Critical Device Reliability Assessment in Healthcare Services

Abd Rahman, Noorul Husna; Ibrahim, Ayman Khallel; Hasikin, Khairunnisa; Abd Razak, Nasrul Anuar

doi:https://doi.org/10.1155/2023/3136511

Journal of Healthcare Engineering

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Abstract Introduction Materials and Methods Results and Discussion Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

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Advanced Computational Intelligence Technology in Healthcare Management

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Review Article | Open Access

Volume 2023 | Article ID 3136511 | https://doi.org/10.1155/2023/3136511

Critical Device Reliability Assessment in Healthcare Services

Noorul Husna Abd Rahman,^1,2Ayman Khallel Ibrahim,³Khairunnisa Hasikin,¹and Nasrul Anuar Abd Razak¹

Academic Editor: Antonio Gloria

Received10 Aug 2021

Accepted05 Apr 2022

Published20 Feb 2023

Abstract

Medical device reliability is the ability of medical devices to endure functioning and is indispensable to ensure service delivery to patients. Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) technique was employed in May 2021 to evaluate existing reporting guidelines on medical device reliability. The systematic searching is conducted in eight different databases, including Web of Science, Science Direct, Scopus, IEEE Explorer, Emerald, MEDLINE Complete, Dimensions, and Springer Link, with 36 articles shortlisted from the year 2010 to May 2021. This study aims to epitomize existing literature on medical device reliability, scrutinize existing literature outcomes, investigate parameters affecting medical device reliability, and determine the scientific research gaps. The result of the systematic review listed three main topics on medical device reliability: risk management, performance prediction using Artificial Intelligence or machine learning, and management system. The medical device reliability assessment challenges are inadequate maintenance cost data, determining significant input parameter selection, difficulties accessing healthcare facilities, and limited age in service. Medical device systems are interconnected and interoperating, which increases complexity in assessing their reliability. To the best of our knowledge, although machine learning has become popular in predicting medical device performance, the existing models are only applicable to selected devices such as infant incubators, syringe pumps, and defibrillators. Despite the importance of medical device reliability assessment, there is no explicit protocol and predictive model to anticipate the situation. The problem worsens with the unavailability of a comprehensive assessment strategy for critical medical devices. Therefore, this study reviews the current state of critical device reliability in healthcare facilities. The present knowledge can be improved by adding new scientific data emphasis on critical medical devices used in healthcare services.

1. Introduction

Medical device reliability is defined as the probability of medical devices to perform its intended function for a specified period of time or medical device ability to operate without failure and specifies a future function or performance of a device [1]. Reliability can be defined as dependability and the International Electrotechnical Commission (IEC) defines dependability as “availability performance and its influencing factors: reliability performance, maintainability performance and maintenance support performance” [1]. Previously, mean time between failures (MTBF) was often used to express reliability, whereas mean time to repair (MTTR) expresses maintainability, impacting the device's efficiency. A probabilistic measure of failure-free operation or the ability of equipment to function well without failure during a given time period under certain conditions is another definition for reliability [2]. Therefore, reliability, maintainability, availability, and safety or dependability is the ultimate goal in the maintenance scope. Medical devices should not fail frequently and must be fixed promptly when the failures are detected.

It is reported in many studies that severe injuries and patient death are closely related to faulty medical devices [2]. The World Health Organization (WHO) estimates 50–80% of equipment remains nonfunctional due to lack of maintenance culture, lack of competency, and the tendency to emphasis more on corrective maintenance than preventive maintenance [3]. Deficiency of adequate maintenance of medical devices leads to equipment downtime and reduces device performance, waste cost, and resources [4]. The data recorded in the Malaysian Government Hospitals database proved that hospital operates with massive number of aging medical devices [5]. Equipment failures are one of the challenges, and uptime of the equipment is essential for efficient healthcare delivery in any country. The older technology devices generally demand extra attention due to a lack of service or user manuals and insufficiency of manufacturer recommendations [6]. Sezdi [6] propound user training and maintenance of environmental conditions specified by equipment manufacturers as an action to minimize failures. The event of downtime or equipment failures is predominantly due to improper storage and transportation, initial failure, inappropriate handling (damage during usage), inadequate maintenance, nongenuine spare parts or refurbish spare parts, environmental stress, random failure, and improper handling repair technique and wear out failures [3].

