Shedding light on the ART laboratory.

This commentary examines the impact of light conditions in the assisted reproductive technology (ART) laboratory, specifically considering gametes and embryo culture. While these processes traditionally occur in the absence of light within the female reproductive tract, laboratory conditions often involve exposure to varying wavelengths, intensities and light sources. Although literature reports describe potential detrimental effects of certain wavelengths of light on biological material, these findings are often based on experiments that might not reflect actual laboratory conditions. Current ART laboratory practices aim to minimize light exposure; however, some procedures necessitate light exposure, typically involving microscopy. Results from the authors' cross-sectional survey on light-intensity practices in ART laboratories revealed the frequent use of inadequate lighting, leading to errors and impacting staff well-being. A failure mode and effects analysis was used to identify potential failure modes and their impacts due to poor lighting. Overall, this manuscript stresses the importance of maintaining proper ambient lighting in the ART laboratory, balancing the potentially detrimental effects of light on gametes and embryos against the need for proper lighting for accurate procedures and staff well-being. Adequate lighting not only ensures the safety of reproductive cells, but also improves process management and the operators' psychological conditions.

Análisis modal de fallos y efectos en las transferencias de pacientes de urgencias a hospitalización / Patient transfers from emergency departments to other in-hospital areas: a failure mode and effects analysis

Objetivo: Este estudio analiza en profundidad el proceso de transferencia de pacientes de urgencias a hospitalización y posibles fallos para evitar problemas de seguridad mediante la identificación de líneas de mejora. Método: Se conformó un grupo de trabajo multidisciplinar compuesto por profesionales asistenciales de urgencias y hospitalización de adultos que, mediante la metodología de análisis modal de fallos y efectos (AMFE), analizó pormenorizadamente el proceso de transferencia de pacientes de urgencias a hospitalización. Para los puntos críticos identificados se estableció el índice de prioridad del riesgo (IPR) en base a su gravedad, probabilidad de aparición y de detección. Resultados: Se identificaron 8 subprocesos y 14 puntos críticos que podrían generar fallos en el proceso de transferencia. Los aspectos relacionados con la administración de medicamentos y el proceso de identificación fueron los que obtuvieron mayores puntuaciones de IPR. Para todos ellos se establecieron acciones de mejora. Se elaboró un procedimiento específico de transferencia de pacientes entre estas áreas y un listado de verificación de ingresos en hospitalización. Conclusiones: Con la metodología AMFE se ha conseguido desgranar un proceso de especial vulnerabilidad como es la transferencia de pacientes de urgencias a hospitalización y definir acciones de mejora en aras de incrementar la seguridad de los pacientes. (AU)

Objectives: To perform an in-depth analysis of the process of transferring patients from an emergency department (ED) to other areas inside a hospital and identify possible points of failure and risk so that strategies for improvement can be developed. Methods: We formed a multidisciplinary group of ED and other personnel working with hospitalized adults. The group applied failure mode and effects analysis (FMEA) to understand the in-hospital transfer processes. A risk priority scoring system was then established to assess the seriousness of each risk and the likelihood it would appear and be detected. Results: We identified 8 transfer subprocesses and 14 critical points at which failures could occur. Processes related to administering medications and identifying patients were the components that received the highest risk priority scores. Improvement strategies were established for all risks. The group created a specific protocol for in-hospital transfers and a checklist to use during handovers. Conclusion: The FMEA method helped the group to identify points when there is risk of failure during patient transfers and to define ways to improve patient safety. (AU)

Patient transfers from emergency departments to other in-hospital areas: a failure mode and effects analysis. / Análisis modal de fallos y efectos en las transferencias de pacientes de urgencias a hospitalización.

OBJECTIVES: To perform an in-depth analysis of the process of transferring patients from an emergency department (ED) to other areas inside a hospital and identify possible points of failure and risk so that strategies for improvement can be developed. MATERIAL AND METHODS: We formed a multidisciplinary group of ED and other personnel working with hospitalized adults. The group applied failure mode and effects analysis (FMEA) to understand the in-hospital transfer processes. A risk priority scoring system was then established to assess the seriousness of each risk and the likelihood it would appear and be detected. RESULTS: We identified 8 transfer subprocesses and 14 critical points at which failures could occur. Processes related to administering medications and identifying patients were the components that received the highest risk priority scores. Improvement strategies were established for all risks. The group created a specific protocol for in-hospital transfers and a checklist to use during handovers. CONCLUSION: The FMEA method helped the group to identify points when there is risk of failure during patient transfers and to define ways to improve patient safety.

