Abstract WP365: Reducing Door-in-Door-out Times by Applying Failure Modes Effects and Criticality Analysis

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Zahra Parnianpour ◽  
Rebeca Khorzad ◽  
Christopher Richards ◽  
William Meurer ◽  
Amy Barnard ◽  
...  
Keyword(s):  
Acute Stroke ◽  
Failure Modes ◽  
Clinical Impact ◽  
Impact Frequency ◽  
Learning Collaborative ◽  
Critical Areas ◽  
12 Steps ◽  
Time Critical ◽  
Criticality Analysis ◽  
Process Failures

Introduction: Given the time-critical nature of acute stroke, reducing door-in-door-out (DIDO) times at primary stroke centers (PSC) prior to transfer to comprehensive stroke centers (CSC) is a priority. We applied Failure Modes Effects and Criticality Analysis (FMECA) to the DIDO process at a PSC, an engineering methodology widely used in other industries, to understand the most critical areas negatively impacting DIDO time. Methods: We collected data during 2 in-person and 5 virtual Learning Collaborative (LC) meetings, enhanced by electronic surveys. The LC team consisted of 18 clinicians affiliated with 6 different healthcare systems including 3 PSCs and 3 CSCs, 2 participants from EMS agencies, and 5 patients and caregivers. The LC team created a DIDO process map with individual steps. For each step, we asked LC members to identify ways in which the process could be performed incorrectly, incompletely, skipped or delayed (failures) along with the clinical impact, their causes, frequency and existing safeguards. Each clinical impact, frequency and safeguard was scored from 1-10 (lowest to highest). Frequency, severity, and safeguards scores were multiplied to calculate a criticality score to rank the top DIDO process failures. Results: Among 61 DIDO process steps, the top 12 steps with the highest criticality score represented 40.4% of the sum of criticalities (Figure). Among these, the highest criticality scores were for: 1) Delay in the decision to obtain CTA; 2) Delay in stroke recognition by the EMS team; 3) Delay in stroke identification at triage. Conclusion: We identified opportunities to re-design the DIDO process for acute stroke. Existing safeguards for the identified “high” criticality failures rely on human factors (e.g., multiple visual inspections, provider’s experience). There is a need to develop better stroke identification tools and automatic triggers within the DIDO process to increase timely stroke transfers from PSC to CSCs.

scholarly journals Risk Assessment of the Door‐In‐Door‐Out Process at Primary Stroke Centers for Patients With Acute Stroke Requiring Transfer to Comprehensive Stroke Centers

2021 ◽  
Vol 10 (18) ◽  
Author(s):  
Jane L. Holl ◽  
Rebeca Khorzad ◽  
Rebecca Zobel ◽  
Amy Barnard ◽  
Maureen Hillman ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Risk Assessment ◽  
Acute Stroke ◽  
Failure Modes ◽  
Learning Collaborative ◽  
Vessel Occlusion ◽  
Solution Design ◽  
Comprehensive Stroke Center ◽  
Stroke Center ◽  
Criticality Analysis

Background Patients with acute stroke at non‐ or primary stroke centers (PSCs) are transferred to comprehensive stroke centers for advanced treatments that reduce disability but experience significant delays in treatment and increased adjusted mortality. This study reports the results of a proactive, systematic, risk assessment of the door‐in‐door‐out process and its application to solution design. Methods and Results A learning collaborative (clinicians, patients, and caregivers) at 2 PSCs and 3 comprehensive stroke centers in Chicago, Illinois participated in a failure modes, effects, and criticality analysis to identify steps in the process; failures of each step, underlying causes; and to characterize each failure’s frequency, impact, and safeguards using standardized scores to calculate risk priority and criticality numbers for ranking. Targets for solution design were selected among the highest‐ranked failures. The failure modes, effects, and criticality analysis process map and risk table were completed during in‐person and virtual sessions. Failure to detect severe stroke/large‐vessel occlusion on arrival at the PSC is the highest‐ranked failure and can lead to a 45‐minute door‐in‐door‐out delay caused by failure to obtain a head computed tomography and computed tomography angiogram together. Lower risk failures include communication problems and delays within the PSC team and across the PSC comprehensive stroke center and paramedic teams. Seven solution prototypes were iteratively designed and address 4 of the 10 highest‐ranked failures. Conclusions The failure modes, effects, and criticality analysis identified and characterized previously unrecognized failures of the door‐in‐door‐out process. Use of a risk‐informed approach for solution design is novel for stroke and should mitigate or eliminate the failures.

