID, PubMed, ProQuest, Scopus, Embase, Science Direct and web of science without any time limitation until October 2015. The result was updated in June 2017. Inclusion criteria were all studies related to medical equipment maintenance management and mentioning at least one factor that affects this process. Two independent reviewers checked the research process, screening of articles and quality assessment. Quality of the studies was assessed by QASP and STROBE checklist. Results: A total of 29 articles were included in this study. All the included articles were in English language. Finally, 89 factors were identified that affect the medical equipment maintenance management. Five of the factors were found related to resources item, 12 factors related to service, four factors related to education, 15 of these factors regarding to quality control, 19 factors related to inspection, 12 factors related to information bank and 22 factors was dedicated to management. Conclusion: Influential factors (management, resources, information bank, inspection, quality control, education and service) are implicated in decision-making in support of selection, purchase, repair and maintenance of medical equipment, especially for capital equipment managers and medical engineers in hospitals and also for assessment of this process. Identification and classification of influential factors can be of help for raising critical alerts about equipments more prone to maintenance problems. © 2018, Journal of Clinical and Diagnostic Research. All rights reserved.
... As a result, this situation has increased maintenance costs and affected the quality of services provided. The global medical devices outsourcing market reached over USD 116.7 billion in 2022 [4], and it is projected to grow to USD 352.3 billion in 2023. ...
Medical devices are essential in healthcare, and their availability and reliability are critical for quality service. In most Saudi hospitals, maintenance schedules for these devices follow manufacturer recommendations, which often do not account for Saudi Arabia's unique climate and lifestyle. This research introduces a mathematical model to optimize maintenance schedules tailored to Saudi conditions. Three governmental hospitals in Riyadh, Jeddah, and Madinah were selected for a case study. The research developed the Medical Equipment Maintenance Prioritization Factor (MEMPF) model to enhance maintenance schedules. This model uses ten parameters: device function, failure consequence risk, maintenance complexity, device age, utilization rate, failure frequency, maintenance/repair cost, causes of downtime, backup availability, and downtime duration. These parameters were weighted based on their importance. The model was tested on a dataset of 3,640 medical devices from 54 healthcare sections. The outcomes defined maintenance priorities for each device based on the MEMPF value, categorizing them into high, moderate, low, and very low priority. Implementing this model in the case study hospitals could reduce maintenance costs by 36% over ten years.
... The third and last module deals with the maintenance externalization decision, including the selection of an appropriate contracting policy for each externally maintained device. In developing countries, including the country in which the current study is conducted, there are generally four types of contracts to choose from (Masmoudi et al., 2016): ...
Purpose:
This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.
Design/methodology/approach:
The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.
Findings:
Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.
Originality/value:
An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.
... Nevertheless, discrete sensor and internet covering of every location resulted in substantial facility management expenses. Moreover, adherence, safety, and trustworthiness standards are becoming more stringent and subject to rapid change (Masmoudi et al., 2016). ...
Background
Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas.
Methodology
Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles.
Results
The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies’ flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring.
Conclusions
Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.
... We generate the related parameters of each equipment as in Table 2 based on the method presented by [42], including the shape parameter (β k ) and scale parameter (θ k ) of Weibull distribution, the cost of PM (C pm (k)) and CM (C cm (k)), unit downtime penalty cost (c k ), unit traveling cost (c t v ), maintenance time (T pm (k), T cm (k), T rm (k)), age decline factor (α k ), failure rate increasing factor (ε k ), and the maximum number of teams (V ). ...
... Thus, it reduces the need to purchase equipment or do repairs. In addition, as the efficiency of the equipment increases, the efficiency of the diagnostic, treatment, and rehabilitation process increases as well, and higher quality services are provided (2,3). ...
Background: Medical equipment maintenance plays an important role in improving the equipment's function, increasing its effectiveness and efficiency, providing continuous health services, and improving the quality of services. This study is conducted to investigate the factors affecting medical equipment maintenance management.
