Artificial Intelligence Assisted Social Failure Mode and Effect Analysis (FMEA) for Sustainable Product Design

Nowadays, the social dimension of product sustainability is increasingly in demand, however, industrial designers struggle to pursue it much more than the environmental or economic one due to their unfamiliarity in correlating design choices with social impacts. In addition, this gap is not filled even by the supporting methods that have been conceived to only support specific areas of application. To fill this gap, this study proposed a method to support social failure mode and effect analysis (SFMEA), though the automatic failure determination, based on the use of a chatbot (i.e., an artificial intelligence (AI)-based chat). The method consists of 84 specific questions to ask the chatbot, resulting from the combination of known failures and social failures, elements from design theories, and syntactic structures. The starting hypothesis to be verified is that a GPT Chat (i.e., a common AI-based chat), properly queried, can provide all the main elements for the automatic compilation of a SFMEA (i.e., to determine the social failures). To do this, the proposed questions were tested in three case studies to extract all the failures and elements that express predefined SFMEA scenarios: a coffee cup provoking gender discrimination, a COVID mask denying a human right, and a thermometer undermining the cultural heritage of a community. The obtained results confirmed the starting hypothesis by showing the strengths and weaknesses of the obtained answers in relation to the following factors: the number and type of inputs (i.e., the failures) provided in the questions;the lexicon used in the question, favoring the use of technical terms derived from design theories and social sustainability taxonomies;the type of the problem. Through this test, the proposed method proved its ability to support the social sustainable design of different products and in different ways. However, a dutiful recommendation instead concerns the tool (i.e., the chatbot) due to its filters that limit some answers in which the designer tries to voluntarily hypothesize failures to explore their social consequences.

Risk management in humanitarian supply chain based on FMEA and grey relational analysis.

The COVID-19 pandemic has disrupted the humanitarian supply chain management (HSCM) necessary for delivering emergency items during the disaster. The combined effects of climate change and the pandemic uncover the vulnerabilities of humanitarian supply chain operations and highlight the importance of risk management. This study aimed to identify priority risk factors and proposed mitigating risk strategies of a local government that is at the forefront of relief operations. It used Grey Relational Analysis (GRA) method to validate the Failure Mode and Effect Analysis (FMEA) approach in identifying priority issues relating to the supply chain risks. This paper reveals that the results of FMEA and GRA are almost similar.

Application of Fmea in the Context of Organizations Providing Integral Services of Local and Regional Interest in Romania

Failure Mode and Effects Analysis, FMEA, is a methodology frequently used in the commercial sector to investigate the numerous causes and repercussions that could result from a failure (defects that cause the object to lose its ability to perform functions). In this research paper, we will investigate why it is useful, as well as how this strategy could be used in the context of integrated services of local and regional importance. Organizations that provide services of local and regional importance will benefit if they can predict potential problems and failures of the management infrastructure. A process FMEA study shows discrepancies that impact product safety and quality.

A Lean Six Sigma case study to improve the manufacturing process affected during COVID-19

The key manufacturing industry was badly affected by the COVID-19 in India. In this study, we found that the product demand is dynamic during COVID-19. We selected one of the electrical OEMs in India to execute the value added-Flow analysis and VSM study which showed 96% and 85% of total delivery lead time is contributed by NVA activities at the manufacturing process respectively. We also plotted the spaghetti diagram and analysed that total product movement is 287 metres in the current state with the complex flow. We did total of six main Kaizens after Ishikawa and FMEA. We constructed single-piece flow with saving of the half shop floor space and total product movement was reduced from 287 to 96 metres, while total delivery lead time was reduced from 14.6 to 7.72 days. We concluded that lean Six Sigma deployment in the manufacturing industry solved the problems of demand fluctuations.

