Research on PIVAS risk assessment and control strategy based on quality risk management (QRM).

OBJECTIVE: This study aims to evaluate the effectiveness of the Quality Risk Management (QRM) system in hospital pharmacy intravenous admixture services (PIVAS). METHODS: Failure Modes and Effects Analysis (FMEA) and risk matrix methods were used to systematically assess the critical risk points in PIVAS. By collecting and comparing relevant data from 2019 to 2023, key performance indicators (KPIs) before and after the implementation of the QRM system were quantitatively evaluated. RESULTS: The results showed that the safety and efficiency of pharmacy services significantly improved after the implementation of the QRM system. The medication error rate significantly decreased from 3.2% to 1.1%, the average medication preparation time reduced from 15.5 min to 8.2 min, and staff satisfaction increased from 6.0 to 8.5 points. Other indicators, such as cross-contamination rates and handling errors, also showed significant improvement (all outcomes p < 0.001). DISCUSSION: Systematic risk management effectively enhanced the operational performance of PIVAS, reduced medication errors, and improved the quality of healthcare services. This study highlights the key role of QRM in enhancing medication safety and productivity, providing empirical support for the implementation of similar systems in other healthcare institutions.

Improving the safety of radiotherapy treatment processes via incident-driven FMEA feedback loops.

BACKGROUND: Failure mode and effects analysis (FMEA) is a valuable tool for radiotherapy risk assessment, yet its outputs might be unreliable due to failures not being identified or due to a lack of accurate error rates. PURPOSE: A novel incident reporting system (IRS) linked to an FMEA database was tested and evaluated. The study investigated whether the system was suitable for validating a previously performed analysis and whether it could provide accurate error rates to support the expert occurrence ratings of previously identified failure modes. METHODS: Twenty-three pre-identified failure modes of our external beam radiotherapy process, covering the process steps from patient admission to treatment delivery, were proffered on dedicated FMEA feedback and incident reporting terminals generated by the IRS. The clinical setting involved a computed tomography scanner, dosimetry, and five linacs. Incoming reports were used as basis to identify additional failure modes or confirm initial ones. The Kruskal-Wallis H test was applied to compare the risk priorities of the retrospective and prospective failure modes. Wald's sequential probability ratio test was used to investigate the correctness of the experts' occurrence ratings by means of the number of incoming reports. RESULTS: Over a 15-month period, 304 reports were submitted. There were 0.005 (confidence interval [CI], 0.0014-0.0082) reported incidents per imaging study and 0.0006 (CI, 0.0003-0.0009) reported incidents per treatment fraction. Sixteen additional failure modes could be identified, and their risk priorities did not differ from those of the initial failure modes (p = 0.954). One failure mode occurrence rating could be increased, whereas the other 22 occurrence ratings could not be disproved. CONCLUSIONS: Our approach is suitable for validating FMEAs and deducing additional failure modes on a continual basis. Accurate error rates can only be provided if a sufficient number of reports is available.

Development of modified fuzzy FMEA method in environmental risk assessment of earth dams.

The investigations have shown that the construction of the dam and its related facilities have significant physical-chemical and ecological effects on the ecosystem. Failure Mode and Effects Analysis (FMEA) is a technique for ranking risks in projects to construct dams, but it has many deficiencies and ambiguities. Therefore, to prevent the shortcomings of the classical method, the modified fuzzy inference system (MFIS-FMEA) method has been used by creating a two-stage model to more accurately assess the risk of Eyvashan Dam. First, all the considered indicators are weighted using the Shannon entropy method, and the environmental risk is prioritized using the Fuzzy OWA method. In this study, two-stage fuzzy reasoning and a Max-Min combination rule are used. When severity (SEV) and occurrence (OCC) variables are combined, the critical risk index (RCI) values are predicted in the first stage. RCI and detection index (DET) input are then used to predict the MFIS-RPN in the second stage. The results of the risk priority number (RPN) in the MFIS-RPN method are much more accurate and serious than the FIS-RPN method due to the two-stage nature and the use of new language terms. The results of the proposed MFIS-RPN technique show that the highest RPN was obtained with immediate action in the dam construction phase for soil erosion and soil pollution and in the dam operation phase for aquatic and water pollution. Therefore, due to the increase in risk score, it is necessary to take immediate and more accurate monitoring during the construction and operation phases.

Risk management of radioiodine treatment in differentiated thyroid cancer.