Methods: The present study was a qualitative investigation based on content analysis that was conducted in 2021. Data collection method was an open ended interview. The interviewees included all medical equipment operators and technicians of educational and medical centers affiliated with Qazvin University of Medical Sciences. The semi-structured interview guide was used as a data collection tool. Qualitative data obtained from interviews were analyzed by the content analysis method. The obtained coded were classified into dimensions and components using MAXQDA Software12 .
Results: According to the findings, categories of time constraints, access to information, instructions and programs, type and number of equipment, financial constraints, user, patients, training programs, and equipment repair agencies were the most important factors affecting medical equipment maintenance management.
Conclusion: Observing purchasing standards and guaranteed equipment, training all the staff involved, developing and explaining instructions and programs are essential for optimal equipment maintenance. These measurements will reduce the cost of repairing.
g medical equipment and the need to purchase equipment; improve equipment, service quality performance, patient and user safety; and increase the device life.
... From the environmental perspective not focused on healthcare equipment, Sloan [57] considered the following aspects to be relevant: environmental costs and compliance mechanisms (which provide incentives or penalties according to whether or not given levels of environmental performance are achieved); additionally, they though that the scope of the decision should also be considered beyond the initial period. Other contributions to the choice of maintenance policies for medical equipment can also be used to choose the criteria relevant to renewal of medical equipment; in this case, the intention was generally to associate a criticality index to each device: for example, Fennigkoh and Smith [58] considered the task performed by the equipment, the physical risks associated with the clinical application, and the need for maintenance, while Masmoudi et al. [59] included the criteria degree of complexity of the maintenance, function, risk, degree of the mission importance, and age. Taghipour et al. [60] considered that function, mission criticality, age, risk, recalls, hazard alerts, and maintenance requirements are the appropriate criteria to be included in a model built with the AHP for the prioritization of criticality of medical devices; the obtained normalized scores can be used together with other pre-set thresholds to establish the most suitable maintenance policy, e.g., for devices in the high criticality class (and therefore with scores between 40% and 100% of the total score), proactive, predictive, or time-based maintenance is recommended. ...
In the current literature, there are a clear lack of systems to assist in making decisions about the renewal of technology for healthcare equipment, which means that the limited capacity to invest in new equipment cannot usually be appropriately applied as determined by the care requirements of a community. This may have important repercussions for patients, such as the inability to offer treatment or diagnosis, having to delay treatment or diagnosis, increase the risk of patients and care staff of using obsolete equipment, and preventing early, accurate, and reliable diagnosis, all of which have effects on the quality of care to a community. This study therefore describes the first multicriteria model in a fuzzy environment to assist in decision making related to the renewal of healthcare equipment. The fuzzy analytic hierarchy process (FAHP), which allows for ambiguities, uncertainties, and doubts inherent in real-world decision processes to be taken into account, was used to do this. The model produces a plan with actions to be taken depending on the obtained results. The model includes a novel methodology that consists of modifying the top–down technique to allow for the levels of priority for renewing healthcare equipment to be determined from judgements given by three experts. The model was validated by applying it to a set of medical devices, and we show the results for a surgical C-arm, an X-ray CT room, a neonatal ventilator, a defibrillator, and a video-colonoscope. A program was also created using the NI Labview software to process the model so that it could be applied with a user interface that acts quickly, simply, and intuitively.
... According to Masmoudi et al. (2014Masmoudi et al. ( , 2016, there are three major service and support possibilities for maintaining the medical equipment: in-house biomedical maintenance service, Original Equipment Manufacturers (OEM), and independent third-party service provider (with or without contracts). Based on the equipment criticality and the available budget of the maintenance department, the decision makers determine the sourcing decisions of maintenance activities: in-house or outsourced with or without a service contract. ...