Healthcare waste hazards assessment using EWGM-FMEA: Case study in Oman

In the last years, particularly after Covid-19, Health care waste (HCW) has increased significantly due to the increasing population and number of healthcare organizations. HCW produces a significant risk of infectious contamination and injury. Accordingly, healthcare waste management plays a vital role in creating waste management strategies, and policies and implementing waste management plans. To build robust healthcare management systems, the risk assessment process is a key step. This paper assesses the top hazards of healthcare waste at Sultan Qaboos University Hospital (SQUH) in Oman using the Exponential Weighted Geometric Mean-Failure Mode and Effect Analysis (EWGM-FMEA). Fifteen healthcare waste hazards were selected to apply the tool. These hazards are ranked to prioritize the top hazards wastes. This assessment helps in identifying the most crucial hazards,whiche the policymakers should pay attention thus, the main countermeasures could be conducted. These hazards were proposed based on the conducted survey questionnaire and interviews accordingly, and analyses of the data have been carried out. The applied tool examined the importance of quantifying healthcare waste to apply the appropriate corrective actions which can be applied to mitigate the harm and the negative effects of healthcare waste. The results of the assessment tool will help policymakers in developing clear plans for management, disposal of wastes, and segregation. Furthermore, prioritizing healthcare waste explored the importance of integrating tthe raining plans of workers with the healthcare waste management policy. Although the prospective managerial and policy implications of this research, some limitations could be studied by future researchers. Firstly, the sample covered one hospital that may be representative of only one hospital in Oman which constrains the generalization of results. Secondly, the number of identified waste hazards is fifteen so, increased the number of hazards may help policymakers in building a more effective healthcare waste management plan which will reflect in improving the healthcare management system in the organization, mitigating the harmful effects on human health and the negative effects on the environment. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Logistics System Risk Analysis Using FMEA-AHP Methodology: During and after the COVID-19 pandemic

The COVID-19 pandemic broke out, and the global logistics industry suffered severe losses, therefore, the FMEA-AHP (Failure Mode and Effects Analysis-Analytic Hierarchy Process) method is proposed to analyze the failure reasons of the logistics system in the COVID-19 pandemic. In this article, we have made an improvement on the basis of the traditional FMEA method: The AHP is integrated into the FMEA algorithm (referred to as RPWN (risk priority weighted number) in this article). In this algorithm, the AHP is to determine the weights of risk indicators. Meanwhile, in this article, we also consider about the new logistics failures, such as the failure modes and failure reasons of the logistics system under the COVID-19 pandemic. 12 failures have been identified, and corresponding preventive and corrective measures have been suggested to cut off the path of failure propagation and reduce the impact of failures. © 2022 ACM.

A novel approach to infectious disease control and radiotherapy risk management.

BACKGROUND: Infectious disease outbreaks have always presented challenges to the operation of healthcare systems. In particular, the treatment of cancer patients within Radiation Oncology often cannot be delayed or compromised due to infection control measures. Therefore, there is a need for a strategic approach to simultaneously managing infection control and radiotherapy risks. PURPOSE: To develop a systematic risk management method that uses mathematical models to design mitigation efforts for control of an infectious disease outbreak, while ensuring safe delivery of radiotherapy. METHODS: A two-stage failure mode and effect analysis (FMEA) approach is proposed to modify radiotherapy workflow during an infectious disease outbreak. In stage 1, an Infection Control FMEA (IC-FMEA) is conducted, where risks are evaluated based on environmental parameters, clinical interactions, and modeling of infection risk. occupancy risk index (ORI) is defined as a metric for infection transmission risk level in each room, based on the degree of occupancy. ORI, in combination with ventilation rate per person (Rp ), is used to provide a broad infection risk assessment of workspaces. For detailed IC-FMEA of clinical processes, infection control failure mode (ICFM) is defined to be any instance of disease transmission within the clinic. Infection risk priority number (IRPN) has been formulated as a function of time, distance, and degree of protective measures. Infection control measures are then systematically integrated into the workflow. Since the workflow is perturbed by infection control measures, there is a possibility of introducing new radiotherapy failure modes or increased likelihood of existing failure modes. Therefore, in stage 2, a conventional radiotherapy FMEA (RT-FMEA) should be performed on the adjusted workflow. RESULTS: The COVID-19 pandemic was used to illustrate stage 1 IC-FMEA. ORI and Rp values were calculated for various workspaces within a clinic. A deep inspiration breath hold (DIBH) CT simulation was used as an example to demonstrate detailed IC-FMEA with ICFM identification and IRPN evaluation. A total of 90 ICFMs were identified in the DIBH simulation process. The calculated IRPN values were found to be progressively decreasing for workflows with minimal, moderate, and enhanced levels of protective measures. CONCLUSION: The framework developed in this work provides tools for radiotherapy clinics to systematically assess risk and adjust workflows during the evolving circumstances of any infectious disease outbreak.

Cystic fibrosis learning network telehealth innovation lab during the COVID-19 pandemic: a success QI story for interdisciplinary care and agenda setting.

