Medical Equipment Maintenance: Management and Oversight
Abstract
Medical equipment is not only essential for safe and effective patient care but also has significant impact on the income of healthcare organizations. For this reason, its maintenance and management requires careful supervision by healthcare administrators who may not have the technical background to understand all the relevant factors. This lecture presents the basic elements of medical equipment maintenance and management for healthcare leaders with the responsibility of managing or overseeing this function so they know what is expected of them, what they should expect from their supervised staff, and how to measure and benchmark the performance of the staff against similar organizations.
First, the laws, regulations, codes and standards that are applicable to the maintenance and management of medical equipment in healthcare organizations are summarized to provide a sound foundation. Next, the core functions of the team responsible for maintenance and management are described in sufficient detail for managers and overseers. Then the methods and measures for determining the effectiveness and efficiency of the maintenance and management are presented to allow performance management and benchmarking comparisons. The challenges and opportunities of managing healthcare organizations of different sizes, acuity levels, and geographical locations are discussed. Extensive bibliography and information sources are provided to assist healthcare leaders interested in acquiring more detailed knowledge.
... Therefore, taking a cost effective maintenance policy is required to achieve higher availability and safety and then lower operational costs [2]. This goal seems rather similar to that of healthcare or medicine, particularly, evidence-based medicine (EBM), where professionals supplement the scientific community with the results of the most recent comparative effectiveness studies, such as those conducted using randomized clinical trials on drugs, devices, and procedures [3]. Likewise, instead of merely following laws, regulations, codes, standards, industry practices, and manufacturers' recommendations, clinical maintenance managers need to learn from their colleagues and extend their engineering education with up-to-date results of the maintenance effectiveness studies, which evaluate critically different maintenance strategies, procedures, and frequencies [3], [4]. ...
... This goal seems rather similar to that of healthcare or medicine, particularly, evidence-based medicine (EBM), where professionals supplement the scientific community with the results of the most recent comparative effectiveness studies, such as those conducted using randomized clinical trials on drugs, devices, and procedures [3]. Likewise, instead of merely following laws, regulations, codes, standards, industry practices, and manufacturers' recommendations, clinical maintenance managers need to learn from their colleagues and extend their engineering education with up-to-date results of the maintenance effectiveness studies, which evaluate critically different maintenance strategies, procedures, and frequencies [3], [4]. This goal based approach has been termed evidence-based maintenance (EBM), which can be considered as a continual improvement process that scrutinizes maintenance excellence in comparison to outcomes achieved previously or elsewhere and regulates necessary adjustments to maintenance planning and performance [1], [3]. ...
... Likewise, instead of merely following laws, regulations, codes, standards, industry practices, and manufacturers' recommendations, clinical maintenance managers need to learn from their colleagues and extend their engineering education with up-to-date results of the maintenance effectiveness studies, which evaluate critically different maintenance strategies, procedures, and frequencies [3], [4]. This goal based approach has been termed evidence-based maintenance (EBM), which can be considered as a continual improvement process that scrutinizes maintenance excellence in comparison to outcomes achieved previously or elsewhere and regulates necessary adjustments to maintenance planning and performance [1], [3]. In this context, several investigation studies have been presented to highlight the importance of EBM in healthcare domain. ...
The concern for patient safety and regulatory requirements classically based on manufacturers’ recommendations discouraged evidence based maintenance, and thus limited the possibility of maintenance optimization within healthcare organization. Although, the need for preventive maintenance and its appropriate frequency have been debated extensively for decades, very few studies have been conducted to revise healthcare maintenance strategy and improve the effectiveness of clinical engineers work, as well as focus on tasks that could provide the highest return for their limited resources. In this paper, the shift from a compliance-based to a goal-based approach is well established in an improved proportional delay time framework taking into account hidden nature of failures, the influence of the utilization rate and maintenance effectiveness on medical devices degradation. To illustrate the model capabilities, a real case study from the healthcare domain is presented, the model parameters are estimated entirely using the collected maintenance data. Then, the optimal maintenance policy is determined by a multi-objective optimization to achieve the desired cost effectiveness.
... Maintenance excellence can be achieved by making the rational maintenance decision balancing costs and industrial performance [4]. It is worth mentioning that the amount, multiplicity, sophistication, and costs of medical equipment are abruptly rising, which make that their maintenance complexity and costs also escalate sharply in the last few years [5]. In addition to maintenance expenditure, medical devices (MD) are frequently involved in patient incidents (death or injury) [6]. ...
... Likewise, [22] recapped several types of MD maintenance activities consisting of repair, replacement, or inspections. The paper [5] dispelled a misunderstanding related to MD maintenance which is "the more maintenance the better" and introduced the analogous concept "evidence-based maintenance." A high completion rate of scheduled maintenance is not a good indicator of maintenance effectiveness (reliabilityavailability-safety) and efficiency (overall costs), to the extent that evidence-based maintenance would be a continual improvement process that analyzes the effectiveness and efficiency of maintenance policy deployed in comparison to outcomes attained. ...
... In the healthcare domain, optimization models limited scope by considering a single objective (mainly the overall cost) is another limitation perceived in this review [47][48][49], which is often not the case of real industrial environment. The paper [5] confirms that, in addition to reliability, safety, and maintenance efficiency measure, availability is an important indicator of maintenance effectiveness. This statement is argued in [38,50] study. ...
Although medical equipment maintenance has been carefully managed for years, very few in-depth studies have been conducted to evaluate the effectiveness and efficiency of these implemented preventive maintenance strategies, especially after the debate about the credibility of manufacturer’s recommendations has increased in the clinical engineering community. Facing the dilemma of merely following manufactures maintenance manual or establishing an evidence-based maintenance, medical equipment maintenance could have exploited an advanced area in operations research which is maintenance optimization research. In this paper, we review and examine carefully the status of application oriented research on preventive maintenance optimization of medical devices. This study addresses preventive healthcare maintenance with a focus on factors influencing the maintenance decision making. The analysis is structured by defining different aspects necessary to construct a maintenance optimization model. We conclusively propose directions to develop suitable tools for better healthcare maintenance management.
... In addition, medical equipment failures cause a large number of patient deaths every year [3]. The statistics from the Joint Commission show that the "sentinel events" related to medical equipment are usually among the top ten types yearly [4], [5]. Improving the reliability of medical equipment and reducing the harm to patients caused solely by medical equipment anomalies or failures is crucial for healthcare facilities. ...
... OF THE HYPERPARAMETERS OF THE MODEL FOR THE AI4I 2020 PREDICTIVE MAINTENANCE DATA SET Data Preprocessing Time window size of the feature* Time window size of the label Edge-cutting size (The sum of negative instances deleted before and after a group of positive instances) The epsilon of the DBSCAN* Network: SFI module The density threshold of the DBSCAN Kernel size of the causal convolution Kernel number of the causal convolutionSmoothing parameter in LSCL* function represents the hyperparameters for random search5,6,7,8,9,10,11,12,13,14 ...