ANN-Based Relaying Algorithm for Protection of SVC- Compensated AC Transmission Line and Criticality Analysis of a Digital Relay

2020 ◽  
Vol 13 (3) ◽  
pp. 381-393
Author(s):  
Farhana Fayaz ◽  
Gobind Lal Pahuja
Keyword(s):  
Transmission Line ◽  
Failure Modes ◽  
Reactive Power ◽  
Circuit Breaker ◽  
Research Work ◽  
Back Propagation ◽  
Static Var Compensator ◽  
Data Set ◽  
Ac Transmission ◽  
Criticality Analysis

Background:The Static VAR Compensator (SVC) has the capability of improving reliability, operation and control of the transmission system thereby improving the dynamic performance of power system. SVC is a widely used shunt FACTS device, which is an important tool for the reactive power compensation in high voltage AC transmission systems. The transmission lines compensated with the SVC may experience faults and hence need a protection system against the damage caused by these faults as well as provide the uninterrupted supply of power.Methods:The research work reported in the paper is a successful attempt to reduce the time to detect faults on a SVC-compensated transmission line to less than quarter of a cycle. The relay algorithm involves two ANNs, one for detection and the other for classification of faults, including the identification of the faulted phase/phases. RMS (Root Mean Square) values of line voltages and ratios of sequence components of line currents are used as inputs to the ANNs. Extensive training and testing of the two ANNs have been carried out using the data generated by simulating an SVC-compensated transmission line in PSCAD at a signal sampling frequency of 1 kHz. Back-propagation method has been used for the training and testing. Also the criticality analysis of the existing relay and the modified relay has been done using three fault tree importance measures i.e., Fussell-Vesely (FV) Importance, Risk Achievement Worth (RAW) and Risk Reduction Worth (RRW).Results:It is found that the relay detects any type of fault occurring anywhere on the line with 100% accuracy within a short time of 4 ms. It also classifies the type of the fault and indicates the faulted phase or phases, as the case may be, with 100% accuracy within 15 ms, that is well before a circuit breaker can clear the fault. As demonstrated, fault detection and classification by the use of ANNs is reliable and accurate when a large data set is available for training. The results from the criticality analysis show that the criticality ranking varies in both the designs (existing relay and the existing modified relay) and the ranking of the improved measurement system in the modified relay changes from 2 to 4.Conclusion:A relaying algorithm is proposed for the protection of transmission line compensated with Static Var Compensator (SVC) and criticality ranking of different failure modes of a digital relay is carried out. The proposed scheme has significant advantages over more traditional relaying algorithms. It is suitable for high resistance faults and is not affected by the inception angle nor by the location of fault.

Operation and approximation based on the history of failure modes recorded by SCADA system of Amougdoul Moroccan wind farm using FMECA maintenance model

2021 ◽  
pp. 0309524X2199245
Author(s):  
Kawtar Lamhour ◽  
Abdeslam Tizliouine
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Failure Modes ◽  
Wind Farms ◽  
Scada System ◽  
History Of ◽  
Criticality Analysis ◽  
Maintenance Model ◽  
Reliability And Availability ◽  
Critical Subsystems

The wind industry is trying to find tools to accurately predict and know the reliability and availability of newly installed wind turbines. Failure modes, effects and criticality analysis (FMECA) is a technique used to determine critical subsystems, causes and consequences of wind turbines. FMECA has been widely used by manufacturers of wind turbine assemblies to analyze, evaluate and prioritize potential/known failure modes. However, its actual implementation in wind farms has some limitations. This paper aims to determine the most critical subsystems, causes and consequences of the wind turbines of the Moroccan wind farm of Amougdoul during the years 2010–2019 by applying the maintenance model (FMECA), which is an analysis of failure modes, effects and criticality based on a history of failure modes occurred by the SCADA system and proposing solutions and recommendations.