INTRODUCTION: The Cystic Fibrosis Foundation chronic care guidelines recommend monitoring clinical status of a patient with cystic fibrosis (CF) through quarterly interdisciplinary visits. At the beginning of the COVID-19 pandemic, the Cystic Fibrosis Learning Network (CFLN) designed and initiated a telehealth (TH) innovation lab (TH ILab) to support transition from the classic CF care model of quarterly in-person office visits to a care model that included TH. AIM: The specific aims of the TH ILab were to increase the percentage of virtual visits with interdisciplinary care (IDC) from 60% to 85% and increase the percentage of virtual visits in which patients and families participated in shared agenda setting (AS) from 52% to 85% by 31 December 2020. METHODS: The model for improvement methodology was used to determine the ILab aims, theory, interventions and measures. In the testing phase of the ILab, data related to process and outcome measures as well as learnings from plan-do-study-act cycles were collected, analysed and shared weekly with the TH ILab teams. Participating centres created processes for IDC and AS for TH visits and developed and shared quality improvement tools specific to their local context with other centres during the ILab weekly meetings and via a secure CFLN-maintained platform. RESULTS: Both specific aims were achieved ahead of the expected target date. By August 2020, 85% of the TH ILab visits provided IDC and 92% of patients were seen for CF care by teams from the TH ILab that participated in AS. CONCLUSION: Shared learning through a collaborative, data-driven process in the CFLN TH ILab rapidly led to standardised TH IDC and AS, which achieved reliable and sustainable processes which could be reproduced by other networks.

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.

Evaluating the impacts of COVID-19 outbreak on supply chain risks by modified failure mode and effects analysis: a case study in an automotive company.

Supply chains have been facing many disruptions due to natural and man-made disasters. Recently, the global pandemic caused by COVID-19 outbreak, has severely hit trade and investment worldwide. Companies around the world faced significant disruption in their supply chains. This study aims to explore the impacts of COVID-19 outbreak on supply chain risks (SCRs). Based on a comprehensive literature review on supply chain risk management, 70 risks are identified and listed in 7 categories including demand, supply, logistics, political, manufacturing, financial and information. Then, a modified failure mode and effects analysis (FMEA) is proposed to assess the identified SCRs, which integrates FMEA and best-worst method to provide a double effectiveness. The results demonstrate the efficiency of the proposed method, and according to the main findings, "insufficient information about demand quantities", "shortages on supply markets", "bullwhip effect", "loss of key suppliers", "transportation breakdowns", "suppliers", "on-time delivery", "government restrictions", "suppliers' temporary closure", "market demand change" and "single supply sourcing" are the top 10 SCRs during the COVID-19 outbreak, respectively. Finally, the practical implications are discussed and useful managerial insights are recommended.

Development and Simulation of Cyberdisaster Situation Awareness Models

Cyberdisasters require an organization’s disaster team to be prepared. Disaster events are difficult to predict, but the impact of this risk on an organization is large. However, organizations sometimes struggle in being prepared for disaster situations. Here, awareness of disaster situations when analysing priority disasters (e.g., earthquakes and pandemics) and how to mitigate them can help an organization’s preparedness. Mitigation scenarios need to be determined and simulated so that a disaster team is ready to face disaster. Using Endsley’s situational awareness model and a tabletop exercise, this study aimed to help a disaster team determine cyberdisaster risk priority and assess a team’s preparedness for dealing with a cyberdisaster. The situation awareness model was divided into two stages: awareness of cyberdisaster situations and tabletop evaluations. Awareness of a disaster situation was carried out by determining the highest priority for disaster risk using the fuzzy failure modes and effects analysis (FMEA) method. The results of the first study show that the high-risk category contains ransomware attacks during pandemics and earthquakes. The second study performed a tabletop simulation questionnaire survey of earthquakes and ransomware attacks during a pandemic for several disaster teams with 152 respondents. The results of the survey evaluation of the earthquakes and ransomware attacks simulation survey show that the effect factors of cyberdisaster simulation decisions are 95% system capability (p < 0.05), 90% knowledge (p < 0.05), and 90% awareness of a disaster situation (p < 0.05);these factors show the effect of a disaster team’s decision during a tabletop simulation. The novelty of this research lies in building a model for how an organizational process determines the priority of a cyberdisaster tabletop simulation and the factors that contribute to increasing a disaster team’s awareness in dealing with cyberattacks.

