Software Failure Mode and Effects Analysis

With the publication of the quality guideline ICH Q9 "Quality Risk Management" by the International Conference on Harmonization, risk management has already become a standard requirement during the life cycle of a pharmaceutical product. Failure mode and effect analysis (FMEA) is a powerful risk analysis tool that has been used for decades in mechanical and electrical industries. However, the adaptation of the FMEA methodology to biopharmaceutical processes brings about some difficulties. The proposal presented here is intended to serve as a brief but nevertheless comprehensive and detailed guideline on how to conduct a biopharmaceutical process FMEA. It includes a detailed 1-to-10-scale FMEA rating table for occurrence, severity, and detectability of failures that has been especially designed for typical biopharmaceutical processes. The application for such a biopharmaceutical process FMEA is widespread. It can be useful whenever a biopharmaceutical manufacturing process is developed or scaled-up, or when it is transferred to a different manufacturing site. It may also be conducted during substantial optimization of an existing process or the development of a second-generation process. According to their resulting risk ratings, process parameters can be ranked for importance and important variables for process development, characterization, or validation can be identified. Health authorities around the world ask pharmaceutical companies to manage risk during development and manufacturing of pharmaceuticals. The so-called failure mode and effect analysis (FMEA) is an established risk analysis tool that has been used for decades in mechanical and electrical industries. However, the adaptation of the FMEA methodology to pharmaceutical processes that use modern biotechnology (biopharmaceutical processes) brings about some difficulties, because those biopharmaceutical processes differ from processes in mechanical and electrical industries. The proposal presented here explains how a biopharmaceutical process FMEA can be conducted. It includes a detailed 1-to-10-scale FMEA rating table for occurrence, severity, and detectability of failures that has been especially designed for typical biopharmaceutical processes. With the help of this guideline, different details of the manufacturing process can be ranked according to their potential risks, and this can help pharmaceutical companies to identify aspects with high potential risks and to react accordingly to improve the safety of medicines.

With increasing deployment, complexity, and sophistication of equipment and related processes within the clinical imaging environment, system failures are more likely to occur. These failures may have varying effects on the patient, ranging from no harm to devastating harm. Failure mode and effect analysis (FMEA) is a tool that permits the proactive identification of possible failures in complex processes and provides a basis for continuous improvement. This overview of the basic principles and methodology of FMEA provides an explanation of how FMEA can be applied to clinical operations in a radiology department to reduce, predict, or prevent errors. The six sequential steps in the FMEA process are explained, and clinical magnetic resonance imaging services are used as an example for which FMEA is particularly applicable. A modified version of traditional FMEA called Healthcare Failure Mode and Effect Analysis, which was introduced by the U.S. Department of Veterans Affairs National Center for Patient Safety, is briefly reviewed. In conclusion, FMEA is an effective and reliable method to proactively examine complex processes in the radiology department. FMEA can be used to highlight the high-risk subprocesses and allows these to be targeted to minimize the future occurrence of failures, thus improving patient safety and streamlining the efficiency of the radiology department.

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.

Objective To consummate the cleaning process of medical power tool so as to improve the quality of cleaning of medical power tool. Methods The failure mode and effect analysis (FMEA) project group was built up in April 2016. The cleaning process of medical power tool was analyzed with the method of FMEA. The intervention was carried out aiming at high risk factor. Besides, the cleaning process of medical power tool was consummated and improved continually combining the detection results of ATP bioluminescence assay. The risk assessment of the cleaning process of medical power tool was carried out to determine high risk factors and formulate as well as implement improvement measures. The control effect of risk priority number (RPN) of cleaning on medical power tool and cleaning effect were compared before (March 2016) and after (September 2016) FMEA. Results The RPN of the cleaning process of medical power tool decreased from (218.17±89.14) before implementing FMEA to (33.26±9.54) after implementing FMEA. The qualified rate of ATP bioluminescence assay in washing quality increased from 68.6% to 98.5% before and after implementation with a significant difference (χ2=22.94, P<0.01) . Conclusions The application of FMEA can effectively control the risk factors of the cleaning process of medical power tool, improve the cleaning quality of power tool and reduce the opportunity for infection in patients. Key words: Failure mode and effect analysis; Cleaning; Medical power tool; Management