The main target of the maintenance department in the hospital is to guarantee the patient safety by properly keeping up the medical devices. Any potential hazard due to the bad performance of the devices can have severe consequences on the patient life. In this paper, we propose a Mixed Integer Linear Programming (MILP) model to: 1) select the best maintenance strategy, e.g., run-to-failure, time-based, and conditional based, for each equipment in the hospital, 2) decide on the best option for insourcing or outsourcing maintenance activities per equipment, 3) optimize the tactical maintenance decisions per equipment. Maintenance service in the hospital has limited resources to maintain the medical devices. Therefore, by selecting which equipment to be maintained in-house or to be outsourced and the contract to be used for outsourcing are considered as tactical decisions. The objective is to minimize the total annual maintenance costs without affecting the availability of critical devices.
... Equipment maintenance and repair is the most critical function which supports proper delivery of healthcare services. The WHO [11] observed that the biomedical engineers ought to optimize the best maintenance strategy to reduce the failure rate of medical equipment to improve the medical equipment reliability. The biomedical engineering sector has to ensure the safety of the equipment and the reduction of maintenance cost is paramount. ...
... They used analytical hierarchy process (AHP) method consisting of six criteria (risk, equipment function, age, utilization, redundancy, and mission criticality, recalls, and maintenance requirements) and then suggested heuristic guidelines to select the appropriate maintenance strategy. The same heuristic reasoning was recently used by (Masmoudi et al. 2016) to decide the type of maintenance policy (outsourcing/in-house) and select the suitable service contract. They developed a criticality based model with consideration of maintenance resources performance, risk and cost criteria. ...
Purpose – The aim of this paper is to review maintenance strategies within healthcare domain and to discuss practical needs as gaps between research and practice.
Design/methodology/approach – The paper systematically categorizes the published literature on clinical maintenance optimization and then synthesizes it methodically.
Findings – This study highlights the significant issues relevant to the application of dependability analysis in healthcare maintenance, including the quantitative and qualitative criteria taken into account, data collection techniques, and applied approaches to find the solution. Within each category, the gaps and further research needs have been discussed with respect to both an academic and industrial perspective.
Practical implications – It is worth mentioning that medical devices are becoming more and more numerous, various and complex. Although, they are often affected by environmental disturbances, sharp technological development, stochastic and uncertain nature of operations and degradation and the integrity and interoperability of supportability system, the associated practices related to asset management and maintenance in healthcare are still lacking. Therefore, the literature review of applied based research on maintenance subject is necessary to reveal the holistic issues and interrelationships of what has been published as categorized specific topics.
Originality/value – The paper presents a comprehensive review that will be useful to understand the maintenance problem and solution space within the healthcare context.
Keywords Healthcare, Medical device, Maintenance decision making, Dependability, Maintenance
optimization model
Slide-Stainer is of great significance for the digitalization and intellectualization of pathological staining. In view of the problems of inaccurate layout and demand docking, the Slide-Stainer layout optimization problem is defined based on common staining methods in pathology, aiming at obtaining a scientific and reasonable Slide-Stainer layout, making the whole staining process more efficient and smoother. Also, the result can provide guidance for future specification design of Slide-Stainer. First of all, this paper describes the Slide-Stainer layout optimization problem, and defines the required variables and basic constants of Slide-Stainer. Secondly, based on the definition of the problem, a mathematical model of Slide-Stainer layout optimization problem was established, and an optimization function including binocular criteria was established. Finally, the optimal layout design result under the condition was obtained by practical working simulation using the common pap staining method, the rationality of the layout was verified by the Y algorithm. The results showed that the staining operation with this layout had high efficiency.KeywordsSlide-StainerPathological slidesMathematical modelComputational intelligenceLayout optimization
La planification de la maintenance des équipements médicaux vise à gérer au mieux l'activité du département de maintenance de l'hôpital afin de répondre aux attentes des patients et améliorer la qualité des services de production des soins. Tout risque potentiel en raison d'une mauvaise performance des équipements peut avoir des conséquences graves sur la vie des patients.
