Proactive Risk Mitigation: Using Failure Modes and Effects Analysis for Evaluating Vascular Access.

Effect of Adding a Pediatric Vascular Access Team Component to a Pediatric Peripheral Vascular Access Algorithm

Enhancing reliability of failure modes and effects analysis dealing with linguistic distribution assessments: A consistency based approach

Blasting operation is the main unit of ore production in open-pit mining cycle. Detonation energy is mostly released as ground vibration using Rayleigh waves that extensively damage nearby infrastructures within or outside of a mining area. This work introduces a new hybrid approach based on the integration of fuzzy failure mode and effects analysis with the fuzzy slack-based measure model form of data envelopment analysis; this approach prioritizes and analyzes ground vibration risks on open-pit infrastructures. The approach uses a special risk prioritization algorithm by failure mode and effects analysis and data envelopment analysis under fuzzy conditions. In the fuzzy slack-based measure part of the approach, α-level cut method is used to prepare the necessary data. An open-pit copper mine is exemplified to present the approach in detail and analyze ground vibration risk. Potential failure modes, consist of infrastructures and blasting sources, are specified on the map on the mine area. Results indicate that blasting at the point of 3 imposes the highest ground vibration risk on the industrial site located within the final pit limits. The failure modes of water treatment site, crusher, gas station, explosive storage, switchgear, thickener site, power station, and processing plant have other priorities.

The Scope of Diagnostic and Interventional Nephrology

Objective To analyze the risks of hypertonic glucose injection ( 20%) (hypertonic glucose) in clinical application using failure mode and effect analysis (FMEA) method and improve the relevant management measures. Methods A study group on risk prevention strategies of hypertonic glucose was established in the First People′s Hospital of Hefei. Domestic and foreign literature on medication error cases, preventive measures or management strategies, and data from questionnaires and on-the-spot investigations were collected. The failure modes in the process of hypertonic glucose application were analyzed and assessed. Severity (S), frequency of occurrence (O), and likelihood of detection (D) of the risks related to these failure modes were scored and the risk priority numbers (RPN) were determined. The failure modes with higher RPN were screened out and the corresponding prevention strategies were formulated. The incidence of medication errors and potential hazards, cognition rate of knowledge on drugs, qualification rate of drug storage, and satisfaction rate for management measures before and after the improvement were analyzed comparatively. Results According to the results from literature search, questionnaires, and on-the-spot investigations, a total of 24 failure modes were found in the 6 links relevant with hypertonic glucose application in clinical, which included links of prescription writing and delivery, pharmacist dispensing, nurse dispensing, post-administration monitoring, drug management, and information technology. Ranking the RPN from high to low, 7 major failure modes with higher RPN were selected from the 24 failure modes, including the existence of drugs with similar name and drugs with similar package, wrong drug dispensing, absence of post-administration monitoring, lack of marked labels for drugs, garbles caused by lack of medication knowledge, and untimely record and report of medication errors. The incidences of medication errors and potential safety hazards before and after management improvement were 3.2% (7/216) and 0.4% (1/234), respectively and the difference had no statistical significance (P=0.058). The average cognitive rates of physicians, nurses and pharmacists before and after the improvement were 65.2%(15/23), 54.2%(13/24), 67.9%(19/28) and 95.8%(23/24), 91.7%(22/24), 95.5%(21/22), respectively and the differences were statistically significant (P<0.05 for all). The qualification rates of drug storage before and after management improvement were 70.2%(151/215) and 98.7%(230/233), respectively and the difference was statistically significant (P<0.05). The average satisfaction degrees of medical staff with hyperosmotic glucose management were 81.3%(61/75) and 95.7%(65/70), respectively before and after the management improvement and the difference was statistically significant (P<0.05). Conclusion Failure modes in the process of hypertonic glucose use can be effectively identified using the FMEA method, and the risk events can be effectively reduced after the corresponding improvement measures. Key words: Healthcare failure mode and effect analysis; Glucose solution, hypertonic; Hospital administration; High-alert medicine