OBJETIVO: Este estudio analiza en profundidad el proceso de transferencia de pacientes de urgencias a hospitalización y posibles fallos para evitar problemas de seguridad mediante la identificación de líneas de mejora. METODO: Se conformó un grupo de trabajo multidisciplinar compuesto por profesionales asistenciales de urgencias y hospitalización de adultos que, mediante la metodología de análisis modal de fallos y efectos (AMFE), analizó pormenorizadamente el proceso de transferencia de pacientes de urgencias a hospitalización. Para los puntos críticos identificados se estableció el índice de prioridad del riesgo (IPR) en base a su gravedad, probabilidad de aparición y de detección. RESULTADOS: Se identificaron 8 subprocesos y 14 puntos críticos que podrían generar fallos en el proceso de transferencia. Los aspectos relacionados con la administración de medicamentos y el proceso de identificación fueron los que obtuvieron mayores puntuaciones de IPR. Para todos ellos se establecieron acciones de mejora. Se elaboró un procedimiento específico de transferencia de pacientes entre estas áreas y un listado de verificación de ingresos en hospitalización. CONCLUSIONES: Con la metodología AMFE se ha conseguido desgranar un proceso de especial vulnerabilidad como es la transferencia de pacientes de urgencias a hospitalización y definir acciones de mejora en aras de incrementar la seguridad de los pacientes.

Multicriteria Decision Making in Supply Chain Management Using FMEA and Hybrid AHP-PROMETHEE Algorithms.

In today's global environment, supplier selection is one of the critical strategic decisions made by supply chain management. The supplier selection process involves the evaluation of suppliers based on several criteria, including their core capabilities, price offerings, lead times, geographical proximity, data collection sensor networks, and associated risks. The ubiquitous presence of internet of things (IoT) sensors at different levels of supply chains can result in risks that cascade to the upstream end of the supply chain, making it imperative to implement a systematic supplier selection methodology. This research proposes a combinatorial approach for risk assessment in supplier selection using the failure mode effect analysis (FMEA) with hybrid analytic hierarchy process (AHP) and the preference ranking organization method for enrichment evaluation (PROMETHEE). The FMEA is used to identify the failure modes based on a set of supplier criteria. The AHP is implemented to determine the global weights for each criterion, and PROMETHEE is used to prioritize the optimal supplier based on the lowest supply chain risk. The integration of multicriteria decision making (MCDM) methods overcomes the shortcomings of the traditional FMEA and enhances the precision of prioritizing the risk priority numbers (RPN). A case study is presented to validate the combinatorial model. The outcomes indicate that suppliers were evaluated more effectively based on company chosen criteria to select a low-risk supplier over the traditional FMEA approach. This research establishes a foundation for the application of multicriteria decision-making methodology for unbiased prioritization of critical supplier selection criteria and evaluation of different supply chain suppliers.

Application of the Failure Mode and Effects Analysis (FMEA) to identify vulnerabilities and opportunities for improvement prior to implementing a computerized prescription order entry (CPOE) system in a university hospital oncology clinic.

INTRODUCTION: Prior to implementing a new computerized prescription order entry (CPOE) application, the potential risks associated with this system were assessed and compared to those of paper-based prescriptions. The goal of this study is to identify the vulnerabilities of the CPOE process in order to adapt its design and prevent these potential risks. METHODS AND MATERIALS: Failure mode and effects analysis (FMEA) was used as a prospective risk-management technique to evaluate the chemotherapy medication process in a university hospital oncology clinic. A multidisciplinary team assessed the process and compared the critical steps of a newly developed CPOE application versus paper-based prescriptions. The potential severity, occurrence and detectability were assessed prior to the implementation of the CPOE application in the clinical setting. RESULTS: The FMEA led to the identification of 24 process steps that could theoretically be vulnerable, therefore called failure modes. These failure modes were grouped into four categories of potential risk factors: prescription writing, patient scheduling, treatment dispensing and patient follow-up. Criticality scores were calculated and compared for both strategies. Three failure modes were prioritized and led to modification of the CPOE design. Overall, the CPOE pathway showed a potential risk reduction of 51% compared to paper-based prescriptions. CONCLUSION: FMEA was found to be a useful approach to identify potential risks in the chemotherapy medication process using either CPOE or paper-based prescriptions. The e-prescription mode was estimated to result in less risk than the traditional paper mode.