Anomalies or failures in medical equipment may lead to severe consequences. Data-driven prognostic and health management (PHM) approaches can improve maintenance efficiency and reduce maintenance costs at hospitals while protecting patients’ lives. However, currently, the research and application of PHM in medical equipment is still rather limited. The development of the Internet of Things (IoT) technology provides new opportunities for PHM, which can safely collect, analyze and store real-time equipment data in hospitals. The data-driven models used in PHM predict anomalies or failures. However, current data-driven models’ performance may be limited due to lack of consideration for the interaction of similar features and the importance of different time steps. Hence, this paper proposes a new deep learning network called similar feature interaction with distance self-attention for the PHM of medical equipment. Firstly, a similar feature interaction module which uses clustering algorithms and causal convolution layers is proposed to consider the interaction of similar features. Secondly, a distance self-attention mechanism is proposed to allocate more attention to important time steps. The experiments on millions of CT equipment operating status instants collected by IoT in the hospital and the public dataset show that the proposed model is superior to existing models. The results show that the accuracy, recall, precision and f1-score of the proposed model on the real CT log data achieve 0.865, 0.682, 0.469 and 0.556, respectively. The proposed PHM model can assist the equipment maintenance team of hospitals in decision making under the IoT framework.
... The procedures involved in health services, ranging from diagnosis to treatment, rehabilitation to screening, prevention to monitoring, depend on the efficiency of medical equipment (18). Therefore, the provision of health services is almost impossible without proper maintenance of medical equipment (19). In addition, devices must be monitored to maintain performance in terms of calibration, maintenance, restoration, training, and decommissioning (20). ...
... In addition, the presence of a management dashboard makes it possible to monitor the progress of ordinary maintenance and the integration of this data with the aforementioned KPIs will make it possible to evaluate optimization of the maintenance process (12)(13)(14)(15)17). The maintenance process and program can be improved through the development of models that test changes in the periodicity and in the methodologies of maintenance activities with the final aim of increase the lifecycle length and management of biomedical technologies (19)(20)(21)(22)(23)(24). So, this new methodology will provide some useful information for maintenance and technology replacement phases and KPIs for decision-makers in technical analysis within technology management. ...
Introduction
Digital transformation and technological innovation which have influenced several areas of social and productive life in recent years, are now also a tangible and concrete reality in the vast and strategic sector of public healthcare. The progressive introduction of digital technologies and their widespread diffusion in many segments of the population undoubtedly represent a driving force both for the evolution of care delivery methods and for the introduction of new organizational and management methods within clinical structures.
Methods
The CS Clinical Engineering of the “Spedali Civili Hospital in Brescia” decided to design a path that would lead to the development of a software for the management of biomedical technologies within its competence inside the hospital. The ultimate aim of this path stems from the need of Clinical Engineering Department to have up-to-date, realistic, and systematic control of all biomedical technologies present in the company. “Spedali Civili Hospital in Brescia” is not just one of the most important corporate realities in the city, but it is also the largest hospital in Lombardy and one of the largest in Italy. System development has followed the well-established phases: requirement analysis phase, development phase, release phase and evaluating and updating phase.
Results
Finally, cooperation between the various figures involved in the multidisciplinary working group led to the development of an innovative management software called “SIC Brescia”.
Discussion
The contribution of the present paper is to illustrate the development of a complex implementation model for the digitization of processes, information relating to biomedical technologies and their management throughout the entire life cycle. The purpose of sharing this path is to highlight the methodologies followed for its realization, the results obtained and possible future developments. This may enable other realities in the healthcare context to undertake the same type of pathway inspired by an accomplished model. Furthermore, future implementation and data collection related to the proposed Key Performance Indicators, as well as the consequent development of new operational management models for biomedical technologies and maintenance processes will be possible. In this way, the Clinical Risk Management concept will also be able to evolve into a more controlled, safe, and efficient system for the patient and the user.
... The specialised equipment extensively assists healthcare practitioners during the early phase of symptom detection to curb health deterioration (5). Healthcare services delivery is almost impossible without proper maintenance of medical equipment (6). In addition, the devices need to be monitored for upkeeping performance in calibration, maintenance, restoration, training, and decommissioning, which are typically managed by clinical engineers (7). ...
... The clinical engineers in a healthcare facility are responsible for regulating and introducing an effective management programme for medical equipment reliability and safety (8). High technology innovation has elevated medical equipment complexity and eventually escalated the procurement and maintenance expenditures (6). ...
The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.
... In the last decade of the 19th century, the US spent 50% of its construction budget to repair and maintain built facilities (Shah Ali 2009). Furthermore, maintenance costs rose in hospitals abruptly in the recent decades due to the escalation of complexity and equipment costs (Wang 2012). ...
... Medical devices are important assets in a hospital (Bahreini et al. 2019;Wang 2012). Hospitals need to continuously ensure medical devices' reliability and performance because of their role in diagnosing and treating patients (Jamshidi et al. 2014). ...
The reliable maintenance management system of medical equipment influences the patients’ treatment and the hospitals’ performance. Although building information modeling (BIM) technology has evolved the construction industry, it does not fully comply with the facility management (FM) industry, particularly with repair and maintenance. BIM-based FM provides a structured platform to effectively capture necessary data during the construction stage for effective facility maintenance management (e.g., prioritizing maintenance work orders). Despite the design improvements and preventive maintenance plans, unplanned failures are inevitable and need quick and appropriate reactions. This paper introduces an integrated BIM-based framework for effective facility maintenance management. This framework consists of an integrated maintenance database for medical equipment, a scheduling engine to prioritize and sequence work orders, and a 4D simulation module to visualize the work-order handling process semiautomatically. Case-based reasoning (CBR) is also employed in the simulation engine to capture expert knowledge and facilitate the sequencing process. The proposed framework’s capabilities are demonstrated by applying and validating in a national healthcare facility in Iran.
... Medical equipment maintenance has different types: inspection and preventive maintenance (IPM) and corrective maintenance (WHO, 2011). Effective management of maintenance and repairs (Kinley, 2012) shall be planned and implemented using appropriate maintenance strategies to keep the devices safe and functional according to basic functional specifications (Wang, 2012). In addition to high initial investment, the medical equipment requires continual and costly maintenance during its useful life (Cheng and Dyro, 2004). ...
... The ultimate goal of maintenance is reliability and safety. It should always be safe for both patients and users (Wang, 2012). Maintenance management has had an extraordinary impact on the ability of organizations to achieve their objectives (Duffuaa et al., 2002). ...
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.