One of the prominent techniques in the field of Total Quality Management (TQM) is Failure Mode and Effects Analysis (FMEA). FMEA facilitates the recording of failures and analysing them to provide solutions for preventing their recurrence. During the early periods of FMEA evolution, there were two types namely Design FMEA and Process FMEA (PFMEA) in practice. In recent days, more types such as System FMEA, Service FMEA and Maintenance FMEA are being prescribed by the researchers. Meanwhile, a number of benefits of FMEA implementation have been reported. Yet, FMEA has not found its implementation in various fields. One among them is service field. This paper reports the examination of FMEA implementation in service industry. This direction of research led to the design of an improved model, named as 'Modified service FMEA'. Its implementation was examined in an Indian State Government owned passenger Transport Company. Despite certain practical hurdles, this exercise was successful in developing modified service FMEA table and pinpointing the seriousness of failures through the portrayal of Service Lost (SL) and Cost Lost (CL).

Purpose: Failure Mode and Effects Analysis (FMEA) provides a proactive method of improving the quality and safety of treatments by identifying and correcting hazards points in the process of clinical care. FMEA, however, is often thought to be prohibitively labor intensive. This study outlines a method of streamlined FMEA, conducted with limited resources, and assesses its feasibility and effectiveness. Methods: FMEA was performed on the external beam service of a clinic treating approximately 650 patients per year on three linear accelerators. A facilitator and local team leader were identified, and a plan was developed to complete the exercise in four one‐ hour meetings as follows: 1 (core group), introduction and process mapping, 2 (all staff) identification of failure modes from expert user input, 3 (core group) scoring of failure modes according to an FMEA risk priority number, RPN, i.e. the product of severity, occurrence and detectability scores, and 4 (all staff) error proofing of the top‐five ranked failure modes. Results: Fifty‐ two failure modes were identified, 43 of which were scored and ranked. Specific interventions were developed for the five highest ranked failure modes. FMEA scoring after intervention indicated that the average RPN score for the top five modes decreased from 273 to 161 (p=0.03) while FMEA scoring of a control group of failure modes with no intervention did not show a significant change in RPN (p=0.07). The exercise was accomplished within the expected timeline and required 55 total hours of staff time and 20 hours of facilitator effort. Conclusion: Streamlined FMEA analysis is feasible with a relatively modest effort, and can reduce the risk profile of a facility. This may open the possibility of performing repeat FMEA on a regular basis.

Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory

Failure Mode and Effect Analysis: An Application in Reducing Risk in Blood Transfusion

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

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

To develop a proactive risk assessment model for human papillomavirus (HPV) genotyping testing by integrating Failure Mode and Effects Analysis (FMEA) with quality indicators (QIs), ensuring compliance with ISO 15189:2022 and improving diagnostic accuracy. A multidisciplinary team designed and performed detailed FMEA across pre-analytical, analytical, and post-analytical phases of HPV genotyping testing. To improve objectivity, we integrated Sigma metrics into the FMEA framework through a molecular diagnostics-specific model of QIs (MQI). The FMEA model systematically identified testing phases, potential failure modes, their effects, root causes, and existing controls. Risk was quantified using Severity, Occurrence (from 1-year QI data), and Detection scores (1-5 scale). Risk Priority Numbers (RPNs) were calculated (Severity×Occurrence×Detection) to prioritize failure modes, with mandatory interventions implemented for high-risk items (RPN≥40). Five high-risk failure modes (e.g., sample misidentification, data analysis errors) were identified and successfully mitigated to acceptable levels (RPN<40) through process optimization and standardization, achieving RPN reductions of 20-80 %. We established a molecular diagnostics-specific MQI, comprising 14pre-analytical, 25 analytical, and three post-analytical phase QIs. QI-based risk assessment of 35 evaluable QIs for HPV genotyping testing revealed one high-risk QI ("Incorrect results due to information system failures") and three medium-risk QIs, all of which were addressed through corrective actions. This study developed an integrated FMEA-QI model for HPV genotyping testing, establishing both a traditional FMEA framework and a molecular diagnostics-specific MQI. The combined approach improves risk assessment objectivity and enables multidimensional analysis compared to conventional methods.