Failure modes and effects analysis study for accelerator-based Boron Neutron Capture Therapy.

BACKGROUND: Boron Neutron Capture Therapy (BNCT) has recently been used in clinical oncology thanks to recent developments of accelerator-based BNCT systems. Although there are some specific processes for BNCT, they have not yet been discussed in detail. PURPOSE: The aim of this study is to provide comprehensive data on the risk of accelerator-based BNCT system to institutions planning to implement an accelerator-based BNCT system. METHODS: In this study, failure mode and effects analysis (FMEA) was performed based on a treatment process map prepared for the accelerator-based BNCT system. A multidisciplinary team consisting of a medical doctor (MD), a registered nurse (RN), two medical physicists (MP), and three radiologic technologists (RT) identified the failure modes (FMs). Occurrence (O), severity (S), and detectability (D) were scored on a scale of 10, respectively. For each failure mode (FM), risk priority number (RPN) was calculated by multiplying the values of O, S, and D, and it was then categorized as high risk, very high risk, and other. Additionally, FMs were statistically compared in terms of countermeasures, associated occupations, and whether or not they were the patient-derived. RESULTS: The identified FMs for BNCT were 165 in which 30 and 17 FMs were classified as high risk and very high risk, respectively. Additionally, 71 FMs were accelerator-based BNCT-specific FMs in which 18 and 5 FMs were classified as high risk and very high risk, respectively. The FMs for which countermeasures were "Education" or "Confirmation" were statistically significantly higher for S than the others (p = 0.019). As the number of BNCT facilities is expected to increase, staff education is even more important. Comparing patient-derived and other FMs, O tended to be higher in patient-derived FMs. This could be because the non-patient-derived FMs included events that could be controlled by software, whereas the patient-derived FMs were impossible to prevent and might also depend on the patient's condition. Alternatively, there were non-patient-derived FMs with higher D, which were difficult to detect mechanically and were classified as more than high risk. In O, significantly higher values (p = 0.096) were found for FMs from MD and RN associated with much patient intervention compared to FMs from MP and RT less patient intervention. Comparing conventional radiotherapy and accelerator-based BNCT, although there were events with comparable risk in same FMs, there were also events with different risk in same FMs. They could be related to differences in the physical characteristics of the two modalities. CONCLUSIONS: This study is the first report for conducting a risk analysis for BNCT using FMEA. Thus, this study provides comprehensive data needed for quality assurance/quality control (QA/QC) in the treatment process for facilities considering the implementation of accelerator-based BNCT in the future. Because many BNCT-specific risks were discussed, it is important to understand the characteristics of BNCT and to take adequate measures in advance. If the effects of all FMs and countermeasures are discussed by multidisciplinary team, it will be possible to take countermeasures against individual FMs from many perspectives and provide BNCT more safely and effectively.

Failure Mode and Effects Analysis (FMEA) at the preanalytical phase for POCT blood gas analysis: proposal for a shared proactive risk analysis model.

OBJECTIVES: Proposal of a risk analysis model to diminish negative impact on patient care by preanalytical errors in blood gas analysis (BGA). METHODS: Here we designed a Failure Mode and Effects Analysis (FMEA) risk assessment template for BGA, based on literature references and expertise of an international team of laboratory and clinical health care professionals. RESULTS: The FMEA identifies pre-analytical process steps, errors that may occur whilst performing BGA (potential failure mode), possible consequences (potential failure effect) and preventive/corrective actions (current controls). Probability of failure occurrence (OCC), severity of failure (SEV) and probability of failure detection (DET) are scored per potential failure mode. OCC and DET depend on test setting and patient population e.g., they differ in primary community health centres as compared to secondary community hospitals and third line university or specialized hospitals. OCC and DET also differ between stand-alone and networked instruments, manual and automated patient identification, and whether results are automatically transmitted to the patient's electronic health record. The risk priority number (RPN = SEV × OCC × DET) can be applied to determine the sequence in which risks are addressed. RPN can be recalculated after implementing changes to decrease OCC and/or increase DET. Key performance indicators are also proposed to evaluate changes. CONCLUSIONS: This FMEA model will help health care professionals manage and minimize the risk of preanalytical errors in BGA.