... Many medical technologies require the services of clinical engineers and/or biomedical engineering technicians (BMETs) to ensure proper use. 32,[83][84][85] The clinical engineering team is responsible for all aspects of health technology management: (i) checking technical specifications, (ii) recommending specific products for procurement, (iii) configuring the device, (iv) training clinical staff in its use, (v) communicating with the manufacturer, (vi) establishing test and maintenance protocols, (vii) scheduling its use to optimize availability, (viii) maintaining a suitable supply of consumables, (ix) device replacement, and (x) eventual decommissioning. 84,85 The BMET is responsible for performing routine maintenance and repairs, which is often sufficient to keep critical equipment in condition for safe and reliable operation. ...
... 32,[83][84][85] The clinical engineering team is responsible for all aspects of health technology management: (i) checking technical specifications, (ii) recommending specific products for procurement, (iii) configuring the device, (iv) training clinical staff in its use, (v) communicating with the manufacturer, (vi) establishing test and maintenance protocols, (vii) scheduling its use to optimize availability, (viii) maintaining a suitable supply of consumables, (ix) device replacement, and (x) eventual decommissioning. 84,85 The BMET is responsible for performing routine maintenance and repairs, which is often sufficient to keep critical equipment in condition for safe and reliable operation. However, some manufacturers require that maintenance and repairs be performed by a technician employed by the manufacturer or by an independent technician who has specific certification on that device. ...
In the two decades after 1990, the rates of child and maternal mortality dropped by over 40% and 47%, respectively. Despite these improvements, which are in part due to increased access to medical technologies, profound health disparities exist. In 2015, a child born in a developing region is nearly eight times as likely to die before the age of 5 than one born in a developed region and developing regions accounted for nearly 99% of the maternal deaths. Recent developments in nanotechnology, however, have great potential to ameliorate these and other health disparities by providing new cost-effective solutions for diagnosis or treatment of a variety of medical conditions. Affordability is only one of the several challenges that will need to be met to translate new ideas into a medical product that addresses a global health need. This article aims to describe some of the other challenges that will be faced by nanotechnologists who seek to make an impact in low-resource settings across the globe.
... The same study (Jamshidi et al., 2014) indicates that maintenance costs represent nearly 1% of the total hospital budget, so hospitals use up around 8 million US$/year. As well as high maintenance costs, statistics ensued by Joint Commission (TJC) highlight that medical equipment is often engaged in patient incidents that lead to grave injuries and deaths (Wang, 2012). In fact, inadequate maintenance and performance degradation of medical devices create an unacceptable risk level. ...
... Accordingly, clinical engineers have been developing Medical Equipment Management Programs (MEMP) to reduce risks and improve the safety of medical equipment in support of patient care. These programs appeal for an effective and efficient framework to prioritize medical devices for appropriate maintenance decisions based on key criteria (Wang, 2012). The boosted complexity of the organizational context resulted in numerous variables to consider among numerous alternatives. ...
Clinical engineering departments have to establish and continuously regulate a Medical Equipment Management Program (MEMP) to ensure a high reliability and safety of their critical medical devices. Asset criticality assessment is an essential element of reliability centered maintenance and risk-based maintenance, especially when enormous various devices exist and the worst failure consequences are not evident. This paper presents a new risk-based prioritization framework for maintenance decisions. We propose a Multi-Expert Multi-Criteria decision making (MEMC) model to classify medical devices according to their criticality and we describe how obtained scores are used to set up guidelines for appropriate maintenance strategies. © 2016 Hassana Mahfoud, Abdellah El Barkany and Ahmed El Biyaali.
... Nowadays, it is simply impossible to provide healthcare at a high level without healthcare equipment. Unlike other technologies (e.g., drugs, implants, etc.) material and technical equipment requires additional maintenance (scheduled and unscheduled) throughout its lifetime (Wang, 2012). If the quality and progressiveness of the equipment increase, the purchase price and maintenance increase as well. ...
... If the quality and progressiveness of the equipment increase, the purchase price and maintenance increase as well. Studies based on data collected from hundreds of hospitals say that every hospital has on average 15-20 types of healthcare equipment for each occupied bed, which costs about 150 to 320,000 €/bed (Wang, 2012). It is therefore apparent that the purchase and maintenance of equipment is often one of the biggest investment of each healthcare organisation right after the investment in property (e.g., the rent or purchase of premises, land, etc.). ...
The use of healthcare technology, which effectively assists in diagnosing diseases, significantly increases comfort and satisfaction of the patients. Prices increase with rising quality and offered possibilities. Since purchase of any equipment in healthcare is a significant expense, especially for a private surgery, it is necessary to carefully consider the advantages and disadvantages of each decision. The aim of this study is to evaluate healthcare equipment in electrocardiography, when the result is determined by a group of decision makers. To obtain individual decisions we applied the AHP to the views of any decision makers. Subsequently these decisions are aggregated into one group vector with the assistance of the AMM and WAMM. The WAMM, uses the view significance of each decision maker during the process of aggregating weights. This study also contains an overview of the past AHP applications in the healthcare and health technology assessment from other authors.
... This advancement has not just improved the survival rate but also has brought improvements in the diagnosis of disease or injury [12]. With these advancements, researchers have started their work for medical healthcare services as they came to know that it is a crucial step toward the effectiveness of clinical engineers to investigate in a better way and to ensure the patients' safety [13,14,15]. ...
... The number of cases involving death due to medical equipment failures is increasing year by year [6]. Besides, the investigation conducted by The Joint Commission [7] reported that the "sentinel events" which refer to the safety accidents caused by medical equipment failures and unrelated to patients' diseases [8] are usually among the top ten causes of medical accidents every year. A more specific investigation that involves 169 anesthesia machines in 45 hospitals shows that most of the medical equipment are degrading and have potential safety hazards [9]. ...
Abstract
Background
The breakdown of healthcare facilities is a huge challenge for hospitals. Medical images obtained by Computed Tomography (CT) provide information about the patients' physical conditions and play a critical role in diagnosis of disease. To deliver high-quality medical images on time, it is essential to minimize the occurrence frequencies of anomalies and failures of the equipment.
Methods
We extracted the real-time CT equipment status time series data such as oil temperature, of three equipment, between May 19, 2020, and May 19, 2021. Tube arcing is treated as the classification label. We propose a dictionary-based data-driven model SAX-HCBOP, where the two methods, Histogram-based Information Gain Binning (HIGB) and Coefficient improved Bag of Pattern (CoBOP), are implemented to transform the data into the bag-of-words paradigm. We compare our model to the existing predictive maintenance models based on statistical and time series classification algorithms.
Results
The results show that the Accuracy, Recall, Precision and F1-score of the proposed model achieve 0.904, 0.747, 0.417, 0.535, respectively. The oil temperature is identified as the most important feature. The proposed model is superior to other models in predicting CT equipment anomalies. In addition, experiments on the public dataset also demonstrate the effectiveness of the proposed model.