Objective To investigate IMRT planning process using the combined application of failure modes and effects analysis (FMEA) and fault tree analysis (FTA) by reference to the report of Task Group 100 of the AAPM, and stablish and optimize the quality. Methods A multidisciplinary team detailed the process mapping of IMRT planning using Eclipse TPS. The team evaluated the potential failure modes (FMs) of every process step. The evaluation was divided into two groups according to whether quality management (QM) was considered. For every FM, occurrence (O), severity (S) and detectability (D) by consensus were evaluated, and the product of O, S and D yielded the risk priority number (RPN), which permitted the ranking of the FMs. Finally, FTA was used to determine the root factors contributing to the riskiest failure modes. Results The IMRT plan process consisted of 10 major sub-processes and 33 steps, which amounted to 47 failure modes. For the group without quality management, the RPN of FMs was between 13.2-271.8, 27 of which had RPN≥80, and 18 FMs had S≥8. For the group with quality management, the RPN of FMs was between 11.2-158.4, 11 of which had RPN≥80. The difference of RPN between the two groups was statistically significant (RPN of the group without QM=101.17±66.34, RPN of the group with QM=59.54±35.64, t=8.501, P<0.05). Finally, FTA was used to determine the root factors contributing to the FMs, i. e., prescription dose definition and importing images. Conclusions The FMEA and FTA methods are operable and practical, which can systematically and comprehensively analyze the potential failures and risks existing in the process of IMRT plan. And the FMEA and FTA can contribute to establish and optimize the quality management program in radiotherapy. Key words: Intensity modulated radiotherapy; Failure modes and effects analysis; Failure tree analysis; Quality management

BackgroundMedical ultrasound device has been more and more widely used in the hospital and Its safety risk is significantly increased when failures occur. However, there is a lack of quantitative risk assessments of different types of failure modes for medical ultrasound device. This study utilizes a failure mode, effect and criticality analysis (FMECA) approach for quantitative risk evaluation of different failure modes for ultrasound devices.MethodsThe 4216 medical ultrasound device failure records at various hospitals were investigated. A failure mode and effect analysis method was developed for the quantitative evaluation of the risks of different failure modes. Visual correlation analysis was conducted for all categories to identify the causes of various failure modes. Based on the severity, occurrence and detectability of the failure causes determined, the risk priority number (RPN) for each failure mode was back-calculated through the obtained tracking diagram.ResultsThe failure modes of unclear images, unable to power on and dark shadows on an image had the highest RPNs. One failure mode could be caused by various factors, and the failure location was not necessarily related to the cause of the failure.ConclusionsThis quantitative approach more accurately evaluated the risks of different failure modes, and the results of the corresponding analysis of failure modes and causes could support the rapid determination of the causes of failures in clinical practice.

Objective To explore the application effect of health-care failure mode and effect analysis (HFMEA) in infection control among hemodialysis patients. Methods In October 2014, Northern Jiangsu People′s Hospital hemodialysis unit established a management group for HFMEA. We assessed the susceptible failure of hemodialysis patients and analyzed the causes to propose corrective measures. We compared the control group and the intervention group in following aspects: the occurrence of infection (vascular access infection, respiratory infection, urinary system infection and other infections) , risk priority number (RPN) of HFMEA and patient′s satisfaction. Results The incidence rate of infection in hemodialysis patients was significantly decreased after the application of HFMEA mode. RPN value of failure mode easy to cause infection dropped to 605 points′s from 1 470 points, and the total scores reduced by 58.8%; the patient′s satisfaction increased from (81.31±6.74) to (91.70±5.02) (t=23.935, P<0.05) . Conclusions By using HFMEA mode, the failure can be forecasted in hemodialysis procedure and the potential danger can be quantized through RPN. In addition, the projects that need to be focusd on can be identified. So as to effectively reduce the incidence of infection in hemodialysis patients and improve patient′s satisfaction. Key words: Renal dialysis; Infection; Effect of health-care failure and effect analysis (HFMEA)

BackgroundIntroducing a de-novo home haemodialysis (HHD) program often raises safety concerns as errors could potentially lead to serious adverse events. Despite the complexity of performing haemodialysis at home without the supervision of healthcare staff, HHD has a good safety record. We aim to pre-emptively identify and reduce the risks to our new HHD program by risk assessment and using failure mode and effects analysis (FMEA) to identify potential defects in the design and planning of HHD.MethodsWe performed a general risk assessment of failure during transitioning from in-centre to HHD with a failure mode and effects analysis focused on the highest areas of failure. We collaborated with key team members from a well-established HHD program and one HHD patient. Risk assessment was conducted separately and then through video conference meetings for joint deliberation. We listed all key processes, sub-processes, step and then identified failure mode by scoring based on risk priority numbers. Solutions were then designed to eliminate and mitigate risk.ResultsTransitioning to HHD was found to have the highest risk of failure with 3 main processes and 34 steps. We identified a total of 59 areas with potential failures. The median and mean risk priority number (RPN) scores from failure mode effect analysis were 5 and 38, with the highest RPN related to vascular access at 256. As many failure modes with high RPN scores were related to vascular access, we focussed on FMEA by identifying the risk mitigation strategies and possible solutions in all 9 areas in access-related medical emergencies in a bundled- approach. We discussed, the risk reduction areas of setting up HHD and how to address incidents that occurred and those not preventable.ConclusionsWe developed a safety framework for a de-novo HHD program by performing FMEA in high-risk areas. The involvement of two teams with different clinical experience for HHD allowed us to successfully pre-emptively identify risks and develop solutions.