Upstream cell culture process characterization and in-process control strategy development at pandemic speed.

As of early 2022, the coronavirus disease 2019 (COVID-19) pandemic remains a substantial global health concern. Different treatments for COVID-19, such as anti-COVID-19 neutralizing monoclonal antibodies (mAbs), have been developed under tight timelines. Not only mAb product and clinical development but also chemistry, manufacturing, and controls (CMC) process development at pandemic speed are required to address this highly unmet patient need. CMC development consists of early- and late-stage process development to ensure sufficient mAb manufacturing yield and consistent product quality for patient safety and efficacy. Here, we report a case study of late-stage cell culture process development at pandemic speed for mAb1 and mAb2 production as a combination therapy for a highly unmet patient treatment. We completed late-stage cell culture process characterization (PC) within approximately 4 months from the cell culture process definition to the initiation of the manufacturing process performance qualification (PPQ) campaign for mAb1 and mAb2, in comparison to a standard one-year PC timeline. Different strategies were presented in detail at different PC steps, i.e., pre-PC risk assessment, scale-down model development and qualification, formal PC experiments, and in-process control strategy development for a successful PPQ campaign that did not sacrifice quality. The strategies we present may be applied to accelerate late-stage process development for other biologics to reduce timelines.

Análisis causal integrado en la radiosinoviortesis y el tratamiento mielosupresor de la policitemia vera / Integrated causal analysis in radiosynoviorthesis and the myelosupressor treatment of polycythemia vera

Introducción: Con la matriz de riesgo se identifican las medidas de control relevantes. El análisis de modos y efectos de fallo posterga definir la efectividad de las medidas correctivas. El uso de uno solo de estos métodos limita el alcance al evaluar los riesgos y la toma de decisiones. Objetivos: Determinar la contribución individual de las causas básicas de fallo en el riesgo radiológico de la radiosinoviortesis y el tratamiento mielosupresor de la policitemia vera, a partir del modelo de la matriz y los reportes. Métodos: Se adaptó el análisis de la gestión de la calidad en radioterapia a las prácticas en estudio y la selección individual de las causas básicas más contribuyentes al riesgo radiológico. La base internacional de incidentes aportó las causas que completaron el listado de las derivadas de la aplicación del principio de Pareto. Resultados: Los subprocesos más contribuyentes al riesgo fueron, por orden de importancia, la administración del radiofármaco, su preparación y la prescripción clínica. Para estos se identificaron las etapas, modos de fallo y sus causas más importantes. Existieron causas que contribuyeron a varios modos de fallo. El incumplimiento de procedimientos, protocolos o prácticas, la falta de entrenamiento del personal y la fatiga del personal son las causas de los riesgos identificados. Conclusiones: Se caracterizó la efectividad de las medidas correctivas de las causas más contribuyentes, las que se adicionan a las derivadas de la matriz, en el plan de mejora en la radiosinoviortesis y el tratamiento mielosupresor de la policitemia vera en Cuba(AU)

Introduction: The risk matrix identifies the relevant control measures. Failure modes and effects analysis postpones defining the effectiveness of corrective measures. Using just one of these methods limits the scope when assessing risks and making decisions. Objectives: To determine the individual contribution of the basic causes of failure in the radiological risk of radiosynoviorthesis and the myelosupressor treatment of polycythemia vera, based on the matrix model and the reports. Methods: The analysis of quality management in radiotherapy was adapted to the practices under study and the individual selection of the basic causes most contributing to radiological risk. The international incident base provided the causes that completed the list of those derived from the application of the Pareto principle. Results: The sub-processes that contributed the most to risk were, in order of importance, the administration of the radiopharmaceutical, its preparation and the clinical prescription. For these, the most important, stages, failure modes and their causes were identified. There were causes that contributed to various failure modes. Non-compliance with procedures, protocols or practices, lack of staff training and staff fatigue are the causes of the identified risks. Conclusions: The effectiveness of the corrective measures of the most contributing causes, which are added to those derived from the matrix, was characterized in the improvement plan in radiosynoviorthesis and myelosupressor treatment of polycythemia vera in Cuba(AU)

Failure mode and effects analysis on the control effect of multi-drug-resistant bacteria in ICU patients.