INTRODUCTION: Patient safety is paramount in providing quality healthcare and constitutes a global concern for healthcare systems. Radioiodine treatment to patients with well-differentiated thyroid cancer is not without risks. The aim of this study is to identify, evaluate and mitigate the risks associated with this procedure. MATERIALS AND METHODS: A single-centre descriptive study was conducted in which risk management was carried out by establishing a risk map using FMEA methodology. RESULTS: Based on the process map 6 sub-processes and 23 failure modes in the three phases of the treatment process were analysed. According to risk priority number (RPN), the sub-process with the highest risk was administrative management (RPN 82), followed by treatment per se and post-treatment imaging (both with RPN 70). An overall process RPN of 300 (156 pre-treatment, 74 treatment and 70 post-treatment) was obtained. Failures directly related to the patient pose a high risk. The implementation of verification systems, performing tasks earlier and providing quality medical information are the most relevant preventive measures to be implemented. CONCLUSIONS: The application of the FMEA methodology in the risk management for radioiodine treatment is a valuable tool for improving the quality and safety of this process. The risk map has been able to identify failures at different stages, assess their causes and effects, prioritise the risks identified and implement preventive and corrective measures that can be monitored, ensuring the effectiveness of the actions taken.

A novel sequential risk assessment model for analyzing commercial aviation accidents: Soft computing perspective.

Due to the importance of the commercial aviation system and, also, the existence of countless accidents and unfortunate occurrences in this industry, there has been a need for a structured approach to deal with them in recent years. Therefore, this study presents a comprehensive and sequential model for analyzing commercial aviation accidents based on historical data and reports. The model first uses the failure mode and effects analysis (FMEA) technique to determine and score existing risks; then, the risks are prioritized using two multi-attribute decision making (MADM) methods and two novel and innovative techniques, including ranking based on intuitionistic fuzzy risk priority number and ranking based on the vague sets. These techniques are based in an intuitionistic fuzzy environment to handle uncertainties and the FMEA features. A fuzzy cognitive map is utilized to evaluate existing interactions among the risk factors, and additionally, various scenarios are implemented to analyze the role of each risk, group of risks, and behavior of the system in different conditions. Finally, the model is performed for a real case study to clarify its applicability and the two novel risk prioritization techniques. Although this model can be used for other similar complex transportation systems with adequate data, it is mainly employed to illustrate the most critical risks and for analyzing existing relationships among the concepts of the system.

A method for empirically validating FMEA RPN scores in a radiation oncology clinic using physics QC data.

In failure modes and effects analysis (FMEA), the components of the risk priority number (RPN) for a failure mode (FM) are often chosen by consensus. We describe an empirical method for estimating the occurrence (O) and detectability (D) components of a RPN. The method requires for a given FM that its associated quality control measure be performed twice as is the case when a FM is checked for in an initial physics check and again during a weekly physics check. If instances of the FM caught by these checks are recorded, O and D can be computed. Incorporation of the remaining RPN component, Severity, is discussed. This method can be used as part of quality management design ahead of an anticipated FMEA or afterwards to validate consensus values.

How good are large language models at product risk assessment?

Product safety professionals must assess the risks to consumers associated with the foreseeable uses and misuses of products. In this study, we investigate the utility of generative artificial intelligence (AI), specifically large language models (LLMs) such as ChatGPT, across a number of tasks involved in the product risk assessment process. For a set of six consumer products, prompts were developed related to failure mode identification, the construction and population of a failure mode and effects analysis (FMEA) table, risk mitigation identification, and guidance to product designers, users, and regulators. These prompts were input into ChatGPT and the outputs were recorded. A survey was administered to product safety professionals to ascertain the quality of the outputs. We found that ChatGPT generally performed better at divergent thinking tasks such as brainstorming potential failure modes and risk mitigations. However, there were errors and inconsistencies in some of the results, and the guidance provided was perceived as overly generic, occasionally outlandish, and not reflective of the depth of knowledge held by a subject matter expert. When tested against a sample of other LLMs, similar patterns in strengths and weaknesses were demonstrated. Despite these challenges, a role for LLMs may still exist in product risk assessment to assist in ideation, while experts may shift their focus to critical review of AI-generated content.

EFLM Working Group Accreditation and ISO/CEN standards on dealing with ISO 15189 demands for retention of documents and examination objects.