Conclusions
The two proposed methods can improve the performance of the dictionary-based time series classification methods in predictive maintenance. In addition, based on the proposed real-time anomaly prediction system, the model assists hospitals in making accurate healthcare facilities maintenance decisions.
... After the simulation, it can be seen from the cloud map that when the inlet speed is 7.5 m/s, the wind speed range in the barrel cavity is 7 m/s~10 m/s. When the wind speed is 10 m/s, the wind speed range in the cavity is 9 m/s~12 m/s; When the wind speed is 12.5 m/s, the wind speed range in the cavity is 11 m/s~14 m/s; When the wind speed is 15 m/s, the wind speed range in the cavity is 12 m/s~16 m/s [14]. Under different inlet speeds, the speed change line diagram in the air sorting room is shown in figure 6. Figure 7 is the histogram of the separation situation after statistics, because the best separation effect is to make the tobacco outlet have the least tobacco stem content, and at the same time, the tobacco stem outlet should have the least tobacco content; It can be seen from the histogram that when the wind speed is 7.5 m/s, the tobacco content at the outlet of the smoke stalk is more, reaching 35%; when the wind speed is 12.5 m/s, the content of tobacco stalk at the tobacco outlet and the tobacco content at the outlet of the tobacco stalk are low. ...
To enhance the separation efficiency in the separator during the process of separating tobacco leaves from the stems, it is necessary to study how the airflow affects the motion of the tobacco in the separation chamber. Therefore, a DEM-CFD coupling model is established to simulate the interaction between the airflow and the mixed tobacco leaves and stems. Different inlet wind speeds, between 7.5 m/s–15 m/s are set to study the effect of inlet velocity on the stems separation efficiency. The results show that the inlet wind speed of 12.5 m/s–15 m/s can lead to a better separation effect, giving a 76% destemming rate with only 4% tobacco leaves waste.
... The equipment anomalies could result in low quality radiographic images, unexpected delays in patient care, costly maintenance services, and even serious patient incidents. According to the Joint Commission (TJC) [1], the safety accidents such as premature deaths, severe injuries and disability accidents, are closely related to the medical equipment failures [2]. It was reported that there were a total of 176 medical equipment-related incidents in the US, accounting for 2.9% of the total number of 6093 activities collected from 8 hospitals during the period 2004-2011 [3]. ...
Background
Large-scale medical equipment, which is extensively implemented in medical services, is of vital importance for diagnosis but vulnerable to various anomalies and failures. Most hospitals that conduct regular maintenance have been suffering from medical equipment-related incidents for years. Currently, the Internet of Medical Things (IoMT) has emerged as a crucial tool in monitoring the real-time status of the medical equipment. In this paper, we develop an IoMT system of Computed Tomography (CT) equipment in the West China Hospital, Sichuan University and collected the system status time-series data. Novel multivariate time-series classification models and frameworks are proposed to predict the anomalies of CT equipment. The important features that are closely related to the equipment anomalies are identified with the model.
Methods
We extracted the real-time CT equipment status time-series data of 11 equipment between May 19, 2020 and May 19, 2021 from the IoMT, which includes the equipment oil temperature, anode voltage, etc. The arcs are identified as labels of anomalies due to their relationship with decreased imaging quality and CT equipment failures. To improve prediction accuracy, the statistics and transformations of the raw historical time-series data segment in the sliding time window are used to construct new features. Due to the particularity of time-series data, two frameworks are proposed for splitting the training and test sets. Then the Decision Tree, Support Vector Machine, Logistic Regression, Naive Bayesian, and K-Nearest Neighbor classification models are used to classify the system status. We also compare our model to state-of-the-art models.
Results
The results show that the anomaly prediction accuracy and recall of our method are 79% and 77%, respectively. The oil temperature and anode voltage are identified as the decisive features that may lead to anomalies. The proposed model outperforms the others when predicting the anomalies of the CT equipment based on our dataset.
Conclusions
The proposed method could predict the state of CT equipment and be used as a reference for practical maintenance, where unexpected anomalies of medical equipment could be reduced. It also brings new insights into how to handle non-uniform and imbalanced time series data in practical cases.
... As technology advances and the number of equipment increases in healthcare facilities, R&M costs spent in hospitals have ascended correspondingly. Researchers estimated that 1% of a hospital budget would spend on maintenance and management of medical equipment, which is about $5 million/year for a 500-bed facility [5]. In addition to the high costs of R&M plans, a large number of factors affect R&M schedules that make work order planning a significant challenge for healthcare FM managers. ...
Work order management in hospitals that are dynamic and high-pressured environments is a significant challenge. Healthcare Repair and Maintenance (R&M) managers need to plan work orders precisely, quickly, and cost-effectively according to technical and administrative aspects and resource limitations. This paper develops a new framework to optimize R&M schedules in hospitals by minimizing direct and indirect costs (personnel, travel, and delay costs), considering managers' preferences and constraints. The proposed framework integrates Building Information Modeling (BIM), Genetic Algorithm (GA), and Discrete Event Simulation (DES) to discover the optimum plan for R&M tasks and employs BIM and Augmented Reality (AR) to support on-site activities by navigating and retrieving information. The proposed framework enables healthcare managers to reduce the time and costs of R&M plans and utilize personnel more efficiently. Implementing this system on an actual case study proves its capabilities in basing a good foundation to provide personnel's visualized work packages automatically.
... The delivery of healthcare services to the communities are significantly affected without effective management implementation (3)(4)(5). Medical equipment is a crucial asset that substantially contributes to the effectiveness and healthcare services quality enhancement (6,7). As the medical equipment aids various services in the healthcare sector, the management representative, such as clinical engineers, must monitor and upkeep the assets by performing several maintenances works throughout the equipment life cycle (8,9). ...
Medical equipment highly contributes to the effectiveness of healthcare services quality. Generally, healthcare institutions experience malfunctioning and unavailability of medical equipment that affects the healthcare services delivery to the public. The problems are frequently due to a deficiency in managing and maintaining the medical equipment condition by the responsible party. The assessment of the medical equipment condition is an important activity during the maintenance and management of the equipment life cycle to increase availability, performance, and safety. The study aimed to perform a systematic review in extracting and categorising the input parameters applied in assessing the medical equipment condition. A systematic searching was undertaken in several databases, including Web of Science, Scopus, PubMed, Science Direct, IEEE Xplore, Emerald, Springer, Medline, and Dimensions, from 2000 to 2020. The searching processes were conducted in January 2020. A total of 16 articles were included in this study by adopting Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). The review managed to classify eight categories of medical equipment reliability attributes, namely equipment features, function, maintenance requirement, performance, risk and safety, availability and readiness, utilisation, and cost. Applying the eight attributes extracted from computerised asset maintenance management system will assist the clinical engineers in assessing the reliability of medical equipment utilised in healthcare institution. The reliability assessment done in these eight attributes will aid clinical engineers in executing a strategic maintenance action, which can increase the equipment's availability, upkeep the performance, optimise the resources, and eventually contributes in providing effective healthcare service to the community. Finally, the recommendations for future works are presented at the end of this study.