Fuzzy risk assessment for electro-optical target tracker

2016 ◽  
Vol 33 (6) ◽  
pp. 830-851 ◽  
Author(s):  
Soumen Kumar Roy ◽  
A K Sarkar ◽  
Biswajit Mahanty
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Linear Equations ◽  
Mode Analysis ◽  
Failure Behavior ◽  
Membership Functions ◽  
Design And Development ◽  
Effect Analysis ◽  
Content Type ◽  
Criticality Analysis

Purpose – The purpose of this paper is to evolve a guideline for scientists and development engineers to the failure behavior of electro-optical target tracker system (EOTTS) using fuzzy methodology leading to success of short-range homing guided missile (SRHGM) in which this critical subsystems is exploited. Design/methodology/approach – Technology index (TI) and fuzzy failure mode effect analysis (FMEA) are used to build an integrated framework to facilitate the system technology assessment and failure modes. Failure mode analysis is carried out for the system using data gathered from technical experts involved in design and realization of the EOTTS. In order to circumvent the limitations of the traditional failure mode effects and criticality analysis (FMECA), fuzzy FMCEA is adopted for the prioritization of the risks. FMEA parameters – severity, occurrence and detection are fuzzifed with suitable membership functions. These membership functions are used to define failure modes. Open source linear programming solver is used to solve linear equations. Findings – It is found that EOTTS has the highest TI among the major technologies used in the SRHGM. Fuzzy risk priority numbers (FRPN) for all important failure modes of the EOTTS are calculated and the failure modes are ranked to arrive at important monitoring points during design and development of the weapon system. Originality/value – This paper integrates the use of TI, fuzzy logic and experts’ database with FMEA toward assisting the scientists and engineers while conducting failure mode and effect analysis to prioritize failures toward taking corrective measure during the design and development of EOTTS.

Criticality Analysis for Maintenance Purposes of Platform Supply Vessels in Remote Areas

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
A. Marhaug ◽  
A. Barabadi ◽  
E. Stagrum ◽  
K. Karlsen ◽  
A. Olsen ◽  
...  
Keyword(s):  
Failure Modes ◽  
Oil And Gas Industry ◽  
Spare Parts ◽  
Acceptable Level ◽  
Component Level ◽  
Screening Process ◽  
Essential Information ◽  
Remote Areas ◽  
Critical Components ◽  
Criticality Analysis

The oil and gas industry is pushing toward new unexplored remote areas, potentially rich in resources but with limited industry presence, infrastructure, and emergency preparedness. Maintenance support is very important and challenging in such remote areas. A platform supply vessel (PSV) is an essential part of maintenance support. Hence, the acceptable level of its availability performance is high. Identification of critical components of the PSV provides essential information for optimizing maintenance management, defining a spare parts strategy, estimating competence needs for PSV operation, and achieving the acceptable level of availability performance. Currently, there are no standards or guidelines for the criticality analysis of PSVs for maintenance purposes. In this paper, a methodology for the identification of the critical components of PSVs has been developed, based on the available standard. It is a systematic screening process. The method considers functional redundancy and the consequences of loss of function as criticality criteria at the main and subfunction levels. Furthermore, at the component level, risk tools such as failure modes, effects and criticality analysis (FMECA), and fault tree analysis (FTA) will be applied in order to identify the most critical components. Moreover, the application of the proposed approach will be illustrated by a real case study.

Do methods of hospital pre-alerts influence the on-scene times for acute pre-hospital stroke patients? A retrospective observational study

2021 ◽  
Vol 6 (2) ◽  
pp. 19-25
Author(s):  
Jacob Gunn
Keyword(s):  
Standard Deviation ◽  
Observational Study ◽  
Acute Stroke ◽  
Ambulance Service ◽  
Time Saving ◽  
North East ◽  
Admission Policies ◽  
The Mean ◽  
Time Critical ◽  
Mean Time