Objective To explore the effect of failure mode and effect analysis (FMEA) method in strengthening the management of critical value of blood specimens in hospital. Methods According to the procedures of FMEA, critical values management team were established, drew flow chart of generating test specimens and crisis value, explored the potential failure mode and failure cause, analyzed the severity, possibility and critical value, and formulate and implement countermeasures according to the research outcome. Results The crisis value of the failure mode fell from 1 320 to 151 after the implementation of FMEA, and rate of decline was 88.56% ; number of unqualified blood samples decreased from 510 cases to 78 cases, and the difference is statistically significant (χ2=407.27, P < 0.05); critical value processing defects decreased from 85 cases (a total of 236 cases) to 3 cases (a total of 229 cases), and the difference was statistically significant (χ2=91.26, P < 0.05). Conclusions FMEA method is helpful to improve the accuracy and precision of critical value management of clinical blood samples, so as to ensure the safety of patients in hospitals. Key words: Failure mode and effect analysis; Critical value management

Failure mode and effect analysis is one of the QS-9000 quality system requirement supplements, with a wide applicability in all industrial fields. FMEA is the inductive failure analysis instruments which can be defined as a methodical group of activities intended to recognize and evaluate the potential failure modes of a product/ process and its effects with an aim to identify actions which could eliminate or reduce the chance of the potential failure before the problem occur. The purpose of this paper is to evaluate the FMEA research and application in the Thermal Power Plant Industry. The research will highlight the application of FMEA method to water tubes (WT) in boilers with an aim to find-out all the major and primary causes of boiler failure and reduce the breakdown for continuous power generation in the plant. Failure Mode and Effect Analysis technique is applied on most critical or serious parts (components) of the plant which having highest Risk Priority Number (RPN). Comparison is made between the quantitative results of FMEA and reliability field data from real tube systems. These results are discussed to establish relationships which are useful for future water tube designs.

There exist different management models in the current flood pumping station practice. The most common three types of management models include: (1) the local township office carries out the operation and the local contractor performs the maintenance, (2) the local township office carries out the operation but the county (or city) government is responsible for the commission of maintenance work and (3) the county (or city) government commissions contractors to perform both operation and maintenance. Furthermore, different pumping stations are equipped with variants of machine set. The specifications and capacities vary station to station. Therefore, the manpower arrangements and pumping frequencies is not possible fit to a standard planning. Without a unified and fair comparison basis, it would be extremely difficult to assess which management model can achieve the best performance in efficiency, cost reduction and manpower management. This thesis employs Failure Mode and Effects Analysis (FMEA) to establish an objective evaluation system, which is able to evaluate the working efficiency of flood pumping stations under different management models. This research investigates three flood pumping stations located in northern Taiwan. They are equipped with the same type of machine set, but operated in three different types of management models aforementioned. We collect the crucial causes that might lead to the pumping failure, i.e., failure mode, design the FMEA control table, and then develop a scoring system to evaluate the working efficiency of each station under different management models. The crucial causes include deterioration of material, operating procedure, equipment maintenance, equipment itself and so on. By means of the routine inspection records of each station through the most recent year as input data for the developed system, conclusions are drawn based on the analyses of these case studies. In this research we have successfully shown how to use Failure Mode and Effects Analysis (FMEA) to quantitatively evaluate the working efficiency of flood pumping stations under different management models. We also effectively differentiate working efficiency resulted from various management models through the established evaluation system. In addition, the use of FMEA control tables is helpful to discover how the failure mode occurred in different pumping stations, by which information the decision of corrective actions can be taken to prevent failure in advance.

Failure mode and effect analysis (FMEA) is a system reliability analysis technique that has been widely used in various industries to reduce the failure risk of products, systems, and services. However, traditional FMEA methods have limitations in managing the complex uncertain environment as well as the aggregation and weight allocation of FMEA attributes. Complex real-world problems usually involve multiple decision-makers. Individual perceptions and subjective factors are likely to lead to the differences in opinion, and even the conflict risk, which can ultimately become a challenge to achieve a highly recognized solution. This paper proposes an improved FMEA method to implement the risk assessment, which integrates probabilistic linguistic information and conflict risk mitigation. Probabilistic linguistic term sets (PLTSs) are used to describe the risk assessments, and a comprehensive method is applied to determine the weights of the FMEA attributes. Several new operations and distance measures related to PLTSs are defined. Then, a conflict risk mitigation model is developed to reduce the differences among decision-makers’ FMEA risk assessments. Finally, a case study on global production base selection is presented to illustrate the feasibility and effectiveness of the proposed method. Comparative analysis and discussion verify features and advantages of the method.