Many aspects of the activity of a medical laboratory have to be documented so as to facilitate the maintenance of the ongoing quality of service. As a consequence, many documents, forms and reports are generated. The retention time for each of these has to be specified. In addition to medical laboratory reports as part of the patient's medical record, the medical laboratory has to retain many documents and specimens according to national legislation or guidance from professional organizations, if these exist. If not, the laboratory management needs to define a retention schedule, which shall define the storage conditions and period of storage, according to ISO 15189:2022 requirements for retention of general quality management documents and records. The EFLM Working Group on Accreditation and ISO/CEN standards provides here a proposal on retention periods of documentation and specimens based on a failure-mode-effects-analysis (FMEA) risk-based approach, a concept of risk reduction that has become an integral part of modern standards.

Risk-Based Fault Detection Using Bayesian Networks Based on Failure Mode and Effect Analysis.

In this article, the authors focus on the introduction of a hybrid method for risk-based fault detection (FD) using dynamic principal component analysis (DPCA) and failure method and effect analysis (FMEA) based Bayesian networks (BNs). The FD problem has garnered great interest in industrial application, yet methods for integrating process risk into the detection procedure are still scarce. It is, however, critical to assess the risk each possible process fault holds to differentiate between non-safety-critical and safety-critical abnormalities and thus minimize alarm rates. The proposed method utilizes a BN established through FMEA analysis of the supervised process and the results of dynamical principal component analysis to estimate a modified risk priority number (RPN) of different process states. The RPN is used parallel to the FD procedure, incorporating the results of both to differentiate between process abnormalities and highlight critical issues. The method is showcased using an industrial benchmark problem as well as the model of a reactor utilized in the emerging liquid organic hydrogen carrier (LOHC) technology.

Re-evaluation of the prospective risk analysis for artificial-intelligence driven cone beam computed tomography-based online adaptive radiotherapy after one year of clinical experience.

Cone-beam computed tomography (CBCT)-based online adaptation is increasingly being introduced into many clinics. Upon implementation of a new treatment technique, a prospective risk analysis is required and enhances workflow safety. We conducted a risk analysis using Failure Mode and Effects Analysis (FMEA) upon the introduction of an online adaptive treatment programme (Wegener et al., Z Med Phys. 2022). A prospective risk analysis, lacking in-depth clinical experience with a treatment modality or treatment machine, relies on imagination and estimates of the occurrence of different failure modes. Therefore, we systematically documented all irregularities during the first year of online adaptation, namely all cases in which quality assurance detected undesired states potentially leading to negative consequences. Additionally, the quality of automatic contouring was evaluated. Based on those quantitative data, the risk analysis was updated by an interprofessional team. Furthermore, a hypothetical radiation therapist-only workflow during adaptive sessions was included in the prospective analysis, as opposed to the involvement of an interprofessional team performing each adaptive treatment. A total of 126 irregularities were recorded during the first year. During that time period, many of the previously anticipated failure modes (almost) occurred, indicating that the initial prospective risk analysis captured relevant failure modes. However, some scenarios were not anticipated, emphasizing the limits of a prospective risk analysis. This underscores the need for regular updates to the risk analysis. The most critical failure modes are presented together with possible mitigation strategies. It was further noted that almost half of the reported irregularities applied to the non-adaptive treatments on this treatment machine, primarily due to a manual plan import step implemented in the institution's workflow.

Optimization of treatment workflow for 0.35T MR-Linac system.

PURPOSE: This study presents a novel and comprehensive framework for evaluating magnetic resonance guided radiotherapy (MRgRT) workflow by integrating the Failure Modes and Effects Analysis (FMEA) approach with Time-Driven Activity-Based Costing (TDABC). We assess the workflow for safety, quality, and economic implications, providing a holistic understanding of the MRgRT implementation. The aim is to offer valuable insights to healthcare practitioners and administrators, facilitating informed decision-making regarding the 0.35T MRIdian MR-Linac system's clinical workflow. METHODS: For FMEA, a multidisciplinary team followed the TG-100 methodology to assess the MRgRT workflow's potential failure modes. Following the mitigation of primary failure modes and workflow optimization, a treatment process was established for TDABC analysis. The TDABC was applied to both MRgRT and computed tomography guided RT (CTgRT) for typical five-fraction stereotactic body RT (SBRT) treatments, assessing total workflow and costs associated between the two treatment workflows. RESULTS: A total of 279 failure modes were identified, with 31 categorized as high-risk, 55 as medium-risk, and the rest as low-risk. The top 20% risk priority numbers (RPN) were determined for each radiation oncology care team member. Total MRgRT and CTgRT costs were assessed. Implementing technological advancements, such as real-time multi leaf collimator (MLC) tracking with volumetric modulated arc therapy (VMAT), auto-segmentation, and increasing the Linac dose rate, led to significant cost savings for MRgRT. CONCLUSION: In this study, we integrated FMEA with TDABC to comprehensively evaluate the workflow and the associated costs of MRgRT compared to conventional CTgRT for five-fraction SBRT treatments. FMEA analysis identified critical failure modes, offering insights to enhance patient safety. TDABC analysis revealed that while MRgRT provides unique advantages, it may involve higher costs. Our findings underscore the importance of exploring cost-effective strategies and key technological advancements to ensure the widespread adoption and financial sustainability of MRgRT in clinical practice.