Introduction: Stroke is one of the leading causes of death and disability worldwide. The ambulance service is often the first medical service to reach an acute stroke patient, and due to the time-critical nature of stroke, a time-critical assessment and rapid transport to a hyper acute stroke unit are essential. As stroke services have been centralised, different hospitals have implemented different pre-alert admission policies that may affect the on-scene time of the attending ambulance crew. The aim of this study is to investigate if the different pre-alert admission policies affect time on scene.Method: The current study is a retrospective quantitative observational study using data routinely collected by North East Ambulance Service NHS Foundation Trust. The time on scene was divided into two variables; group one was a telephone pre-alert in which a telephone discussion with the receiving hospital is required before they accept admission of the patient. Group two was a radio-style pre-alert in which the attending clinician makes an autonomous decision on the receiving hospital and alerts them via a short radio message of the incoming patient. These times were then compared to identify if there was any difference between them.Results: Data on 927 patients over a three-month period, from October to December 2019, who had received the full stroke bundle of care, were within the thrombolysis window and recorded as a stroke by the attending clinician, were split into the variable groups and reported on. The mean time on scene for a telephone call pre-alert was 33 minutes and 19 seconds, with a standard deviation of 13 minutes and 8 seconds. The mean on-scene time for a radio pre-alert was 28 minutes and 24 seconds, with a standard deviation of 11 minutes and 51 seconds.Conclusion: A pre-alert given via radio instead of via telephone is shown to have a mean time saving of 4 minutes and 55 seconds, representing an important decrease in time which could be beneficial to patients.

Failure mode, effects and criticality analysis improvement by using new criticality assessment and prioritization based approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ammar Chakhrit ◽  
Mohammed Chennoufi
Keyword(s):  
Failure Mode ◽  
Conventional Method ◽  
Fuzzy Inference System ◽  
Risk Evaluation ◽  
Failure Modes ◽  
Fuzzy Inference ◽  
Decision Makers ◽  
Inference System ◽  
Content Type ◽  
Criticality Analysis

Purpose This paper aims to enable the analysts of reliability and safety system to assess the criticality and prioritize failure modes perfectly to prefer actions for controlling the risks of undesirable scenarios. Design/methodology/approach To resolve the challenge of uncertainty and ambiguous related to the parameters, frequency, non-detection and severity considered in the traditional approach failure mode effect and criticality analysis (FMECA) for risk evaluation, the authors used fuzzy logic where these parameters are shown as members of a fuzzy set, which fuzzified by using appropriate membership functions. The adaptive neuro-fuzzy inference system process is suggested as a dynamic, intelligently chosen model to ameliorate and validate the results obtained by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. A new hybrid model is proposed that combines the grey relational approach and fuzzy analytic hierarchy process to improve the exploitation of the FMECA conventional method. Findings This research project aims to reflect the real case study of the gas turbine system. Using this analysis allows evaluating the criticality effectively and provides an alternate prioritizing to that obtained by the conventional method. The obtained results show that the integration of two multi-criteria decision methods and incorporating their results enable to instill confidence in decision-makers regarding the criticality prioritizations of failure modes and the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection and frequency, which are considered to have equal importance in the traditional method. Originality/value This paper is providing encouraging results regarding the risk evaluation and prioritizing failures mode and decision-makers guidance to refine the relevance of decision-making to reduce the probability of occurrence and the severity of the undesirable scenarios with handling different forms of ambiguity, uncertainty and divergent judgments of experts.

Uncertainty Analysis in Quantitative Risk Assessment

1996 ◽  
Vol 118 (1) ◽  
pp. 121-124 ◽  
Author(s):  
S. Quin ◽  
G. E. O. Widera
Keyword(s):  
Risk Assessment ◽  
Uncertainty Analysis ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Failure Modes ◽  
The Other ◽  
Quantitative Risk Assessment ◽  
Engineering Practice ◽  
Criticality Analysis

Of the quantitative approaches applied to inservice inspection, failure modes, effects,criticality analysis (FMECA) methodology is recommended. FMECA can provide a straightforward illustration of how risk can be used to prioritize components for inspection (ASME, 1991). But, at present, it has two limitations. One is that it cannot be used in the situation where components have multiple failure modes. The other is that it cannot be used in the situation where the uncertainties in the data of components have nonuniform distributions. In engineering practice, these two situations exist in many cases. In this paper, two methods based on fuzzy set theory are presented to treat these problems. The methods proposed here can be considered as a supplement to FMECA, thus extending its range of applicability.

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