Đề tài nghiên cứu nhằm xác định các yếu tố ảnh hưởng đến công tác quản lý trang thiết bị y tế tại hệ thống các Bệnh viện công lập: trường hợp nghiên cứu Bệnh viện Đa khoa Cà Mau. Kết quả nghiên cứu của tác giả cho thấy có 5 nhân tố tác động đến công tác quản lý trang thiết bị y tế tại Bệnh viện Đa khoa Cà Mau, các nhân tố này bao gồm: Công tác quản lý; (2) Nhân lực; (3) Thông tin; (4) Kiểm tra, bảo dưỡng; (5) Kiểm soát chất lượng. Từ đó tác giả đề ra một số hàm ý quản trị phù hợp nhằm nâng cao công tác quản lý trang thiết bị y tế tại Bệnh viện Đa khoa Cà Mau nói riêng cũng như hệ thống các Bệnh viện công lập nói chung
The objective of this thesis is to provide tools to help the biomedical maintenance service of the hospital to make decisions that allow a better control of costs, while ensuring patient and user safety and maintaining optimal performance of medical equipment.
Portable suction machines are widely implemented in both clinical and home care settings. Two identical new machines were tested at 3 different levels of pressure settings operating at intermittent intervals. The study uncovered that noncalibrated portable suction triggers an additional 17% higher suction pressure than the actual most efficient and desired pressure setting. There are statistically significant differences between the calibrated suction machine and noncalibrated suction machine at 3 levels of settings: 80, 100, and 120 mm Hg. This emphasizes the need for fundamental safety redesign of clinical calibration measures for clinical engineering.
This study investigates the relationship between the reliability of critical medical equipment (CME) and the effectiveness of CME maintenance management strategies in relation to patient outcomes in 84 public hospitals of a top 20 OECD country. The work has examined the effectiveness of CME maintenance management strategies used by the public hospital system of a large state run health organization. The conceptual framework was designed to examine the significance of the relationship between six variables:
(1) types of maintenance management strategies, (2) maintenance services, (3) maintenance practice, (4) medical equipment reliability, (5) maintenance costs and (6) patient outcomes. The results provide
interesting insights into the effectiveness of the maintenance strategies used. For example, there appears to be about a 1 in 10 000 probability of failure of anesthesia equipment, but these seem to be confined to specific maintenance situations. There are also some findings in relation to outsourcing of maintenance. For each of the
variables listed, results are reported in relation to the various types of maintenance strategies and services. Decision-makers may use these results to evaluate more effective maintenance strategies for their CME and generate more effective patient outcomes.
Modern medical devices and equipment have become very complex and sophisticated and are expected to operate under stringent environments. Hospitals must ensure that their critical medical devices are safe, accurate, reliable and operating at the required level of performance. Even though the importance, the application of all inspection, maintenance and optimization models to medical devices is fairly new. In Canada, most, if not all healthcare organizations include all their medical equipment in their maintenance program and just follow manufacturers' recommendations for preventative maintenance. Then, current maintenance strategies employed in hospitals and healthcare organizations have difficulty in identifying specific risks and applying optimal risk reduction activities. This paper addresses these gaps found in literature for medical equipment inspection and maintenance and reviews various important aspects including current policies applied in hospitals. Finally we suggest future research which will be the starting point to develop tools and policies for better medical devices management in the future.
Hospitals outsource several activities of support in order to focus on healthcare production. Maintenance is one of these support activities. Recently, faced with rising healthcare costs, governments have implemented new reforms to control costs and improve efficiency and quality. Hospitals became interested in minimizing the total cost of the activity, by minimizing both healthcare production activities and support activities. In developing countries, medical equipment maintenance is costly and partially mastered most of the time because it is usually managed by external service contracts [1]. Reorganizing medical equipment maintenance service became a priority for hospital managers to reduce the cost and dependency while raising quality and reliability. In this paper, we propose an efficient procedure to take the appropriate decisions for medical equipment maintenance such as the strategy, to insource or outsource and the type of contract in case of outsourcing and its content.