A comprehensive protocol for evaluating health, safety, and environmental risks of hospital solid waste through FMEA technique.

This protocol outlines a comprehensive approach to evaluating hospital solid waste levels and assessing associated health, safety, and environmental (HSE) risks using the Failure Mode and Effects Analysis (FMEA) methodology. The study focuses on Imam Khomeini Hospital (RA) and employs both quantitative and qualitative methods. Over a 3-month period, waste production and potential risks are assessed, with specific attention to household, infectious, medicinal, and sharps waste. Through FMEA, potential failure modes and associated risks in waste management sectors are identified, enabling targeted interventions for risk mitigation. The protocol emphasizes the importance of aligning waste management practices with international standards and highlights the need for comprehensive training, awareness campaigns, and effective waste management methods to ensure the safety and environmental responsibility of hospital waste management practices.

B-FMEA-TRIZ model for scheme decision in conceptual product design: A study on upper-limb hemiplegia rehabilitation exoskeleton.

Upper-limb rehabilitation devices are essential in restoring and improving the motor function of hemiplegic patients. However, developing a product design that meets the needs of users is challenging. Current design tools and methods suffer from limitations such as a single model, poor synergy between integrated models, and subjective bias in analysing user needs and translating them into product attributes. To address these issues, this study proposes a new structural design decision-making model based on Behaviour Analysis (B), Failure Mode Effect Analysis (FMEA), and Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ theory). The model was developed and applied to design an upper-limb rehabilitation exoskeleton for hemiplegia. In this paper, an empirical investigation was conducted in several rehabilitation hospitals in Xuzhou City and used user journey mapping to identify potential failure points in the behaviour process. Then, the fault models were ranked according to the Fuzzy Risk Priority Number (FRPN) calculated by FMEA and used TRIZ theory to determine principles for resolving contradictions and generating creative design solutions for the product. By integrating B, FMEA, and TRIZ theory, it eliminated subjective bias in product design, improved the design decision-making process, and provided new methods and ideas for designing assistive rehabilitation devices and similar products. The framework of the proposed approach can be used in other contexts to develop effective and precise product designs that meet the needs of users.

Applying hesitant q-rung orthopair fuzzy sets to evaluate uncertainty in subsidence causes factors.

Land subsidence is a widespread problem impacting communities worldwide. Understanding the causes and factors of land subsidence is crucial for identifying and prioritizing effective mitigation measures. One of the main reasons for prioritizing land subsidence causes is the potential impact on infrastructure and the environment. The main objective of this paper is to emphasize the importance of prioritizing the causes of land subsidence. By understanding and prioritizing the factors contributing to land subsidence based on their impact and urgency, the aim is to develop targeted strategies for mitigation, inform policy decisions, and prevent further exacerbation of this problems. The study comprises three phases, where experts in the field provide their opinions and propose a robust hybrid framework. This framework integrates the Failure Mode and Effect Analysis (FMEA) and Step-wise Weight Assessment Ratio Analysis (SWARA) with Hesitant q-rung orthopair fuzzy set (Hq-ROFS). The performance of the proposed technique was then compared with two other decision-making techniques for evaluating and ranking land subsidence causes. According to the results, extraction of groundwater, excessive irrigation using groundwater, and oxidation and drainage of organic soils were identified as primary drivers of subsidence.

A novel decision support system for proactive risk management in healthcare based on fuzzy inference, neural network and support vector machine.