Service design touch point failure model and effect analysis based on the triangular fuzzy number evaluation method

How to improve employees' satisfaction in the remote office mode while working together has become a challenge for enterprises to deal with the new office management mode. The concept of service design touch point is introduced into the remote office management system to improve employees' office experience and help enterprises complete the remote office service design. From the perspective of virtuous circle, the cost of failure analysis and preventive measures for service design touch point in the process of service design is lower than that of dealing with failure after service failure. Therefore, this paper made a risk assessment on the failure mode of service design touch point loss analysis method based on the triangular fuzzy number evaluation method. In the failure risk assessment, the fuzzy failure mode and effect analysis theory and the failure mode of service design touch point are analyzed first, and the expert scoring method is used to evaluate and determine the fuzzy level of severity, occurrence and detection of each failure mode;Then, aiming at the ability difference of different experts in evaluating different objects, the expert importance matrix, which is based on attribute difference, is determined;On this basis, the risk priority value of each failure mode is analyzed and determined by fuzzy calculation method. The risk evaluation ranking results of touch point failure modes are obtained according to the risk priority value. Finally, taking the failure analysis of Y company's remote collaborative office service as an example, the feasibility of the method is verified by the case of protection during COVID-19.

A Proposed Framework for Developing FMEA Method Using Pythagorean Fuzzy CODAS

The purpose of this research article is to develop a hybridization between the Failure Mode and Effect Analysis (FMEA) method and the Combinative Distance-Based Assessment (CODAS) method under Pythagorean Fuzzy environment. The traditional FMEA procedure is based on the multiplication between the parameters of severity, occurrence, and detectability where everyone has equal relative importance;therefore, different combinations of these parameters can generate the same result creating uncertainty in the analysis. In this mode, the hybridization proposed in this research deal with relative importance of each parameter;in the fact to have a more suitable combination which consider the level of knowledge of the experts in the assessment. Finally, a numerical case was carried out concerning the public transportation service to validate our proposal;the results show that 31 failure modes and potential risks can be evaluated using user perceptions, a dominant with high level of knowledge about the public transportation service and an apprentice or common user, as team of experts and exploiting the subjectivity of the information in a mathematical model. Also, we compare the results with a variation of the proposed model with the multi-criteria method multi-objective optimization method by relationship analysis (MOORA);it was observed that the convergence of the failure modes depends on the nature of the mathematical model even under the same conditions at the start.

Holistic risk assessment of container shipping service based on Bayesian Network Modelling

Container shipping makes significant contribution to the global economy and is confronted with various hazards and risks especially during the COVID-19 pandemic. These risks can disrupt resilient container shipping service, leading to further deterioration of the global economy. Hence, it is vital to develop resilient container shipping service, which is associated with being on-time, safe, and hassle-free. Theoretically, this research identifies 28 root risks using the PESTLE framework, conducts risk assessment using a hybrid method comprising failure modes and effects analysis, evidential reasoning, and rule-based Bayesian network. A three-hierarchy Bayesian network model is established. The results reveal that economic, political, and technical risks are the most threatening risks affecting resilient container shipping service. Moreover, the holistic container shipping risk is most sensitive to environmental risks. Managerially, this research provides container shipping companies with guidance of drafting risk mitigation plans with economic risks and political risks as priorities.

Íç Denetim Süreçlerini Íyileştirme ve Risk Yönetimi Araçları: Yalın Altı Sigma ve FMEA

Íşletmelerin yapısında var olan karmaşıklık, 2019 yılı itibariyle dünyayı etkisi altına alan COVID-19 salgını ile daha da karmaşık hale gelmiştir. Íşletmelerin iş yapış şekillerini değiştirmesi ve salgından korunmak adına alınan önlemler kapsamında, faaliyetlerin uzaktan yürütülmesi birçok riski de beraberinde getirmiştir. Her geçen gün artan risklerle birlikte, iç denetimin etkin şekilde yürütülmesinin önemi artmış ve yeni yaklaşımların kullanılması zorunlu hale gelmiştir. Bu çalışmada, iç denetimde süreçlerin iyileştirilmesi ve risklerin belirlenmesi aşamalarında kullanılmak üzere, risk yönetim aracı olarak Yalın Altı Sigma kapsamında sunulan Tanımla-Ölçme-Analiz Etme-Íyileştirme-Kontrol (Define-Measure- Analyze-Improvement-Control/DMAIC ) modeli ve bu model içerisine gömülü olan Hata Türü ve Etkileri Analizi (Failure Mode and Effects Analysis-FMEA) aracı olmak üzere iki yaklaşım önerilmiştir. Çalışmada, iç denetimin Yalın Altı Sigma-DMAIC modeli ve Hata Türü ve Etkileri Analizi ile olan ilişkisi incelenmiştir. Süreçlerin iyileştirilmesinde kullanılan birçok yaklaşım olmakla birlikte, çalışma kapsamında önerilen bu iki yaklaşım, özellikle iş süreçlerinin verimliliğine, etkililiğine ve tutumluluğuna odaklanan iç denetimin, riskleri değerlendirmesi, süreçteki zayıflıkları belirlemesi ve iyileştirilmesi için tavsiyelerde bulunmaktadır.Alternate : In addition to the complexity inherent in businesses, the COVID-19 pandemic that affected the world as of 2019 has made this even more complex. Changing the way businesses do business and conducting activities remotely within the scope of the measures taken to protect against the epidemic has brought many risks. With the increasing risks day by day, the importance of conducting internal audit has increased and it has become necessary to use new approaches. In this study, two approaches are proposed as a risk management tool, the Lean Six Sigma-DMAIC model and the Failure Mode and Effects Analysis (FMEA) tool embedded in the DMAIC model, to be used in the stages of improving processes and identifying risks in internal auditing. In the study, the relationship between internal audit and FMEA and Six Sigma was examined. Although there are many approaches used in the improvement of processes, these two approaches suggested within the scope of the study, especially focusing on the efficiency, effectiveness and frugality of business processes, make recommendations for internal audit to assess risks, identify weaknesses in the process and improve them.