The medical devices maintenance market are growing sophisticatedly driven by the advancement of technology and is forecast to grow at a 9.1% Compound Annual Growth Rate (CAGR) from 2021 to 2026 [7]. According to the WHO, in the USA, approximately 412 US dollars were allocated for medical devices maintenance in 2010 [8]. Besides, the capital expenditure cost for procurement and medical devices system installation is high and contributes almost 40–50% of costs in tertiary hospital setup [9, 10]. An optimal medical maintenance strategy is an essential goal with a necessity to reduce maintenance costs while increasing the device's useful life [11]. Due to myriad number of medical devices in healthcare facilities, plenty of methodologies has been adopted to ensure the devices are reliable. Prioritizing maintenance strategies is proposed to ensure uptime, budget utilization, reduce maintenance, and replacement cost by categorizing the devices to their criticality [12, 13]. Zamzam et al. [13] proposed three preventive and corrective maintenance models and replacement prioritization for comprehensive maintenance planning. A budget curtailment for maintenance expenses and replacement is essential and always a significant concern at the top management level. A prioritization technique to prevent failures is proposed for complex medical device systems and components, which may imply the patients directly [2]. Coherent medical device maintenance can ensure a longer lifespan, functionality, reliability and relies explicitly on the scientific and engineering principle, the application of biomedical engineering education, the previous maintenance history and experience, a recommendation from manufacturers, a suggestion from experts, and the obligation to comply to country regulatory requirements [14, 15]. Maintenance program’s effectuality and efficiency are measured and evaluated through maintenance history data, physical inspection, and failure analysis using several techniques to minimize or mitigate the risks. The maintenance program should not be limited to the cost of ownership, maintenance cost per acquisition, clinical engineering costs for a group, cost of clinical engineering per occupied beds, cost of clinical engineering per patient discharged, and service contracts as a percentage of total maintenance and management costs [14].

The expertise level of users and biomedical staff is another factor that affects the reliability and failures of medical devices. Employing a skilled worker with expertise in biomedical engineering is a challenge, and maintenance contracting or outsourcing services is an alternative to overcome this limitation [2]. Malaysia had adopted a maintenance service contract for medical devices and implemented a privatization policy with Concession Company since 1997 due to the same constraint. Besides the importance of scheduled maintenance and competencies to ensure reliability, other factors influencing medical devices' performance are calibration work and electrical safety testing. Calibration is accomplished to ensure accuracy, and an electrical safety test is conducted to ensure the patient is safe from electrical leakage or injury. An investigation test conducted on six high-risk medical devices concluded that 58% of devices failed the performance test, higher than previous studies with 21% and 26%, respectively [16]. Moreover, approximately 9% of infusion pumps and 12.6% of dialysis machines do not meet electrical safety requirements when measurements are conducted at healthcare facilities [17, 18]. In Malaysia, an electrical safety test is imperative throughout schedule maintenance or a newly purchased medical device, and calibration work is committed as the manufacturer suggests on selected types of medical devices.