BACKGROUND: The nature of activities practiced in healthcare organizations makes risk management the most crucial issue for decision-makers, especially in developing countries. New technologies provide effective solutions to support engineers in managing risks. PURPOSE: This study aims to develop a Decision Support System (DSS) adapted to the healthcare constraints of developing countries that enables the provision of decisions about risk tolerance classes and prioritizations of risk treatment. METHODS: Failure Modes and Effects Analysis (FMEA) is a popular method for risk assessment and quality improvement. Fuzzy logic theory is combined with this method to provide a robust tool for risk evaluation. The fuzzy FMEA provides fuzzy Risk Priority Number (RPN) values. The artificial neural network is a powerful algorithm used in this study to classify identified risk tolerances. The risk treatment process is taken into consideration in this study by improving FMEA. A new factor is added to evaluate the feasibility of correcting the intolerable risks, named the control factor, to prioritize these risks and start with the easiest. The new factor is combined with the fuzzy RPN to obtain intolerable risk prioritization. This prioritization is classified using the support vector machine. FINDINGS: Results prove that our DSS is effective according to these reasons: (1) The fuzzy-FMEA surmounts classical FMEA drawbacks. (2) The accuracy of the risk tolerance classification is higher than 98%. (3) The second fuzzy inference system developed (the control factor for intolerable risks with the fuzzy RPN) is useful because of the imprecise situation. (4) The accuracy of the fuzzy-priority results is 74% (mean of testing and training data). CONCLUSIONS: Despite the advantages, our DSS also has limitations: There is a need to generalize this support to other healthcare departments rather than one case study (the sterilization unit) in order to confirm its applicability and efficiency in developing countries.

Clinical implementation of failure modes and effects analysis for gynecological high-dose-rate brachytherapy.

Purpose: To use failure modes and effects analysis (FMEA) to identify failure modes for gynecological high-dose-rate (HDR) brachytherapy pathway and score with severity, occurrence, and detectability. Material and methods: A research team was organized to observe gynecological HDR brachytherapy pathway, and draw detailed process map to identify all potential failure modes (FMs). The whole team scored FMs based on three parameters, including occurrence (O), detectability (D), and severity (S), and then multiplied three scores to obtain risk priority number (RPN). All FMs were ranked according to RPNs and/or severity scores, and FMs with the highest RPN scores (> 100) and severity scores (> 8) were selected for in-depth analysis. Fault tree analysis (FTA) was applied to find progenitor causes of high-risk FMs and their propagation path, and determine which steps in the process need to be changed and optimized. Efficiency of each existing preventive methods to detect and stop FMs was analyzed, and proposals to improve quality management (QM) and ensure patient safety were suggested. Results: The whole gynecological HDR brachytherapy pathway consisted of 5 sub-processes and 30 specific steps, in which 57 FMs were identified. Twelve high-risk FMs were found, including 7 FMs with RPNs > 100 and 5 FMs with severity scores > 8. For these FMs, 2 were in the insertion stage, 1 in the imaging stage, 4 in the treatment planning stage, and 5 in the final stage of treatment delivery. The most serious of these FMs was the change in organ at risk (OAR) during treatment delivery (RPN = 245.7). The FM that occurred most frequently was the applicator shift during patient transfer. Conclusions: Failure modes and effects analysis is a prospective risk-based tool that can identity high-risk steps before failures occur, provide preventive measures to stop their occurrence, and improve quality management system.

A survey on the effects of patient safety training programs based on SBAR and FMEA techniques on the level of self-efficacy and observance of patient safety culture in Iran hospital, Shiraz in 2022-2023.

BACKGROUND AND OBJECTIVE: Patient safety and medical personnel self-efficacy are among the main factors involved in providing quality health services. Moreover, safety culture in an organization is considered one of the most critical factors regarding patients' safety. Therefore, the present study aimed to determine the effects of patient safety programs based on Situation, Background, Assessment, Recommendation (SBAR) and Failure Model Effects Analysis (FMEA) techniques on self-efficacy and patient safety culture in Iran Hospital of Shiraz in 2022-2023. MATERIALS AND METHODS: This two-stage quasi-experimental study was conducted in 2022-2033. Considering inclusion criteria, the present study included 80 nurses working in Iran Hospital. The participants were divided into groups of SBAR (40 participants) and FMEA (40 participants). All the data were collected using a Hospital Survey on Patient Safety Culture questionnaire and Sherer General Self-Efficacy Scale. Then, the collected data were analyzed using SPSS 13, Fisher's exact test, paired t-test, and independent t-test with a significant level of P < 0.05. RESULTS: The mean score of total patient safety culture between the two groups was insignificant before the intervention (P = 0.58). However, it was more significant in the FMEA group than the SBAR group after the intervention (P < 0/05). In addition, the mean self-efficacy score between the two groups was insignificant before the intervention (P = 0.80). However, after the intervention, the mean score of the FMEA group was significantly higher than the SBAR group (P < 0.05). CONCLUSION: According to the findings of this study, there is a meaningful relationship between patient safety training programs based on SBAR and FMEA techniques on patient safety and self-efficacy of nurses; however, FMEA training has more positive effects on self-efficacy and patient safety compared to other techniques. As a result, these techniques, along with other plans, are recommended to authorities in order to help improve patient safety.