OBJECTIVE: The failure mode and effect analysis of the prevention and control in intensive care unit (ICU) patients with multi-drug-resistant (MDR) bacterial infection were explored and analyzed in this research. METHODS: A total of 251 critically ill patients who were hospitalized in the ICU from June to December 2019 were selected as the control group, and another 258 patients who were hospitalized in the ICU from January to June 2020 were set as the observation group. The control-group patients received conventional ICU care, the observation group was treated by the failure mode and effects analysis (FMEA), and then the prevention and control effect of the two nursing modes on multi-drug-resistant bacteria infection in the two groups were compared accordingly. RESULTS: The RPN values of the five highest-level factors in the nursing process were critically lower after the improved interventions than before the improvement. The infection rate of MDR bacteria in the observation group was obviously lower than that in the control group (14.73%, 26.69%, χ2 =11.1233, P=0.0009). In addition, the mortality rate of patients with MDR in the observation group was remarkably lower than that in the control group, and the difference was statistically significant (5.26%, 22.39%, χ2 =5.2405, P=0.0221). The satisfaction of the observation group with the ICU treatment was critically higher than that of the control group, and the difference was statistically significant (89.53%, 76.49%, χ2 =15.4094, P=0.0001). CONCLUSION: Through the application of FMEA to prevent MDR bacterial infection in ICU patients, nursing staff can accurately pay attention to the keynotes in nursing process, and as such reduce the proportion and mortality of MDR infection in ICU patients and promote the patients' satisfaction with nursing, which are all worthy of clinical application.

Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis.

Failure mode and effects analysis (FMEA), as a commonly used risk management method, has been extensively applied to the engineering domain. A vital parameter in FMEA is the risk priority number (RPN), which is the product of occurrence (O), severity (S), and detection (D) of a failure mode. To deal with the uncertainty in the assessments given by domain experts, a novel Deng entropy weighted risk priority number (DEWRPN) for FMEA is proposed in the framework of Dempster-Shafer evidence theory (DST). DEWRPN takes into consideration the relative importance in both risk factors and FMEA experts. The uncertain degree of objective assessments coming from experts are measured by the Deng entropy. An expert's weight is comprised of the three risk factors' weights obtained independently from expert's assessments. In DEWRPN, the strategy of assigning weight for each expert is flexible and compatible to the real decision-making situation. The entropy-based relative weight symbolizes the relative importance. In detail, the higher the uncertain degree of a risk factor from an expert is, the lower the weight of the corresponding risk factor will be and vice versa. We utilize Deng entropy to construct the exponential weight of each risk factor as well as an expert's relative importance on an FMEA item in a state-of-the-art way. A case study is adopted to verify the practicability and effectiveness of the proposed model.

Modeling and Fusing the Uncertainty of FMEA Experts Using an Entropy-Like Measure with an Application in Fault Evaluation of Aircraft Turbine Rotor Blades.

As a typical tool of risk analysis in practical engineering, failure mode and effects analysis (FMEA) theory is a well known method for risk prediction and prevention. However, how to quantify the uncertainty of the subjective assessments from FMEA experts and aggregate the corresponding uncertainty to the classical FMEA approach still needs further study. In this paper, we argue that the subjective assessments of FMEA experts can be adopted to model the weight of each FMEA expert, which can be regarded as a data-driven method for ambiguity information modeling in FMEA method. Based on this new perspective, a modified FMEA approach is proposed, where the subjective uncertainty of FMEA experts is handled in the framework of Dempster-Shafer evidence theory (DST). In the improved FMEA approach, the ambiguity measure (AM) which is an entropy-like uncertainty measure in DST framework is applied to quantify the uncertainty degree of each FMEA expert. Then, the classical risk priority number (RPN) model is improved by aggregating an AM-based weight factor into the RPN function. A case study based on the new RPN model in aircraft turbine rotor blades verifies the applicable and useful of the proposed FMEA approach.

Risk assessment and quality improvement of liquid waste management in Taiwan University chemical laboratories.