Maintenance for medical devices at Government Hospitals in Malaysia can be categorized into three primary classifications: uptime, downtime, and upgrading time with four types of maintenance, specifically preventive maintenance, routine inspection, corrective maintenance, and schedule corrective maintenance, as indicated in Figure 1(a). Similarly, in Figure 1(b), predetermined maintenance, condition-based maintenance, and predictive maintenance are used to explain the classification of maintenance. As the manufacturer suggests, preventive maintenance is accomplished in a specific time or interval throughout the year. The task is executed before failures occur or a planned replacement of wearable parts to prevent predicted failure before respective mean time to failures (MTTF) [19–21]. Routine inspection is a physical inspection of selected medical devices with minimal intervention. Meanwhile, corrective maintenance is performed subsequent to breakdown or fitting the existing errors [19]. In contrast, scheduled corrective maintenance is performed due to the extended time required to resolve the equipment breakdown issues that had been encountered during preventive maintenance [22]. Predictive maintenance is another proactive action applied to forecast the device's malfunctions by gathering data and predicting the errors that may occur in the future, decreasing the failure rate, and improving device utilization [19]. Figure 1(c)) in [21] uses an additional term: design out maintenance, construction, installation, start-up, and tuning in adopting maintenance program with similar interpretation. Besides, the medical devices management system is also embarked in the healthcare system, incorporating an equipment inventory, schedules for preventive maintenance, a work order system, outsourcing of contract management, and asset management [8]. Shohet and Lavy [23] proposed a framework on six interrelated healthcare facilities management (HFM) core domains to ensure an efficient management system. The core domains are illustrated in Figure 2 in the pentagon, with five sections reflecting one topic in HFM. There are two main sections from these core domains: Maintenance Management and Risk Management which will be elaborated further in the results and discussion section.

(a)

(b)

(c)

Based on recent global scenarios as illustrated before, although numerous studies have been conducted on medical device reliability, there is still a scarcity of review papers summarizing the bigger picture of problems associated with medical device reliability assessment. The results from systematic reviews are advantageous in identifying the gap and improving the current scientific knowledge. This study summarizes the last ten years’ publications, and research gap has been identified for future work and improvement. This manuscript evaluates and execute a systematic review using the PRISMA technique to summarize, identify existing related studies in the same research area, analyses the significant parameters affecting medical device reliability, and appropriate techniques applied formerly to assess medical device condition and its reliability.

2. Materials and Methods

2.1. Identification of Reporting Guidelines Using PRISMA Technique

The review of various articles is adopted from PRISMA, and this technique has been established since 2009 and is widely used in healthcare facilities and clinical applications [25, 26]. The process involves searching specific keywords, selecting articles, screening, sorting inclusion and exclusion criteria, extracting data, and synthesizing. A systematic review on medical devices reliability is accomplished by searching in eight databases, namely Science Direct, Scopus, IEEE Explore, Web of Science, Emerald, MEDLINE Complete, Dimensions, and Springer Link, from 2010 to May 2021. The Google Scholar database was excluded from this review due to a high number of duplicate articles accessible by the reader.

2.2. Selection of Relevant Articles

The selection of relevant articles is achieved by using the inclusion and exclusion criteria as tabulated in Table 1. There are huge numbers of articles under the similar search string keywords extracted from various sources. The article is screened, and only relevant articles are selected. Keywords in Table 2 are applied with the medical device as the main keyword, and reliability, prioritization, maintenance, and machine learning are other keywords included in the selection process. Data analysis is performed by extracting essential techniques, parameters, and deliverables are tabulated to effectively recapitulate the content of the selected articles and lead to identification of scientific research gap. The inclusion criteria and exclusion criteria are used to screen and filter relevant articles in the timeline of the year 2010 to May 2021. The criterion is listed in Table 1, where only research articles, review articles, conferences, and proceedings papers are included. The period is limited from 2010 to May 2021 to ensure only the latest articles are selected. The target is to review, screen, and summarize articles with comprehensive content specific to the desired research area and optimize the outcomes. The selection of papers is challenging and time-consuming. There are difficulties selecting only related articles in the field with innumerable medical device publications that emphasize clinical applications, diseases, and patient care.

2.3. VOSViewer Mapping and Visualization

VOSviewer is a software tool for creating maps based on network data and for visualizing and exploring maps. This software can generate maps based on network data for visualization purposes. The maps use a network as a set of items and links between the items, whereas the cluster is a set of items included in a map. The boxes in the maps represent the object of interest, whereas the link in the connection represents the relationships among two different items. The width of the line shows the strength of the link, whereas a wider length indicates a higher number of publications are obtainable online. The cluster is segregated by different colors, which shows the set of items on the map [27]. This mapping aims to visualize existing literature and their relationship, which helps to view current topics available under medical device reliability and ensures these topics are included in this review.