Applying Human Factors and Systems Simulation Methods to Inform a Multimillion-Dollar Healthcare Decision.

PURPOSE: This article describes a case study of a collaborative human factors (HF) and systems-focused simulation (SFS) project to evaluate potential patient and staff safety risks associated with a multimillion-dollar design and construction decision. BACKGROUND: The combined integration of HF and SFS methods in healthcare related to testing and informing the design of new environments and processes is underutilized. Few realize the effectiveness of this integration in healthcare to reduce risk and improve decision-making, safety, design, efficiency, patient experience, and outcomes. This project showcases how the combined use of HF and SFS methods can provide objective evidence to help inform decisions. METHODS: The project was initiated by a healthcare executive team looking for an objective, user informed analysis of a current connector passageway between two existing buildings. The goal was to understand the implications of keeping the current route for simultaneous use for public and patients service flow versus building and financing a new passageway for separate flow and transport. An interprofessional team of intensive care unit professionals participated in two simulations designed to test the current connector. A failure mode and effects analysis and qualitative debrief feedback was used to evaluate risks and potential failures. RESULTS: The evaluation resulted in data that enabled informed executive decision making for the most effective, efficient, and safest option for public, staff, and patient transport between two buildings. This evaluation resulted in the decision to go forward with building a multimillion-dollar new connector passageway to improve integrated care and transport.

Radiation Oncology Peer Review in a Community Setting: The Value of Prospective Review.

Peer review is an important component of any radiation oncology continuous quality improvement program. While limited guidelines exist, there is no consensus about how peer review should be performed, and large variations exist among different institutions. The purpose of this report is to describe our experience with peer review at a busy Radiation Oncology clinic and to evaluate the difference between prospective and retrospective peer review. We also performed a failure modes and effects analysis (FMEA) of the peer review process. Starting in 2015, every peer review session was tracked, including recommended changes to treatment plans. We reviewed the frequency, types and severity of these changes. A team of physicians and physicists conducted an FMEA of the peer review process. Between April 2015 and June 2020, a total of 3,691 patients were peer-reviewed. Out of those, 1,903 were prospective reviews (51.6%). Plans reviewed before treatment were almost 4.5 times more likely to be changed by peer review than those reviewed after the start of treatment (0.9% vs 0.2%). Plan changes after the start of treatment had a higher severity than changes prior to the start of treatment. FMEA identified several critical components of peer review. While there is no national standard for peer review, it is evident that prospective peer review is preferable. There may be a subconscious reluctance to change plans already underway, which could be a barrier to improving plans with the peer review process. Rather than reviewing in a group setting, it would be ideal to individually assign review tasks that are embedded in the clinical flow, assuring prospective review for all patients prior to final physician approval. Individual review rather than group review may be more candid, due to interpersonal concerns about publicly disagreeing with colleagues.

An integrative approach to medical laboratory equipment risk management.

Medical Laboratory Equipment (MLE) is one of the most influential means for diagnosing a patient in healthcare facilities. The accuracy and dependability of clinical laboratory testing is essential for making disease diagnosis. A risk-reduction plan for managing MLE is presented in the study. The methodology was initially based on the Failure Mode and Effects Analysis (FMEA) method. Because of the drawbacks of standard FMEA implementation, a Technique for Ordering Preference by Similarity to the Ideal Solution (TOPSIS) was adopted in addition to the Simple Additive Weighting (SAW) method. Each piece of MLE under investigation was given a risk priority number (RPN), which in turn assigned its risk level. The equipment performance can be improved, and maintenance work can be prioritized using the generated RPN values. Moreover, five machine learning classifiers were employed to classify TOPSIS results for appropriate decision-making. The current study was conducted on 15 various hospitals in Egypt, utilizing a 150 MLE set of data from an actual laboratory, considering three different types of MLE. By applying the TOPSIS and SAW methods, new RPN values were obtained to rank the MLE risk. Because of its stability in ranking the MLE risk value compared to the conventional FMEA and SAW methods, the TOPSIS approach has been accepted. Thus, a prioritized list of MLEs was identified to make decisions related to appropriate incoming maintenance and scrapping strategies according to the guidance of machine learning classifiers.