... Monitoring during the sterilization process is crucial to do [7]. One of the objectives is to determine the conditions during which the autoclave is used based on its parameters [8]. The main parameters of the autoclave are temperature, pressure, and working time [9]. ...
The autoclave is one of the tools used for the sterilization process. If it is used as medical equipment in a hospital, according to the laws and regulations, it must have a certificate of proper use. Currently, autoclave calibration is carried out analogously by testing bacterial growth on spore strip paper. This research is part of the autoclave innovation and aims to create a process design for recording history data on the sterilization process. Data recording uses the data logger method recorded onto the SD Card memory media to record parameter values every second. Program commands are written in C language and run on the Arduino Mega 2560 microprocessor module. The autoclave temperature was detected by the RTD PT100 sensor, and the pressure inside the autoclave was detected using the MPX5700AP sensor. The recording was done in real-time using the DS3231 Real Time Clock (RTC) timer. The research results are to analyze the autoclave condition in more detail in order to obtain the eligibility status and facilitate the calibration process.
... In this regard, implementation of appropriate maintenance strategies requires the following types of resources: human resources, material resources, financial resources and documentation. Our findings also point to the importance of these resources [21]. ...
Background
Effective maintenance management of medical equipment is one of the major issues for quality of care, for providing cost-effective health services and for saving scarce resources. This study aimed to develop a comprehensive checklist for assessing the medical equipment maintenance management (MEMM) in the Iranian hospitals.
Methods
This is a multi-methods study. First, data related to factors which affect the assessment of MEMM were collected through a systematic review in PubMed, ProQuest, Scopus, Embase, and web of science without any time limitation until October 2015, updated in June 2017. Then, we investigated these factors affecting using document review and interviews with experts in the Iranian hospitals. In the end, the results of the first and second stages were combined using content analysis and the final checklist was developed in a two-round Delphi.
Results
Using a combination of factors extracted from the systematic and qualitative studies, the primary checklist was developed in the form of assessment checklists in seven dimensions. The final checklist includes 7 dimensions and 19 sub-categories: “resources = 3,” “quality control = 3,” “information bank = 4,” “education = 1,” “service = 3,” “inspection and preventive maintenance = 2” and “design and implementation = 3.”
Conclusions
Developing an assessment checklist for MEMM provide a comprehensive framework for the proper implementation of accurate assessment of medical equipment maintenance. This checklist can be used to improve the profitability of health facilities and the reliability of medical equipment. In addition, it is implicated in the 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 the assessment of this process.
... Studies conducted using data collected from hundreds of acute-care hospitals indicate that on average, each hospital acquired about 15-20 pieces of medical equipment for each staffed bed, with a capital investment of around US$200-400.000/staffed bed [34,35]. In addition to high initial investment, the fact that medical equipment re-quires continual and costly maintenance, as well as supplies and specialized users, means that the initial purchasing cost of medical equipment, actually, represents only a small fraction of the total cost. ...
Biomedical technologies are the basis of a functioning health system, in particular, medical devices are essential for the prevention, diagnosis, treatment of diseases. However, while developed country hospitals are renewing their fleet of machines by divesting large quantities of biomedical equipment annually, there is a chronic lack of biomedical technology in developing countries to support clinical activities, which could be met by the reuse of used equipment, adapted to the new hospital environment. However, even if the donations of biomedical technologies are generally made with good intentions and not-profit making as in the case under study, obtained results are not what we expected also due to a not perfect communication between donors and recipients and a lack of culture about technology maintenance in the developing countries. At the moment, there is little documented evidence to support these statements. For this reason, the aim of this paper is to quantify the donated medical equipment that are out of service in two different hospitals in Benin. The information was collected on the type of communication existing between donors and beneficiaries and on the type of support that donors provide in terms of staff training, manuals and maintenance. It was observed that more than 50% of the donated equipment is not functional. In addition in more than 70% of the cases the donors do not support the beneficiaries nor training sessions and staff formation are provided. An in-depth assessments of beneficiary structures should be carried out and all donations must be accompanied by initial user training and monitoring by donors regarding the functionality of the system. Donors-beneficiaries communication results as a key elements in the management of health technologies in low-income countries.
... This would be analogous to improving location accuracy of a global positioning system (GPS) with more satellites. In fact, a multidimensional model has been proven to work fairly well in comparing or predicting hospital equipment MC. 10 In essence, maintenance professionals should consider abandoning MC/AV or at least supplement it with additional benchmarks. There is no irrefutable reason that MC should be related to AV, be it purchasing or replacement cost. ...
... Unlike other healthcare technologies (drugs, implants, and disposable products), medical equipment need to undergo maintenance in order to maintain the high-level performance and prevent the sudden failure especially when they are serving in critical departments like for example the intensive care unit (ICU); because the failure in such cases might lead to death. Moreover, the continuous development of complex and sophisticated medical equipment is increasing their cost and hence the cost of maintenance is rising too [1]. Therefore, there is an increasing and stressing need to consider the maintenance of medical equipment from a technical point of view and a managerial point of view as well in order to have a successful maintenance program which reduces the cost of maintenance, failure rate and ensures the high reliability of devices. ...
... Maintenance can be defined as those actions (scheduled and unscheduled) for retaining a piece of equipment in, or restoring it to, a given safe and reliable condition [1]. The annual cost of maintenance (corrective and preventive), as a fraction of the total operating budget can be as high as 40%50% for the mining industry [2], 20%-30% for the chemical industry [3], and 1% for medical devices in hospital settings [4]. Unavailability of devices causes economical loses in production lines. ...
This paper aims to implement and validate a Monte Carlo-based algorithm to determine the optimal interval of preventive maintenance of medical devices. The optimization criterion used was that which maximizes the equipment’s achieved availability. The Monte Carlo algorithm was implemented and tested using maintenance data from infusion pumps, electrocardiographs, and ECG monitors from both primary and secondary data source of information. The performance of the algorithm behaved well; it had a 65-sec response time for 10,000 simulations. The accuracy of the calculations did not exceed 1%. In addition to that, the implementation of the Monte Carlo algorithm was able to determine the better availability curve for the interval of preventive maintenance “tuned” with the mean time to failure for each medical devices population type.
... In addition to its high maintenance costs, medical equipment is often involved in patient incidents that resulted in serious injuries or deaths. In fact, statistics accumulated by The Joint Commission (TJC) show medical equipment-related "sentinel events1" is typically among the top ten types every year [2]. Therefore, Hospitals and healthcare organizations must ensure that their critical medical devices are safe, accurate, reliable and operating at the required level of performance. ...