: To be accredited, every healthcare organization is required by JCAHO to determine which equipment should be included in its medical equipment management plan. Traditionally, most organizations have adopted the equipment inclusion criteria proposed by Fennigkoh and Smith (1989). This classic interpretation of JCAHO standard uses three criteria (equipment function, physical risks, and maintenance requirements) to establish a numerical value, called the equipment management (EM) number. Only equipment with an EM value higher than a predetermined threshold is included in the management plan. A fourth criterion (incident history) can be used to modify the EM number if data are available. If followed literally, this interpretation can lead to confusion and, occasionally, unreasonable conclusions that might result in inefficiencies and potentially unsafe conditions. A new interpretation of the inclusion criteria is proposed here. The major difference is in reinterpreting the equipment-function criterion as the equipment's importance within the organization's global mission. This helps to balance risk to a single patient with the organization's commitment to its entire community. An additional factor that should be considered is the equipment utilization rate; heavily used items require more frequent inspection and higher priority for repairs. Together, these two elements form an interpretation that is more attuned to JCAHO's new directive (and general business management principles) and encourages greater concurrence with the organization's mission and vision. (C)2000Aspen Publishers, Inc.
Today, medical equipment maintenance suffers from the same ailments that traditional medicine was suffering from before. Rapid advance of medical technologies has proven that traditional maintenance is no longer enough to ensure that equipment is getting the best possible maintenance. Medical equipment industry has been following empirical approaches and very little was done on mathematical modelling. Preliminary data collected from some hospitals in USA and analysed show that current maintenance strategies might be effective but there is no clear evidence whether they are efficient. However, incorporating mission-criticality concept with patient risk might produce much higher impact on reduction of risk. Refocusing resources from scheduled maintenance to higher impact tasks, e.g., use error tracking, self-identified failures and repairs, user training and working with facilities and purchasing should lead to a balanced mix between needs and resources. A mathematical model is developed using a mixed integer based approach for maintenance operations schedules for medical equipment. Field data are used to get the parameters of the model by nonlinear least square regression. A greedy algorithm is proposed to give an initial solution for the model.
Clinical engineering departments in hospitals are responsible for establishing and regulating a Medical Equipment Management Program to ensure that medical devices are safe and reliable. In order to mitigate functional failures, significant and critical devices should be identified and prioritized. In this paper, we present a multi-criteria decision-making model to prioritize medical devices according to their criticality. Devices with lower criticality scores can be assigned a lower priority in a maintenance management program. However, those with higher scores should be investigated in detail to find the reasons for their higher criticality, and appropriate actions, such as ‘preventive maintenance’, ‘user training’, ‘redesigning the device’, etc, should be taken. In this paper,we also describe how individual score values obtained for each criterion can be used to establish guidelines for appropriate maintenance strategies for different classes of devices. The information of 26 different medical devices is extracted from a hospital's maintenance management system to illustrate an application of the proposed model.
Nowadays, plant maintenance has gained significant recognition as a
very important process, which can be transformed to a potential profit generator
for the corporation. The development of a suitable maintenance concept
enables the decision of specific maintenance strategies based on the existing
situational factors that affect the organisation. A clear maintenance concept
permits the design of the maintenance system that will be responsible for
efficient and effective plant maintenance. This paper proposes a maintenance
system design framework and presents a successful implementation of the
suggested design framework in a Greek manufacturing company.
Mechatronics is now firmly established across the world. The last decade has seen an explosion in activity both in terms of academic work and industrial development. This paper details recent contributions to the filed of mechatronics by the Loughborough University Mechatronics Research Centre in the UK. The focus of our work has been system design, embedded multi-technology sensing, multi-layered decision making, optimised actuation solutions and of course, systems integration. The range and focus of this work is almost exclusively driven by industrial need. Four examples of mechatronic research projects are presented showing industrial need, technology mix and integrated solutions. The paper concludes with a view of the future for mechatronics identifying likely trends and developments.
