Risk assessment of green infrastructure using failure mode and effect analysis: a case study of green wall projects across the project lifecycle

A Method for Evaluating Quality Assurance Needs in Radiation Therapy

Targeting specimen misprocessing safety events with failure modes and effects analysis.

The Improvement of a Novel Radiation Therapy Workflow by Failure Mode and Effects Analysis

Fuzzy Inference Based FMEA for MR Image Based HDR Brachytherapy

Risk Management: It's Not Just FMEA

Overview of Failure Mode and Effects Analysis (FMEA): A Patient Safety Tool.

Though failure mode and effects analysis (FMEA) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge, its output has never been validated against data on errors that actually occur. The objective of this study was to perform FMEA of a stereotactic body radiation therapy (SBRT) treatment planning process and validate the results against data recorded within an incident learning system. FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, dosimetrists, and IT technologists. Potential failure modes were identified through a systematic review of the process map. Failure modes were rated for severity, occurrence, and detectability on a scale of one to ten and risk priority number (RPN) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that has been active for two and a half years. Differences between FMEA anticipated failure modes and existing incidents were identified. FMEA identified 63 failure modes. RPN values for the top 25% of failure modes ranged from 60 to 336. Analysis of the incident learning database identified 33 reported near-miss events related to SBRT planning. Combining both methods yielded a total of 76 possible process failures, of which 13 (17%) were missed by FMEA while 43 (57%) identified by FMEA only. When scored for RPN, the 13 events missed by FMEA ranked within the lower half of all failure modes and exhibited significantly lower severity relative to those identified by FMEA (p = 0.02). FMEA, though valuable, is subject to certain limitations. In this study, FMEA failed to identify 17% of actual failure modes, though these were of lower risk. Similarly, an incident learning system alone fails to identify a large number of potentially high-severity process errors. Using FMEA in combination with incident learning may render an improved overview of risks within a process.

Clustering and visualization of failure modes using an evolving tree

Purpose:Though FMEA (Failure Mode and Effects Analysis) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge it has never been validated against actual incident learning data. The objective of this study was to perform an FMEA analysis of an SBRT (Stereotactic Body Radiation Therapy) treatment planning process and validate this against data recorded within an incident learning system.Methods:FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, and dosimetrists. Potential failure modes were identified through a systematic review of the workflow process. Failure modes were rated for severity, occurrence, and detectability on a scale of 1 to 10 and RPN (Risk Priority Number) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that had been active for two years. Differences were identified.Results:FMEA identified 63 failure modes. RPN values for the top 25% of failure modes ranged from 60 to 336. Analysis of the incident learning database identified 33 reported near‐miss events related to SBRT planning. FMEA failed to anticipate 13 of these events, among which 3 were registered with severity ratings of severe or critical in the incident learning system. Combining both methods yielded a total of 76 failure modes, and when scored for RPN the 13 events missed by FMEA ranked within the middle half of all failure modes.Conclusion:FMEA, though valuable, is subject to certain limitations, among them the limited ability to anticipate all potential errors for a given process. This FMEA exercise failed to identify a significant number of possible errors (17%). Integration of FMEA with retrospective incident data may be able to render an improved overview of risks within a process.