Systems engineering analysis of diagnostic referral closed-loop processes.

BACKGROUND: Closing loops to complete diagnostic referrals remains a significant patient safety problem in most health systems, with 65%-73% failure rates and significant delays common despite years of improvement efforts, suggesting new approaches may be useful. Systems engineering (SE) methods increasingly are advocated in healthcare for their value in studying and redesigning complex processes. OBJECTIVE: Conduct a formative SE analysis of process logic, variation, reliability and failures for completing diagnostic referrals originating in two primary care practices serving different demographics, using dermatology as an illustrating use case. METHODS: An interdisciplinary team of clinicians, systems engineers, quality improvement specialists, and patient representatives collaborated to understand processes of initiating and completing diagnostic referrals. Cross-functional process maps were developed through iterative group interviews with an urban community-based health centre and a teaching practice within a large academic medical centre. Results were used to conduct an engineering process analysis, assess variation within and between practices, and identify common failure modes and potential solutions. RESULTS: Processes to complete diagnostic referrals involve many sub-standard design constructs, with significant workflow variation between and within practices, statistical instability and special cause variation in completion rates and timeliness, and only 21% of all process activities estimated as value-add. Failure modes were similar between the two practices, with most process activities relying on low-reliability concepts (eg, reminders, workarounds, education and verification/inspection). Several opportunities were identified to incorporate higher reliability process constructs (eg, simplification, consolidation, standardisation, forcing functions, automation and opt-outs). CONCLUSION: From a systems science perspective, diagnostic referral processes perform poorly in part because their fundamental designs are fraught with low-reliability characteristics and mental models, including formalised workaround and rework activities, suggesting a need for different approaches versus incremental improvement of existing processes. SE perspectives and methods offer new ways of thinking about patient safety problems, failures and potential solutions.

Home spirometry utilisation in telemedicine clinic for cystic fibrosis care during COVID-19 pandemic: a quality improvement process.

IntroductionThe Cystic Fibrosis (CF) Foundation chronic care guidelines recommend monitoring spirometry during quarterly multidisciplinary visits to identify early lung function decline. During the COVID-19 pandemic, the CF adult clinic at University of Virginia (UVA) transitioned from the classic CF care model to a model that included quarterly multidisciplinary telemedicine visits. While using telemedicine, CF care needed to include spirometry monitoring. Only a fraction of adult CF patients at UVA owned and used home spirometers (HS) in March 2020. AIM: The specific aims of this quality improvement (QI) project were to increase the percentage of eligible adult CF patients who owned an HSs from 37% to 85% and to increase the percentage of adult CF patients seen at UVA with available spirometry in telemedicine from 50% to 95% by 31 December 2020. METHODS: Following the Model for Improvement QI methodology, a standardised process was developed for monitoring forced expiratory volume in 1 s with HS during multidisciplinary telemedicine visits during the COVID-19 pandemic. INTERVENTION: (1) HSs were distributed to eligible patients and (2) Home spirometry was monitored in eligible patients with each telemedicine visit and results were used for clinical care decisions. RESULTS: Both specific aims were achieved ahead of expected date. In March 2020, the beginning of the pandemic, 37% (49/131) of patients owned an HS and 50% (9/18) of patients seen via telemedicine performed spirometry at home. By September 2020, 97% (127/131) of adult patients at UVA owned an HS and by October 2020, 96% (24/25) of patients provided spirometry results during their telemedicine encounters. CONCLUSION: Employing QI tools to standardise the process of monitoring spirometry data with home devices via telemedicine is reliable and sustainable and can be replicated across centres that provide care for patients with CF.