Figure 3 illustrates an overall view of four different relationship clusters in medical device reliability studies where the most significant cluster or the strongest link is denoted in “red.” Four distinct clusters are differentiated with colors where “red” color represents 12 topics in the study, “green” represents ten topics, “blue” consists of six topics, and the “yellow” color is the smallest cluster with six topics. The main topics used in the largest cluster in Figure 3 are prioritization, preventive maintenance, failure mode, effect analysis, resource, availability, etc. Therefore, this study shall review these important topics by using all these keywords as a search string. The network is analyzed deeper by focusing only on reliability studies in Figure 4. This figure demonstrates a strong relationship to other clusters with a total of 36 items and is divided into four different clusters for reliability study. Cluster 1 in “red” color consists of 13 items: availability, effect analysis, equipment, failure mode, preventive maintenance, prioritization, resource, etc. Cluster 2 in the “green” color indicates 10 items, including artificial intelligence, healthcare, patient, quality, research, etc. The subsequent group of clusters namely Cluster 3 denoted in “blue” with seven different items comprises of development, device, machine, performance, safety, and system. Cluster 4 in “yellow” represents six items namely failure, reliability, ventilator, etc. These findings conclude that the topic mapped under the medical device reliability is huge, and the foremost topics are performance, artificial intelligence, failure, safety, etc. The result section will discuss further all these significant aspects on numerous topics available and their implication to medical device reliability for comprehensive understanding.

3. PRISMA Flowchart

By adopting the PRISMA technique, there are 345,128 articles selected primarily from eight databases based on keywords obtained from VOS viewer network. The total number of articles is enormous due to a vast number of duplicate articles, and further screening is required to exclude the articles in the exclusion criteria. The Google Scholar database is excluded from this article screening to reduce the number of duplicated articles. The existing literature concludes 95% of Web of Science articles and 92% of Scopus articles were also found in Google Scholar [28]. After removing the duplicates, only 36 articles were screened thoroughly and included under this review, as indicated in Table 2. Meanwhile, Figure 5 shows the flowchart or process flow for article selection and screening based on the two main elements: identifying studies via databases and register and identifying studies via other methods from Government Contract Document for Privatization of Hospital Support Services at Government Hospitals in Malaysia.

4. Results and Discussion

4.1. Main Findings and Research Gap

The PRISMA framework and approach resulted in three primary areas under medical devices reliability: risk management, performance prediction for medical devices using machine learning, and medical device management system, as described in Figure 6. These three main topics are extracted from existing literature abridged in table form and summarized in Table 3. The research gap or future work was also extracted from the literature and tabulated in the table. Risk management is the most common topic used in wide applications, where prioritization, failure, and risk analysis are used. The standard techniques used in risk management topics are Failure Mode and Effect Analysis (FMEA), a combination of FMEA and Fuzzy (FFMEA), Analytical Hierarchy Process (AHP), etc.

Table 4 indicates the similarity in the methodology applied to assess risk management. From this result, AHP, FFMEA, FMEA, Mathematical Model, and Quality Function Deployment (QFD) have the most articles publications compared to the others. The same topics are conversed by Shohet and Lavy [23], where risk management and maintenance management is the two core domains in Six Healthcare Facilities Management, as deliberated in the Introduction section. The FFMEA technique is the latest combination technique applied in identifying the risk factors, mitigating failures, and contributing to medical device reliability of the devices. The main outcome in risk management is to determine or restructure the maintenance program, develop a maintenance strategy, suggest a maintenance interval, prioritize maintenance priority based on criticality, optimize budget expenditure, develop software, introduce a mathematical model, and propose a new comprehensive framework for maintenance purposes. Besides, the parameters affecting medical device reliability is highlighted such as equipment features, function, maintenance requirement, performance, risk and safety, failures, availability, utilization, and cost.