Surface image guided, Linac-based radiosurgery (SIG-RS) is a modern approach for delivering radiosurgery that utilizes optical stereoscopic imaging to monitor the surface of the patient during treatment in lieu of using a head frame for patient immobilization. Considering the novelty of the SIG-RS approach and the severity of errors associated with delivery of large doses per fraction, a risk assessment should be conducted to identify potential hazards, determine their causes, and formulate mitigation strategies. The purpose of this work is to investigate SIG-RS using the combined application of failure modes and effects analysis (FMEA) and fault tree analysis (FTA), report on the effort required to complete the analysis, and evaluate the use of FTA in conjunction with FMEA. A multidisciplinary team was assembled to conduct the FMEA on the SIG-RS process. A process map detailing the steps of the SIG-RS was created to guide the FMEA. Failure modes were determined for each step in the SIG-RS process, and risk priority numbers (RPNs) were estimated for each failure mode to facilitate risk stratification. The failure modes were ranked by RPN, and FTA was used to determine the root factors contributing to the riskiest failure modes. Using the FTA, mitigation strategies were formulated to address the root factors and reduce the risk of the process. The RPNs were re-estimated based on the mitigation strategies to determine the margin of risk reduction. The FMEA and FTAs for the top two failure modes required an effort of 36 person-hours (30 person-hours for the FMEA and 6 person-hours for two FTAs). The SIG-RS process consisted of 13 major subprocesses and 91 steps, which amounted to 167 failure modes. Of the 91 steps, 16 were directly related to surface imaging. Twenty-five failure modes resulted in a RPN of 100 or greater. Only one of these top 25 failure modes was specific to surface imaging. The riskiest surface imaging failure mode had an overall RPN-rank of eighth. Mitigation strategies for the top failure mode decreased the RPN from 288 to 72. Based on the FMEA performed in this work, the use of surface imaging for monitoring intrafraction position in Linac-based stereotactic radiosurgery (SRS) did not greatly increase the risk of the Linac-based SRS process. In some cases, SIG helped to reduce the risk of Linac-based RS. The FMEA was augmented by the use of FTA since it divided the failure modes into their fundamental components, which simplified the task of developing mitigation strategies.

Purpose: Failure modes and effects analysis (FMEA) is a subjective risk mitigation technique that has not been applied to physics-specific quality management practices. There is a need for quantitative FMEA data as called for in the literature. This work focuses specifically on quantifying FMEA severity scores for physics components of IMRT delivery and comparing to subjective scores. Methods: Eleven physical failure modes (FMs) for head and neck IMRT dose calculation and delivery are examined near commonly accepted tolerance criteria levels. Phantom treatment planning studies and dosimetry measurements (requiring decommissioning in several cases) are performed to determine the magnitude of dose delivery errors for the FMs (i.e., severity of the FM). Resultant quantitative severity scores are compared to FMEA scores obtained through an international survey and focus group studies. Results: Physical measurements for six FMs have resulted in significant PTV dose errors up to 4.3% as well as close to 1 mm significant distance-to-agreement error between PTV and OAR. Of the 129 survey responses, the vast majority of the responders used Varian machines with Pinnacle and Eclipse planning systems. The average years of experience was 17, yet familiarity with FMEA less than expected. Survey reports perception of dose delivery error magnitudemore » varies widely, in some cases 50% difference in dose delivery error expected amongst respondents. Substantial variance is also seen for all FMs in occurrence, detectability, and severity scores assigned with average variance values of 5.5, 4.6, and 2.2, respectively. Survey shows for MLC positional FM(2mm) average of 7.6% dose error expected (range 0–50%) compared to 2% error seen in measurement. Analysis of ranking in survey, treatment planning studies, and quantitative value comparison will be presented. Conclusion: Resultant quantitative severity scores will expand the utility of FMEA for radiotherapy and verify accuracy of FMEA results compared to highly variable subjective scores.« less