Use of failure modes and effects analysis to mitigate potential risks prior to implementation of an intravenous compounding technology.

PURPOSE: The purpose of this study was to identify potential failure points in a new chemotherapy preparation technology and to implement changes that prevent or minimize the consequences of those failures before they occur using the failure modes and effects analysis (FMEA) approach. METHODS: An FMEA was conducted by a team of medication safety pharmacists, oncology pharmacists and technicians, leadership from informatics, investigational drug, and medication safety services, and representatives from the technology vendor. Failure modes were scored using both Risk Priority Number (RPN) and Risk Hazard Index (RHI) scores. RESULTS: The chemotherapy preparation workflow was defined in a 41-step process with 16 failure modes. The RPN and RHI scores were identical for each failure mode because all failure modes were considered detectable. Five failure modes, all attributable to user error, were deemed to pose the highest risk. Mitigation strategies and system changes were identified for 2 failure modes, with subsequent system modifications resulting in reduced risk. CONCLUSION: The FMEA was a useful tool for risk mitigation and workflow optimization prior to implementation of an intravenous compounding technology. The process of conducting this study served as a collaborative and proactive approach to reducing the potential for medication errors upon adoption of new technology into the chemotherapy preparation process.

Use of Failure Mode and Effect Analysis to Reduce Admission of Asymptomatic COVID-19 Patients to the Adult Emergency Department: An Institutional Experience.

BACKGROUND: Failure mode and effect analysis is an important tool to identify failures in a system with its possible cause, effect, and set actions to be implemented proactively before the occurrence of problems. This study tries to identify common failure modes with its possible causes and effect to the health service and to plot actions to be implemented to reduce COVID-19 transmission to clients, staff, and subsequent service compromise from asymptomatic COVID-19 patients visiting the adult emergency department of SPHMMC (non-COVID-19 setup). METHOD AND STUDY DESIGN: A multidisciplinary team, representing different divisions of the adult emergency department at St. Paul's Hospital Millennium Medical College (SPHMMC), was chosen. This team was trained on failure mode and effect analysis and basics of COVID-19, to identify possible causes of failures and their potential effects, to calculate a risk priority number (RPN) for each failure, and plan changes in practice. RESULTS: A total of 22 failure modes and 89 associated causes and effects were identified. Many of these failure modes (12 out of 22) were found in all steps of patient flow and were associated with either due to lack of or failure to apply standard and transmission-based precautions. This suggests the presence of common targets for improvement, particularly in enhancing the safety of staff and clients. As a result of this FMEA, 23 general improvement actions were proposed. CONCLUSION: FMEA can be used as a useful tool for anticipating potential failures in the process and proposing improvement actions that could help in reducing secondary transmissions during the pandemic.

Algorithmic prediction of failure modes in healthcare.

BACKGROUND: Preventing medical errors is crucial, especially during crises like the COVID-19 pandemic. Failure Modes and Effects Analysis (FMEA) is the most widely used prospective hazard analysis in healthcare. FMEA relies on brainstorming by multi-disciplinary teams to identify hazards. This approach has two major weaknesses: significant time and human resource investments, and lack of complete and error-free results. OBJECTIVES: To introduce the algorithmic prediction of failure modes in healthcare (APFMH) and to examine whether APFMH is leaner in resource allocation in comparison to the traditional FMEA and whether it ensures the complete identification of hazards. METHODS: The patient identification during imaging process at the emergency department of Sheba Medical Center was analyzed by FMEA and APFMH, independently and separately. We compared between the hazards predicted by APFMH method and the hazards predicted by FMEA method; the total participants' working hours invested in each process and the adverse events, categorized as 'patient identification', before and after the recommendations resulted from the above processes were implemented. RESULTS: APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA: the former used 21 h whereas the latter required 63 h. Following the implementation of the recommendations, the adverse events decreased by 44% annually (P = 0.0026). Most adverse events were preventable, had all recommendations been fully implemented. CONCLUSION: In light of our initial and limited-size study, APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA. APFMH is suggested as an alternative to FMEA since it is leaner in time and human resources, ensures more complete hazard identification and is especially valuable during crisis time, when new protocols are often adopted, such as in the current days of the COVID-19 pandemic.