In this paper, we examine the large body of existing research on outsourcing, and assess the status of research on outsourcing the maintenance of medical devices. Because so little research in this area currently exists, the study was broadened to include other fields that outsource maintenance services, and considers possible applications to the field of medical device maintenance. In all, this paper examines 55 articles spanning various dimensions, including: mathematical models, empirical studies, and conceptual papers. We conclude that research into the outsourcing of medical device maintenance services in hospitals is still in its infancy stages, and that further progress in this field would benefit from additional empirical study grounded in management theory.
The selection of contracts is a very important stage for the process of outsourcing maintenance in the current trend towards
reducing costs and increasing competitiveness by focussing on core competences. The prominence of this theme can be seen in
many studies carried out on outsourcing repair and maintenance contracts, most of which deal with qualitative aspects. However,
quantitative approaches, such as multi-criteria decision aid, play an important role in helping decision makers (DMs) to deal
with multiple and conflicting criteria and uncertainties in the selection process for outsourcing contracts. In this context,
several decision models have been developed using different multi-criteria methods. This paper presents a multi-criteria methodology
to support the selection of repair contracts in a context where information is imprecise, when DMs are not able to assign
precise values to importance parameters of the criteria used for contract selection. Utility functions are integrated with
the variable interdependent parameters method to evaluate alternatives through an additive value function regarding mean time
to repair, contract cost, the geographical spread of the candidate's (bidder's) service network, the candidate's reputation
and the compatibility of company cultures. To illustrate the use of the model, a numerical application is presented.
It has been claimed that the financial health of a hospital can achieve a significant contribution from increasing the productivity of its clinical engineering department. However, the overemphasis on cost-effectiveness could be harmful if the risk that might arise from using medical equipment is not properly handled. The first objective of this research is to study clinical engineering in Palestine and evaluate the degree of compliance with international guidelines for medical instrumentation maintenance programs. Descriptive questionnaires and structured interviews were used to evaluate the constructs and variables. The result of evaluation showed a low degree of compliance with both Canadian Medical and Biological Engineering Society and the Association for the Advancement of Medical Instrumentation guidelines. The second objective is to suggest a maintenance model for minimizing the risk and optimizing the cost-effectiveness of medical equipment. The elements of both risk management and cost-effectiveness were evaluated together with the role of medical equipment suppliers. The results showed a poor overall performance and lack of effective procedures regarding risk and costs of maintenance programs. Therefore, the proposed model has been revised to suit clinical engineering departments in Palestinian hospitals.
This paper investigates the use of clustering technique to characterize the providers of maintenance services in a health-care institution according to their performance. A characterization of the inventory of equipment from seven pilot areas was carried out first (including 264 medical devices). The characterization study concluded that the inventory on a whole is old [exploitation time (ET)/useful life (UL) average is 0.78] and has high maintenance service costs relative to the original cost of acquisition (service cost /acquisition cost average 8.61%). A monitoring of the performance of maintenance service providers was then conducted. The variables monitored were response time (RT), service time (ST), availability, and turnaround time (TAT). Finally, the study grouped maintenance service providers into clusters according to performance. The study grouped maintenance service providers into the following clusters. Cluster 0: Identified with the best performance, the lowest values of TAT, RT, and ST, with an average TAT value of 1.46 days; Clusters 1 and 2: Identified with the poorest performance, highest values of TAT, RT, and ST, and an average TAT value of 9.79 days; and Cluster 3: Identified by medium-quality performance, intermediate values of TAT, RT, and ST, and an average TAT value of 2.56 days.