Goal: In this work, the application of FMEA (failure mode and effects analysis) in the execution of maintenance of water network in a medium size city in the state of Maranhão was approached. FMEA (failure mode and effects analysis) with occupational safety approach is applied in the execution of water supply network maintenance in a medium size city in the state of Maranhão. This work shows that FMEA is an effective tool for risk prioritization in work process. The FMEA application with use of index reference table can become faster than application without reference table - and thus spread the tool for risk analysis.
 Design / Methodology / Approach: A photographic records was carried out in loco and the main risks to the workers were enumerated and based on the obtained data, a risk analysis was elaborated with the application of FMEA (Failure modes and effects analysis).
 Results: The maintenance services teams of the supply system are subject to a high risk of accidents caused mainly by the working conditions precariousness. It was observed that the services of manual excavations and the lack of use of PPE (helmet, gloves, pants suitable for flooded environments and etc.) have high risk index (RPN) and that the mismanagement added to the unsafe behavior were the main factors to accident occurrence in this type of work.
 Limitations of the investigation: The study studied the reality found in a medium-sized city with precarious working conditions. Further studies can compare the work reality of other teams in cities of different sizes, with better working conditions. Other limitation of this work is the impossibility of work situation improvement and posterior tool application – a study with this magnitude is non-trivial and needs future research.
 Practical implications: The great achievement of this work is to demonstrate that FMEA - a tool that is widely used in maintenance management and product engineering - is able to identify and prioritize risks based on its preliminary risk index obtained - contributing to reduce the difficulty of index choices previously cited in literature and disseminate the FMEA utilization for employee safety and occupational health. Such a tool has a great capacity for quantitative description of the risks, showing that the FMEA is very useful in the work safety sector for the organization of correction plans.
 Originality / Value: The difficulties of using the tool mentioned in the available literature were minimized with the use of the reference table, showing that the use of FMEA can become faster and thus spread the tool for risk analysis.

PurposeThe purpose of this paper is to propose an integrative approach for improving failure modes and effects analysis (FMEA).Design/methodology/approachAn extensive literature review on FMEA has been performed. Then, an integrative approach has been proposed based on literature review. The proposed approach is an integration of FMEA and quality function deployment (QFD). The proposed approach includes a two-phase QFD. In the first phase, failure modes are prioritized based on failure effects and in the second phase, failure causes are prioritized based on failure modes. The proposed approach has been examined in a case example at the blast furnace operation of a steel-manufacturing company.FindingsResults of the case example indicated that stove shell crack in hot blast blower, pump failure in cooling water supply pump and bleeder valves failed to operate are the first three important failure modes. In addition, fire and explosion are the most important failure effects. Also, improper maintenance, over pressure and excess temperature are the most important failure causes. Findings also indicated that the proposed approach with the consideration of interrelationships among failure effects, failure mode and failure causes can influence and adjust risk priority number (RPN) in FMEA.Research limitations/implicationsAs manufacturing departments are mostly dealing with failure effects and modes of machinery and maintenance departments are mostly dealing with causes of failures, the proposed model can support better coordination and integration between the two departments. Such support seems to be more important in firms with continuous production lines wherein line interruption influences response to customers more seriously. A wide range of future study opportunities indicates the attractiveness and contribution of the subject to the knowledge of FMEA.Originality/valueAlthough the literature indicates that in most of studies the outcomes of QFD were entered into FMEA and in some studies the RPN of FMEA was entered into QFD as importance rating, the proposed approach is a true type of the so-called “integration of FMEA and QFD” because the three main elements of FMEA formed the structure of QFD. In other words, the proposed approach can be considered as an innovation in the FMEA structure, not as a data provider prior to it or a data receiver after it.