Meanwhile, performance prediction of medical devices using machine learning approach is another main topic where only three medical devices were involved in the study, specifically infant incubator [29, 30] infusion, and perfusion pump [31], and defibrillator [32]. However, the three medical devices are written in separate articles. This topic is subjected to more future work, where numerous medical devices are still available to be studied, especially on critical medical devices. Kovačević et al. [30] attained an accuracy of 98.5%, intending to forecast device functionality for infant incubators by two categories: accurate and faulty. A similar methodology is applied by Badnjevic et al. [33] for infant incubators and mechanical ventilators. A performance parameter value is utilized for a defibrillator and achieved excellent performance of 100% accuracy in Random Forest classifier to predict positive: for device passed inspection or negative: for faulty devices [32]. Meanwhile, Hrvat et al. [31] attained 98.06% accuracy based on conformity assessment, where the outcomes are identified as pass or fail for infusion and syringe pumps. These findings conclude although a model is developed in performance prediction study; however, the model does not apply to other types of medical devices, and there is missing of cost analysis impacts an existing maintenance program. A model with the ability to reduce the likelihood of failures can ensure the availability of services, especially during a pandemic.

Medical Devices Management System is another topic to ensure medical device reliability. Bahreini et al. [4, 8] distinguished the factors affecting medical equipment maintenance and management and are divided to resources, human resources, financial, physical documentation, education, service, quality control, information bank, inspection and preventive maintenance, training and education, management, services, design, and implementation. Medical Devices Management System in this review highlights marketing strategies [34], service quality [35, 36], replacement plan [37], utilization and human resource [10], quality assurance program [38], and quality control system [39], where the replacement plan, marketing strategies, and establishment of Standard Operating Procedure (SOP) are essential in management.

The research gap is identified with a future work column in Table 3, where previous medical device reliability focuses only on limited categories of medical devices under one research article. Most articles will include a certain number of medical devices depending on data available within a specific time period. Besides, a function parameter used in existing research has therapeutic, diagnostic, analytical, and miscellaneous categories. None of the article’s highlights or focuses only on critical devices within the said categories. There is a limitation in gathering maintenance cost data, difficulties in accessing medical devices at healthcare facilities, limited years of medical devices in services, the proposed model do not apply to all types of medical devices, small sample size, limited number of hospitals involved, and there is no specific research is conducted globally on critical medical devices performance prediction using Artificial Intelligence (AI). The faulty critical medical device might lead to death and become an essential life-saving device in hospitals. When this study was written in May 2021, Malaysia is facing a third wave of COVID-19 pandemic and lockdown where most of hybrid COVID-19 hospitals running with a limited number of Intensive Care Unit (ICU) beds, limited number medical devices, especially ventilators, and relatively high bed occupancy rate (BOR). Similar impacts affect globally with the limitation of hospital beds and ventilators and involve making tough decisions in deciding patients to treat and patients to release to death [40]. Approximately one-third to one-half of patients in ICU require ventilator support in Wuhan, China, where the COVID-19 virus was first discovered in the world [41]. The shortage of critical medical devices in healthcare facilities implies service delivery to patients during this challenging time. Medical device reliability, functionality, maintenance, and other technical aspects are imperative to ensure patient service delivery. The study on performance prediction for medical devices using AI can be further explored with sufficient maintenance history data to train a model with the best accuracy. A predictive analysis model with the ability to predict impending failures can ensure the device’s uptime, minimize future failures, and deliver better service to patients and the country, especially during this pandemic. Moreover, no similar study had been conducted in Malaysia from 2010 to 2021 on performance prediction for critical medical devices.