The policy of establishing new universities across Taiwan has led to an increase in the number of universities, and many schools have constructed new laboratories to meet students' academic needs. In recent years, there has been an increase in the number of laboratory accidents from the liquid waste in universities. Therefore, how to build a safety system for laboratory liquid waste disposal has become an important issue in the environmental protection, safety, and hygiene of all universities. This study identifies the risk factors of liquid waste disposal and presents an agenda for practices to laboratory managers. An expert questionnaire is adopted to probe into the risk priority procedures of liquid waste disposal; then, the fuzzy theory-based FMEA method and the traditional FMEA method are employed to analyze and improve the procedures for liquid waste disposal. According to the research results, the fuzzy FMEA method is the most effective, and the top 10 potential disabling factors are prioritized for improvement according to the risk priority number (RNP), including "Unclear classification", "Gathering liquid waste without a funnel or a drain pan", "Lack of a clearance and transport contract", "Liquid waste spill during delivery", "Spill over", "Decentralized storage", "Calculating weight in the wrong way", "Compatibility between the container material and the liquid waste", "Lack of dumping and disposal tools", and "Lack of a clear labels for liquid waste containers". After tracking improvements, the overall improvement rate rose to 60.2%.

Process control of maintenance management for large medical equipment / 中国医学装备

Objective:To explore the process of maintenance management for large medical equipment, and the structure design and core connotation of the maintenance management software of them.Methods: Medical linear accelerator of Precise series of Elekta company was combined to achieve the objective. From the aim of designing normal operation rate to start, the failure mode and effects analysis (FMEA) was operated to analyze the maintenance strategies and maintenance manners which were established on the basis of PDCA(Plan, Do, Check, Action) cycle and the analysis mode of SWOT (Strength, Weakness, Opportunity, Threat), and it could achieve the aim of normal operation rate.Results: Through explored the connotation of maintenance management and the preliminary framework of the software of maintenance management, the detail and process of maintenance management that based on personalized accelerator of process control were established, and the element factors of software of maintenance management of accelerator also was established at the same time.Conclusion: The effective maintenance management of large medical equipment is based on informatization process management of process control, and the element factor of management software need personalized customization. On the other hand, failure mode and degradation mechanism of equipment were the basis for personalized customization, and the creation has practical value.

Failure mode and effects analysis of the universal anaesthesia machine in two tertiary care hospitals in Sierra Leone.

BACKGROUND: Anaesthesia care in developed countries involves sophisticated technology and experienced providers. However, advanced machines may be inoperable or fail frequently when placed into the austere medical environment of a developing country. Failure mode and effects analysis (FMEA) is a method for engaging local staff in identifying real or potential breakdowns in processes or work systems and to develop strategies to mitigate risks. METHODS: Nurse anaesthetists from the two tertiary care hospitals in Freetown, Sierra Leone, participated in three sessions moderated by a human factors specialist and an anaesthesiologist. Sessions were audio recorded, and group discussion graphically mapped by the session facilitator for analysis and commentary. These sessions sought to identify potential barriers to implementing an anaesthesia machine designed for austere medical environments-the universal anaesthesia machine (UAM)--and also engaging local nurse anaesthetists in identifying potential solutions to these barriers. RESULTS: Participating Sierra Leonean clinicians identified five main categories of failure modes (resource availability, environmental issues, staff knowledge and attitudes, and workload and staffing issues) and four categories of mitigation strategies (resource management plans, engaging and educating stakeholders, peer support for new machine use, and collectively advocating for needed resources). CONCLUSIONS: We identified factors that may limit the impact of a UAM and devised likely effective strategies for mitigating those risks.

Risk management for outsourcing biomedical waste disposal - using the failure mode and effects analysis.

Using the failure mode and effects analysis, this study examined biomedical waste companies through risk assessment. Moreover, it evaluated the supervisors of biomedical waste units in hospitals, and factors relating to the outsourcing risk assessment of biomedical waste in hospitals by referring to waste disposal acts. An expert questionnaire survey was conducted on the personnel involved in waste disposal units in hospitals, in order to identify important factors relating to the outsourcing risk of biomedical waste in hospitals. This study calculated the risk priority number (RPN) and selected items with an RPN value higher than 80 for improvement. These items included "availability of freezing devices", "availability of containers for sharp items", "disposal frequency", "disposal volume", "disposal method", "vehicles meeting the regulations", and "declaration of three lists". This study also aimed to identify important selection factors of biomedical waste disposal companies by hospitals in terms of risk. These findings can serve as references for hospitals in the selection of outsourcing companies for biomedical waste disposal.