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.
... It is well-known that medical equipment is one of the largest capital investments of every healthcare organisation, often involved in patient incidents that resulted in serious injuries or deaths. [7] At present, our system guarantees preventive maintenance for equipment, but safety is not ensured. International Standard Guideline IEC 62353-2007 'medical electrical equipment-recurrent test and test after repair of medical electrical equipment', document applies to testing of medical equipment and medical electrical systems, which comply with IEC 60601-1 before putting into service, during maintenance, inspection, servicing and after repair or on occasion of recurrent test to assess the safety of such medical equipment. ...
A tertiary care 1000 bedded hospital contains more than 10,000 pieces of equipment worth approximately 41 million USD, while the power cords supplied along with the imported equipment type D/M plug to complete installation and also on-site electrical safety test is not performed. Hence, this project was undertaken to evaluate the electrical safety of all life-saving equipment purchased in the year 2013, referring to the guidelines of International Electrotechnical Commission 62353, the Association for the Advancement of Medical Instrumentation (AAMI) and National Fire Protection Association (NFPA)-99 hospital standard for the analysis of protective earth resistance and chassis leakage current. This study was done with a measuring device namely electrical safety analyser 612 model from Fluke Biomedical.
... . Mean time to repair (MTTR) is one of the widely used technical measures of the maintainability for repairable devices or components [25]. It is the average time required to perform corrective maintenance in a device or system [21]. ...
Large-scale medical equipment, extensively implemented in medical services, is of vital importance for diagnosis but vulnerable to various failures. Most of hospitals conduct regular maintenance for their medical equipment and have been suffering from medical-equipment-related incidents for years. Currently, the Internet of Medical Things (IoMT) has emerged as a crucial technology to monitor the real-time status of medical equipment. In this paper, we develop a IoMT system of Computed Tomography (CT) equipment in the West China Hospital of Sichuan University and meanwhile, select machine-learning algorithms to effectively store, preprocess and analyze the data. Specifically, a data-driven framework is proposed to predict the anomalies of CT equipment successfully. In this framework, the first step is data preprocessing, where we average the original non-uniform data and use linear interpolation to handle missing data. The second step is feature construction, where sliding time window is applied to fully reflect historical information. The third step is features selection, where the seven features that perform best are selected. Finally, we use two methods to split the training and test set, and apply random oversampling to deal with those imbalanced data before putting them into models. The results show that the prediction precision and recall of our method are 70% and 81%, respectively. The proposed method could distinguish the state of CT equipment and be used as a reference for practical maintenance by early prediction, where unexpected anomalies of medical equipment could be reduced. It also brings new insights about how to handle non-uniform and unbalanced time series data in the practical cases.
To address the demands of worldwide demographic and epidemiologic changes and globalization, as well as their effects on population health, the Ministry of Health in Oman developed a long-term plan for its health system called Health Vision 2050. The plan was shaped by international consultants, who sought to augment the vision with up-to-date evidence and achieve alignment with international standards. The Health Vision 2050 main document was anchored by 24 separate strategic studies covering different dimensions and pillars of the health system, one of which was the strategic study of medical equipment and healthcare technology (MEHT). This study analyzed the current status of MEHT, highlighted the achievements and bottlenecks, anticipated future challenges, and determined the future vision through pragmatic, contextualized, and actionable objectives and strategies that will provide a platform for comprehensive MEHT planning. Of note, pharmacological technologies, pharmaceutical drugs, and information technology have not been covered under the scope of this vision. By shedding light on this important strategic study about MEHT, the aim of this article is to assist other countries that are seeking to improve their MEHT based on the latest international guidelines and standards.
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
With the research presented in this chapter we aim to investigate the importance of the concurrent engineering (CE) philosophy in the engineering-medical multidisciplinary environment for integrated product development process (IPDP) of medical equipment. We address the requirements of a health professional user as well as patient’s needs. We have identified and contextualized the medical equipment lifecycle, the importance of CE in the IPDP of medical equipment and present propositions for the insertion of software tools that support product development phases. A discussion is included on the use of CE and IPDP oriented towards medical equipment conception and development, perspectives of engineering modular development and interface between Health and Engineering information areas for increasing technical, clinical and economic quality.
: 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.
Historically, staphylococci, pseudomonads, and Escherichia coli have been the nosocomial infection troika; nosocomial pneumonia, surgical wound infections, and vascular access-related bacteremia have caused the most illness and death in hospitalized patients; and intensive care units have been the epicenters of antibiotic resistance. Acquired antimicrobial resistance is the major problem, and vancomycin-resistant Staphylococcus aureus is the pathogen of greatest concern. The shift to outpatient care is leaving the most vulnerable patients in hospitals. Aging of our population and increasingly aggressive medical and surgical interventions, including implanted foreign bodies, organ transplantations, and xenotransplantation, create a cohort of particularly susceptible persons. Renovation of aging hospitals increases risk of airborne fungal and other infections. To prevent and control these emerging nosocomial infections, we need to increase national surveillance, "risk adjust" infection rates so that interhospital comparisons are valid, develop more noninvasive infection-resistant devices, and work with health-care workers on better implementation of existing control measures such as hand washing.
The health care delivery system is undergoing evolutionary changes that are also affecting Clinical Engineering. The integration of engineering and the life sciences created an industry whose "product" must be quality patient care. The utilization of technologies in the clinical environment is perpetually growing, and has created a need for professional technical management. The present changing environment requires Clinical Engineers to become effective leaders and efficient managers. The efficient consumption of an organization's resources is dependent on its managers' abilities to assess and optimize their operations under dynamic conditions. This paper describes some means for monitoring the clinical engineering department "output" and for measuring and reporting the relative changes in output, thus enhancing progress toward achievement of established goals. The tools and techniques offered here are not an end in themselves, but are rather a part of the process of maximizing productivity with a commitment to program output quality.
Reliability-Centered Maintenance provides valuable insights into current preventive maintenance practices and issues, while explaining how a transition from the current "preserve equipment" to "preserve function" mindset is the key ingredient in a maintenance optimization strategy. This book defines the four principal features of RCM and describes the nine essential steps to achieving a successful RCM program. There is an easy to follow example illustrating the Classical RCM systems analysis process using the water treatment system for a swimming pool. As well as the use of software in the system analysis process, making a specific recommendation on a software product to use. Additionally, this new edition possesses an appendix devoted to discussing an economic model that has been used successfully to decide the most cost effective use of maintenance. Top Level managers, engineers, and especially technicians who rely on PM programs in their plant operations can't afford to miss this inclusive guide to Reliability-Centered Maintenance.