The COVID-19 outbreak is of great concern due to the high rates of infection and the large number of deaths worldwide. In this paper, we considered a Bayesian inference and failure mode and effects analysis of the modified susceptible-exposed-infectious-removed model for the transmission dynamics of COVID-19 with an exponentially distributed infectious period. We estimated the effective reproduction number based on laboratory-confirmed cases and death data using Bayesian inference and analyse the impact of the community spread of COVID-19 across the United Kingdom. We used the failure mode and effects analysis tool to evaluate the effectiveness of the action measures taken to manage the COVID-19 pandemic. We focused on COVID-19 infections and therefore the failure mode is taken as positive cases. The model is applied to COVID-19 data showing the effectiveness of interventions adopted to control the epidemic by reducing the reproduction number of COVID-19. Results have shown that the combination of Bayesian inference, compartmental modelling and failure mode and effects analysis is effective in modelling and studying the risks of COVID-19 transmissions, leading to the quantitative evaluation of the action measures and the identification of the lessons learned from the governmental measures and actions taken in response to COVID-19 in the United Kingdom. Analytical and numerical methods are used to highlight the practical implications of our findings. The proposed methodology will find applications in current and future COVID-19 like pandemics and wide quality engineering.

Failure mode and effects analysis (FMEA) is a widely used, powerful tool to identify and assess potential failure modes in products and to make products more reliable. Due to the complexity of products and lack of knowledge, FMEA involves many uncertainties in practice. In previous studies, numerous modified FMEA methods based on fuzzy logic and Dempster-Shafer (D-S) evidence theory have been employed to address these uncertainties. These studies focus on how to handle uncertainties and to identify a more reliable prioritization of risk priority numbers (RPNs). However, studies have not sufficiently examined how many uncertainties are present in resulting RPNs. To better model and process various types of uncertainties in FMEA, two new area-based metrics are constructed in this paper. One is the interval area metric (IAM), which is used in RPN representation. The other is the dimensionless uncertainty metric (DUM), which is used to measure how many uncertainties there are in RPN. IAM is used to rank the risks in failure modes, and DUM is used to rank the uncertainties in failure modes. Then, an expert system is presented to qualitatively evaluate the DUM, which can help FMEA users intuitively judge whether further investigation should be performed to alleviate the epistemic uncertainties in each failure mode. Finally, a practical risk evaluation case regarding the grinding wheel system of a numerically controlled (NC) machine is provided to demonstrate the application and effectiveness of the proposed FMEA. The case study shows that the calculation programs of IAM and DUM do not require any assumptions or need to address conflict among experts. In addition, proposed method can not only give a more accurate rating of each failure mode, but also help designers intuitively see the uncertainty grade of each RPN, which is useful to help them understand FMEA results.

Identifying risk characteristics using failure mode and effect analysis for risk management in online magnetic resonance-guided adaptive radiation therapy.

Evaluating the academic scientific laboratories' safety by applying failure mode and effect analysis (FMEA) at the public university in Lebanon

User experience (UX) is crucial for interactive system design. To improve UX, one method is to identify failure modes related to UX and then take action on the high-priority failure modes to decrease their negative impacts. For the UX of interactive system design, the failure modes under consideration are human errors or difficulties, and thus the risk factors concerning failure modes are subjective and even subconscious. Existing methods are not sufficient to deal with these issues. In this paper, a fuzzy failure mode and effect analysis (FMEA)-based hybrid approach is proposed to improve the UX of interactive system design. First, hierarchical task analysis (HTA) and systematic human error reduction and prediction approach (SHERPA) are combined to identify potential failure modes concerning UX. Subsequently, fuzzy linguistic variables are employed to assess the risk parameters of the failure modes, and the similarity aggregation method (SAM) is adopted to aggregate the fuzzy opinions. Then, on the basis of the aggregation results, fuzzy logic is adopted to compute the fuzzy risk priority numbers that can prioritize the failure modes. Finally, the failure modes with high priorities are considered for corrective actions. An in-vehicle information system was employed as a case study to illustrate the proposed approach. The findings indicate that, compared with other methods, our approach can provide more accurate results for prioritizing failure modes related to UX, and can successfully deal with the subjective and even subconscious nature of the risk factors associated with failure modes. This approach can be universally utilized to enhance the UX of interactive system design.

Using failure mode and Effects Analysis to increase patient safety in cancer chemotherapy.