Can traceability of gametes and embryos be ensured during IVF? The use of a simple and comprehensive traceability system that includes the most susceptible phases during the IVF process minimizes the risk of mismatches. Mismatches in IVF are very rare but unfortunately possible with dramatic consequences for both patients and health care professionals. Traceability is thus a fundamental aspect of the treatment. A clear process of patient and cell identification involving witnessing protocols has to be in place in every unit. To identify potential failures in the traceability process and to develop strategies to mitigate the risk of mismatches, previously failure mode and effects analysis (FMEA) has been used effectively. The FMEA approach is however a subjective analysis, strictly related to specific protocols and thus the results are not always widely applicable. To reduce subjectivity and to obtain a widespread comprehensive protocol of traceability, a multicentre centrally coordinated FMEA was performed. Seven representative Italian centres (three public and four private) were selected. The study had a duration of 21 months (from April 2015 to December 2016) and was centrally coordinated by a team of experts: a risk analysis specialist, an expert embryologist and a specialist in human factor. Principal investigators of each centre were first instructed about proactive risk assessment and FMEA methodology. A multidisciplinary team to perform the FMEA analysis was then formed in each centre. After mapping the traceability process, each team identified the possible causes of mistakes in their protocol. A risk priority number (RPN) for each identified potential failure mode was calculated. The results of the FMEA analyses were centrally investigated and consistent corrective measures suggested. The teams performed new FMEA analyses after the recommended implementations. In each centre, this study involved: the laboratory director, the Quality Control & Quality Assurance responsible, Embryologist(s), Gynaecologist(s), Nurse(s) and Administration. The FMEA analyses were performed according to the Joint Commission International. The FMEA teams identified seven main process phases: oocyte collection, sperm collection, gamete processing, insemination, embryo culture, embryo transfer and gamete/embryo cryopreservation. A mean of 19.3 (SD ± 5.8) associated process steps and 41.9 (SD ± 12.4) possible failure modes were recognized per centre. A RPN ≥15 was calculated in a mean of 6.4 steps (range 2-12, SD ± 3.60). A total of 293 failure modes were centrally analysed 45 of which were considered at medium/high risk. After consistent corrective measures implementation and re-evaluation, a significant reduction in the RPNs in all centres (RPN <15 for all steps) was observed. A simple and comprehensive traceability system was designed as the result of the seven FMEA analyses. The validity of FMEA is in general questionable due to the subjectivity of the judgments. The design of this study has however minimized this risk by introducing external experts for the analysis of the FMEA results. Specific situations such as sperm/oocyte donation, import/export and pre-implantation genetic testing were not taken into consideration. Finally, this study is only limited to the analysis of failure modes that may lead to mismatches, other possible procedural mistakes are not accounted for. Every single IVF centre should have a clear and reliable protocol for identification of patients and traceability of cells during manipulation. The results of this study can support IVF groups in better recognizing critical steps in their protocols, understanding identification and witnessing process, and in turn enhancing safety by introducing validated corrective measures. This study was designed by the Italian Society of Embryology Reproduction and Research (SIERR) and funded by the Italian National Transplant Centre (CNT) of the Italian National Institute of Health (ISS). The authors have no conflicts of interest. N/A.

Purpose:Failure Modes and Effects Analysis (FMEA) techniques have been used to analyze surface image guided radiosurgery (SIG‐RS). Hazard model, a modified FMEA approach developed by the Dutch, is applied to SIG‐RS risk assessment and evaluated against the AAPM's FMEA approach.Methods:The SAFER approach uses a risk inventory matrix to categorize hazards (rather than probabilities). A multidisciplinary team was assembled to create the process map of SIG‐RS and 91 steps and 167 failure modes were determined. Each failure mode was categorized for frequency (weekly, monthly, quarterly, yearly and less than once a year) and severity (negligible, minor, moderate, major and catastrophic) according to the SAFER procedures. All failure modes are placed in the matrix of arbitrary risk score matrix: very high, high, low, and very low. The top 14 high risk failure modes from the Result of FMEA and SAFER analysis were compared.Results:167 failure modes categorized in the risk inventory matrix with 1 very high, 13 high, 66 low and 87 very low. Comparison of top 14 high risk failure modes between two techniques shows 9 common failure modes and 5 isolated failure modes. Two failure modes (FM: 58, 145) with the highest risk priority number (both RPN=288) in FMEA are also ranked as high risk in SAFER analysis. However one failure mode (FM: 154) with very high risk score in SAFER is not recognized by FMEA analysis due to its low “lack of detectability” score.Conclusion:FMEA is a well‐established technique for prospective risk analysis. SAFER is a practical alternative that is easy to implement with a reliable category structure. Also the risk inventory matrix is conceptually straightforward to obtain agreement among multidisciplinary team members but still demonstrates a full scale of criticality.