The 36 selected articles are analyzed through a pie chart percentage in Figure 7 to demonstrate the weightage between the three main topics described above. The highest knowledge contribution in 36 articles with 34.29% (12 publications) is on Medical Devices Management System and Prioritization, followed by 25.71% or equivalent to nine publications for Failure and Risk Analysis. The lowest percentage of current knowledge is on performance prediction for medical devices using AI or Machine Learning, contributing 8.57% with three publications from selected articles. Performance prediction can be improved by adding new scientific knowledge, data, or methodology to existing expertise and substantially concludes new or improved results. The geographic areas in Figure 8 showed that the medical devices reliability study in Iran and Africa denotes the highest percentage with four publications or 11%. India, Bosnia–Herzegovina, and Italy have three publications on this topic with an 8% contribution to existing knowledge. Contribution and awareness are still limited whereby only one article is published on this subject with a 3% percentage in most countries in the world.

4.2. Input Parameters in Medical Device Reliability Studies

The outcomes of medical device reliability studies had been discussed in the previous section with the diverse methodology applied to achieve the desired goals. The medical device input parameters are the pertinent element highlighted in the methodology to accomplish the outcomes. Table 5 describes eight elements identified as the former methodology’s main elements: features, function, performance, operation and maintenance, service availability, recall and hazards, cost, and utilization. The subcategory elements are listed based on outcomes achieved, and each parameter’s application depends on the objectives. For example, age is an highly used parameter and is applied to five outcomes which are Prioritization, Prioritization for Preventive Maintenance, Performance Prediction, Replacement Plan, and Mathematical Model [12, 20, 32, 37, 42, 44, 45, 47, 52]. The explanation on parameters definition is explicated in the description column, and the outcomes column is organized in colors for better elucidation.

Table 6 summarizes the input parameters based on an application, where the highest demand for input parameters required is for prioritization for PM with 18 parameters distinctly. There are differences (in “bold”) between parameters applied for prioritization and prioritization for PM purposes. A future study using simulation results based on relevant datasets shall examine the most significant parameters to be used and select only significant parameters which influence the outcome instead of using all 18 parameters for prioritization purposes. Excessive selection of input parameters might change the accuracy of the result, and there is a probability of obtaining a different result by adding or removing parameters in the dataset. In machine learning or AI application, excessive predictors demand a higher training time during model development and are laborious.

5. Conclusions

The literature on medical device reliability is examined in this review, with 36 selected articles from 2010 to May 2021 being recapitulated, and research gaps are identified. PRISMA framework approach is used to investigate all articles in the medical devices’ reliability study. The result concludes medical devices’ reliability is categorized into three main areas: risk management, performance prediction using AI, and medical devices management system. Most studies emphasized prioritization, failure and risk analysis, and management systems. Performance prediction using AI shall be enhanced to reduce the likelihood of failures since only three papers are published under these areas, with only three medical devices involved. Medical devices are extensively used to treat COVID-19 patients in healthcare services and need to be operated without failure, especially during a pandemic A critical medical device is generally a life-saving device; high-end equipment with a high maintenance cost and uptime commitment is guaranteed for continuous service delivery. Future work shall highlight critical medical devices in a broader view and not limited to only selected medical devices. Code of Practice for Good Engineering Maintenance Management of Active Medical Devices in Malaysian Standard (MS2058) had listed 44 types of critical medical devices used at healthcare facilities [22]. The listed critical devices should be prioritized in reliability studies since most the devices are life-threatening devices in the therapeutic and diagnostic categories. They are mainly used in critical areas such as Operation Theatre (OT) and Intensive Care Unit (ICU) at healthcare facilities. Future work shall also examine the most significant parameters and select only substantial parameters that influence the overall result instead of choosing all the parameters from other authors’ experiences. Based on all review articles discussed, new scientific data on action taken after failures, maintenance cost, larger dataset, and new predictive analysis model using AI are expected to improve existing knowledge with priority given only to critical medical devices.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this study.

Acknowledgments

The authors would like to express thanks to the Director General of Health Malaysia for the continuous support in conducting this study. This work was supported by the University Malaya Research Grant Faculty Program (RF010-2018A) and International Funding from Motorola Solution Foundation (IF014-2019).

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Copyright © 2023 Noorul Husna Abd Rahman et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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