There is a growing trend for asset intensive industries to outsource maintenance services of their complex assets since outsourcing through service contract reduces upfront investments in infrastructure, expertise and specialised maintenance facilities. Estimation of costs for such contracts is complex and it is important to the user and the service providers for economic variability. The service provider's profit is influenced by many factors such as the terms of the contract, reliability of asset, and the servicing strategies, costs of resources needed to carryout maintenance. There is a need to develop mathematical models for understanding future costs to build it into the contract price. Three policies for service contracts are proposed in this paper considering the concepts of outsourcing maintenance service of assets to the service providers. Conceptual models are developed for estimating servicing costs of outsourcing through service contracts by considering time dependent failure mode. Yes Yes
Managing medical equipments is a formidable task that has to be pursued maximizing the benefits within a highly regulated and cost-constrained environment. Clinical engineers are uniquely equipped to determine which policies are the most efficacious and cost effective for a health care institution to ensure that medical devices meet appropriate standards of safety, quality and performance. Part of this support is a strategy for preventive and corrective maintenance. This paper describes an alternative scheme of OEM (Original Equipment Manufacturer) service contract for medical equipment that combines manufacturers' technical support and in-house maintenance. An efficient and efficacious organization can reduce the high cost of medical equipment maintenance while raising reliability and quality. Methodology and results are discussed.
The maintenance concept of a technical system is the set of rules prescribing what maintenance is required and how demand for it is activated. The requirements to be met by a maintenance concept are specified. A framework for the designing of maintenance concepts is presented.
Guide pratique, Maintenance des dispositifs médicaux, obligations et recommandations. Direction régionale des affaires sanitaires et sociales de Midi-Pyrénées, Conception-réalisation: Drass midi-Pyrénéesmars
- B Walraeve
- P Vigneau
Walraeve B, Vigneau P. Guide pratique, Maintenance des dispositifs médicaux,
obligations et recommandations. Direction régionale des affaires sanitaires et
sociales de Midi-Pyrénées, Conception-réalisation: Drass midi-Pyrénéesmars. Toulouse, France; 2005.
Clustering techniques: measuring the performance of contract service providers
- A M Cruz
- S P Perilla
- Pabó N Nn
Cruz AM, Perilla SP, Pabó n NN. Clustering techniques: measuring the
performance of contract service providers. IEEE Eng Med Biol Mag.
2010;29(2):119-126.
Guidelines for the Establishing and Administering Medical Instrumentation Maintenance Programs. USA
Association for the Advancement of Medical Instrumentation (AAMI,
1985). Guidelines for the Establishing and Administering Medical
Instrumentation Maintenance Programs. USA. http://www.aami.org/.
Accessed November 27, 2015.
A risk-based maintenance strategy using Fuzzy HFMEA for prioritization of critical medical equipment. Presented at the 1st Annual World Conference of the Society for Industrial and Systems Engineering
- A Jamshidi
- R S Abbasgholizadeh
- D Ait-Kadi
- A Ruiz
Jamshidi A, Abbasgholizadeh RS, Ait-kadi D, Ruiz A. A risk-based maintenance strategy using Fuzzy HFMEA for prioritization of critical medical
equipment. Presented at the 1st Annual World Conference of the Society
for Industrial and Systems Engineering;
Mathematical Maintenance Model for Medical Equipment, University of Versailles Saint-Quentin-en-Yvelines
- Ab Khalaf
- Y Hamam
- K Djouani
Khalaf AB, Hamam Y, Djouani K. Mathematical Maintenance Model for
Medical Equipment, University of Versailles Saint-Quentin-en-Yvelines.
Versailles, France; Scholar's Press; 2012.
Manuel des Procédures de Gestion de la Maintenance Biomédicale et Hospitalière
- F Letaief
- A Kammoun
- S Khayat
Letaief F, Kammoun A, Khayat S, et al. Manuel des Procédures de Gestion
de la Maintenance Biomédicale et Hospitalière, Ministre de la santè publique
de Tunisie, Vol. III. 2007.
Multi-criteria decision making for Medical equipment maintenance: insourcing, outsourcing and service contract. Presented at the IEEE
- M Masmoudi
- Ben Houria
- Z Masmoudi
Masmoudi M, Ben Houria Z, Masmoudi F. Multi-criteria decision making
for Medical equipment maintenance: insourcing, outsourcing and service
contract. Presented at the IEEE, 2nd International Conference on Control,
Decision and Information Technologies, CODIT'2014; Metz, France;
November 3-5, 2014.