While debated for over 30 years, productivity and staffing continue to be a challenging topic for the clinical engineering (CE) community. At the core of this challenge is the lack of reliable indicators substantiated by actual data. This article reports an attempt to evaluate some traditional and newer indicators using data collected from 2 distinct sources. Results confirm early concerns that worked hours self-entered by CE staff are subject to misuse and thus should be avoided. In contrast, good statistical correlation was found for staffing data with several hospital indicators that are consistently collected and widely available. Good correlation with CE department indicators was more difficult to find, apparently because of the lack of reliable records and consistent accounting of all CE resources and expenditures. Although no single, easy-to-measure and easy-to-understand indicator emerged as a replacement for the worked-to-paid-hours ratio, it is shown that a multidimensional model can be built to benchmark productivity and staffing. Calculations from such a model are accurate, but not precise, so the results need to be interpreted carefully. With proper precautions, such comparisons can be used as a good starting point for a more detailed analysis of the differences that could reveal substantive causes such as service scope and strategy, organizational characteristics, and geographical challenges as well as opportunities for major productivity improvements.
Data collected from clinical engineering departments in Canada, the USA, the European Economic Community and two Nordic countries, Sweden and Finland, have led to an evaluation of their budget, staffing and other resource levels. Financial strategies to support their role are proposed.
A NEW paradigm for medical practice is emerging. Evidence-based medicine de-emphasizes intuition, unsystematic clinical experience, and pathophysiologic rationale as sufficient grounds for clinical decision making and stresses the examination of evidence from clinical research. Evidence-based medicine requires new skills of the physician, including efficient literature searching and the application of formal rules of evidence evaluating the clinical literature.An important goal of our medical residency program is to educate physicians in the practice of evidence-based medicine. Strategies include a weekly, formal academic half-day for residents, devoted to learning the necessary skills; recruitment into teaching roles of physicians who practice evidence-based medicine; sharing among faculty of approaches to teaching evidence-based medicine; and providing faculty with feedback on their performance as role models and teachers of evidence-based medicine. The influence of evidencebased medicine on clinical practice and medical education is increasing.CLINICAL SCENARIO
A junior medical resident working in a teaching hospital
During the early years of clinical engineering (CE), CE professionals in the United States devoted a significant portion of their resources to detect failures through inspections (incoming and scheduled) and prevent failures through periodic parts replacement, lubrication, and other tasks (preventive maintenance), with the goal of reducing patient risks. With the rapid evolution of technology in the last 3 decades that increased medical equipment reliability, it is unclear whether CE professionals should continue to focus their attention on equipment failure detection and prevention or broaden their scope to enhance further patient safety. Using scheduled and unscheduled maintenance data collected for almost 2 years from 8 hospitals and a standardized failure classification method, 22 equipment types were analyzed in terms of actions that CE can undertake to improve safety: directly, indirectly, or in the future. For each of these 3 CE action groups, the risk associated with the use of equipment was estimated from the respective failure probability and severity of harm. The results show that, for most equipment types, CE professionals have reached the saturation point of what they can do to reduce risks, although some redirection of their attention from certain equipment types to others would optimize the use of limited resources. On the other hand, plenty of opportunities exist in helping the users and other allied health professionals to reduce risks significantly through further training, better communication, and better selection in future acquisitions.
Almost since the beginning of clinical engineering as a profession, the need for scheduled maintenance (mostly safety and performance inspections) and its appropriate frequency have been debated extensively but could not be resolved conclusively because of the lack of comparable data. The combination of regulatory requirements typically based on manufacturers' recommendations and concern for patient safety discouraged experimentation by clinical engineering professionals and thus limited the possibility of comparisons within the same organization. Lateral comparisons among different hospitals have been difficult because of different computerized maintenance management systems, failure classification, and reluctance to share information. Using a small set of standardized failure codes, more than 62,000 work orders were classified by dozens of biomedical technicians at 8 hospitals for almost 2 years. These data were used to compare different maintenance strategies adopted for 7 types of medical equipment commonly encountered in acute-care hospitals. No prominent differences were found among the data collected from hospitals that adopted different maintenance frequencies, statistical sampling, and even run-to-failure strategies. Most of the small differences were comparable to the SDs calculated from the data for each maintenance strategy. These results suggest that it is justifiable to adopt a less resource-demanding maintenance strategy for most equipment types, except for the scheduled replacement of wearable parts that was outside the scope of this study.
Prior studies suggested that the only valid benchmark for clinical engineering (CE) would be the ratio of total CE expenses and total equipment acquisition costs. This article provides data to support the global failure rate (GFR) as a promising benchmark for measuring CE performance. Although GFR appears to work mostly for biomedical equipment, it is an outcome metric that not only measures repair activities but also measures the efforts invested in equipment planning and acquisition, preventive maintenance, user training, and controlling environmental factors. Nonetheless, GFR should not be used alone or in conjunction with financial metrics to assess CE performance. Instead, comprehensive performance tools such as the balanced-scorecard approached should be used to truly evaluate the contribution of CE to a healthcare organization.
Regulatory requirements, risk factors and liabilities, and a requirement for better asset management are generating intense interest in computerized maintenance management system (CMMS) software. CMMS programs provide corrective and preventive maintenance scheduling and record keeping. A common database can share information for repair trending, equipment histories, device tracking and contract warranty information. With the proliferation of medical equipment available, CMMS is viewed as a necessity in most healthcare institutions due to Joint Commission for the Accreditation of Healthcare Organization requirements under the Environment of Care Standards. This article will provide selection criteria, evaluation processes and review of available CMMS software.
(C)1998Aspen Publishers, Inc.
Although medical equipment maintenance has been well planned and executed for more than 30 years, very few studies have been conducted to measure and evaluate its effectiveness in terms of reliability and seriousness of failures. The lack of factual evidence limits the ability of clinical engineering (CE) professionals to revise maintenance strategy and improve the effectiveness of their work, as well as focus on the equipment and tasks that could provide the highest return for their limited resources. Using a small set of failures codes, data were collected from 8 hospitals for a period of up to 24 months, covering more than 40,000 pieces of equipment. Careful analysis of more than 62,000 work orders collected showed that the failures found for each type of equipment within a single hospital tend to converge to a stable pattern with less than 100 work orders. Furthermore, failure patterns obtained from different hospitals for the same equipment type seem to be within statistical variation of each other, although these hospitals may use different brands and models of equipment, in addition to obvious differences in user care and training, utilization intensity, and other environmental factors. The failure data collected were used to determine the probability of failure that will be used in subsequent papers of this series to compare different maintenance strategies adopted at different hospitals, as well as to determine additional opportunities for CE professionals to contribute to enhance patient safety beyond increasing equipment reliability through maintenance.
Operational and financial clinical engineering (CE) data from 253 acute care hospitals were analyzed for indicators that are statistically valid and useful for measuring, monitoring, and improving performance. The sample is mostly composed of nonprofit public and religious hospitals and is evenly distributed among major and minor teaching hospitals and nonteaching institutions. Almost all CE departments manage all biomedical equipment and provide technology management support, but only some manage imaging, laboratory, nonmedical devices, and beds. Clinical engineering departments typically use 2.5 full-time-equivalent employees per 100 staffed beds or 1 full-time-equivalent employee per 4000 adjusted discharges. Administrative support is available only at large departments. Most of the CE budget is typically spent on service contracts, whereas approximately 20% is dedicated to internal labor. One scheduled maintenance and 1 repair are typically performed per capital device per year. Although the ratio of total CE expense and total equipment acquisition costs was confirmed to be a good indicator at around 4%, several other denominators also emerged as valid and, perhaps, even more widely available for comparisons, for example, staffed beds, adjusted discharges, and number of capital devices. Overall, CE budget is around 0.5% of the hospital's total operating budget. Because of uneven data quality and impossibility of validation, each indicator should not be used individually for precise benchmarking. On the other hand, when used together, multiple indicators provide not only valuable ballpark comparisons but also insights into deviations that warrant further investigation for potential uniqueness and/or improvement opportunities.
Technology is essential to the delivery of health care but it is still only a tool that needs to be deployed wisely to ensure beneficial outcomes at reasonable costs. Among various categories of health technology, medical equipment has the unique distinction of requiring both high initial investments and costly maintenance during its entire useful life. This characteristic does not, however, imply that medical equipment is more costly than other categories, provided that it is managed properly. The foundation of a sound technology management process is the planning and acquisition of equipment, collectively called technology incorporation. This lecture presents a rational, strategic process for technology incorporation based on experience, some successful and many unsuccessful, accumulated in industrialized and developing countries over the last three decades. The planning step is focused on establishing a Technology Incorporation Plan (TIP) using data collected from an audit of existing technology, evaluating needs, impacts, costs, and benefits, and consolidating the information collected for decision making. The acquisition step implements TIP by selecting equipment based on technical, regulatory, financial, and supplier considerations, and procuring it using one of the multiple forms of purchasing or agreements with suppliers. This incorporation process is generic enough to be used, with suitable adaptations, for a wide variety of health organizations with different sizes and acuity levels, ranging from health clinics to community hospitals to major teaching hospitals and even to entire health systems. Such a broadly applicable process is possible because it is based on a conceptual framework composed of in-depth analysis of the basic principles that govern each stage of technology lifecycle. Using this incorporation process, successful TIPs have been created and implemented, thereby contributing to the improvement of healthcare services and limiting the associated expenses.
Data collected from clinical engineering departments in Canada, the USA, the European Economic Community and two Nordic countries,
Sweden and Finland, have led to an evaluation of their functional involvement in their healthcare institutions and of the
level of recognition which they have achieved.
Results of the first year of the Association for the Advancement of Medical Instrumentation (AAMI) Validating Metrics pilot project are described. The intent of the pilot project was to find direction in the measurement of medical equipment service costs and quality metrics. The pilot project collected repair and maintenance cost, turnaround time and work order count data from eight hospital-based Clinical Engineering departments using a common set of definitions for all the measured parameters. The projects history, data collection methodology, data analysis, results and direction for future work are described. The project shows that acquisition cost is the most useful indicator of service costs of those indicators evaluated to date and that bed count and device count are not valid aggregate service cost indicators.
Inter-institutional comparisons of productivity and cost-effectiveness can be a valuable source of feedback to the in-house biomedical or clinical engineering services manager. But for such comparisons to be valid, all institutions must use the same criteria. As yet, there are no standard definitions for such criteria and, in most cases, the necessary data are not kept. Therefore, reliable comparisons are not possible. It is possible, however, to keep data on the variety of tasks common to all clinical engineering departments that can then be compared inter-institutionally. As task comparisons become more common, "norms" will evolve that can become standards for the profession. From there, it is a realizable step to standards that permit comparison of productivity and cost-effectiveness. A national organization, like the American Hospital Association could help by including clinical engineering data as part of their annual hospitals survey.
Finances have become the dominant concern of hospital administrators and department heads. Clinical Engineering (CE) can make significant contributions to the financial health of a hospital by increasing CE departmental productivity and by improving the utilization of resources in clinical departments. Several measures of productivity and cost-effectiveness have been applied to the Biomedical Engineering Department of the University Medical Center. The Department provides a wide range of technical services that are integrated into the clinical and administrative activities of the hospital. The Department has accumulated data regarding the financial benefits provided to the hospital, and the data reveal significant savings which show that CE can be viewed as a cost-effective investment. The greatest savings occur in capital equipment acquisition (selection and installation) and maintenance, and result from CE involvement in clinical activities and administrative decision making.
There exists a need for a standard set of definitions to describe and measure the tasks performed by the staffs of clinical engineering departments. Too often, in discussions and publications on productivity, inconsistencies exist that make comparisons difficult between the author's methodology and that used by readers or other authors. To avoid this, there needs to be uniformity in the classification of which tasks are productive and the way those tasks are documented, tabulated, and reported. This paper presents some concepts from industrial engineering, as well as descriptions of staff labor and financial terms. Definitions for productivity and other measures of departmental performance are also developed.
Two approaches to the problem of human fallibility exist: the person and the system approaches. The person approach focuses on the errors of individuals, blaming them for forgetfulness, inattention, or moral weakness. The system approach concentrates on the conditions under which individuals work and tries to build defences to avert errors or mitigate their effects. High reliability organisations - which have less than their fair share of accidents - recognise that human variability is a force to harness in averting errors, but they work hard to focus that variability and are constantly preoccupied with the possibility of failure.
Clinical engineering professionals need to continually review and improve their management strategies in order to keep up with improvements in equipment technology, as well as with increasing expectations of health care organizations. In the last 20 years, management strategies have evolved from the initial obsession with electrical safety to flexible criteria that fit the individual institution's needs. Few hospitals, however, are taking full advantage of the paradigm shift offered by the evolution of joint Commission standards. The focus should be on risks caused by equipment failure, rather than on equipment with highest maintenance demands. Furthermore, it is not enough to consider risks posed by individual pieces of equipment to individual patients. It is critical to anticipate the impact of an equipment failure on larger groups of patients, especially when dealing with one of a kind, sophisticated pieces of equipment that are required to provide timely and accurate diagnoses for immediate therapeutic decisions or surgical interventions. A strategy for incorporating multiple criteria to formulate appropriate management strategies is provided in this article.
Evidence-based Medical Equipment Maintenance Management, in A Practicum for Biomedical Engineering and Technology Management Issues
- B Wang
The Balanced Scorecard
- R S Kaplan
- D P Norton
Medical Device Accidents and Illustrative Cases
- L A Geddes
Start with Why: How Great Leaders Inspire Everyone toTake Action. Portfolio/Penguin
- S Sinek