Comparison of failure modes and effects analyses and time for brachytherapy ring and tandem applicator digitization between manual and solid applicator source placement methods.

PURPOSE: Ring and tandem (R&T) applicator digitization is currently performed at our institution by manually defining the extent of the applicators. Digitization can also be achieved using solid applicators: predefined, 3D models with geometric constraints. This study compares R&T digitization using manual and solid applicator methods through Failure Modes and Effects Analyses (FMEAs) and comparative time studies. We aim to assess the suitability of solid applicator method implementation for R&T cases METHODS: Six qualified medical physicists (QMPs) and two medical physics residents scored potential modes of failure of manual digitization in an FMEA as recommended by TG-100. Occurrence, severity, and detectability (OSD) values were averaged across respondents and then multiplied to form combined Risk Priority Numbers (RPNs) for analysis. Participants were trained to perform treatment planning using a developed solid applicator protocol and asked to score a second FMEA on the distinct process steps from the manual method. For both methods, participant digitization was timed. FMEA and time data were analyzed across methods and participant samples RESULTS: QMPs rated the RPNs of the current, manual method of digitization statistically lower than residents did. When comparing the unique FMEA steps between the two digitization methods, QMP respondents found no significant difference in RPN means. Residents, however, rated the solid applicator method as higher risk. Further, after the solid applicator method was performed twice by participants, the time to digitize plans was not significantly different from manual digitization CONCLUSIONS: This study indicates the non-inferiority of the solid applicator method to manual digitization in terms of risk, according to QMPs, and time, across all participants. Differences were found in FMEA evaluation and solid applicator technique adoption based on years of brachytherapy experience. Further practice with the solid applicator protocol is recommended because familiarity is expected to lower FMEA occurrence ratings and further reduce digitization times.

Technical note: A universal worksheet for failure mode and effects analysis-A project of the Japanese College of Medical Physics.

BACKGROUND: Failure mode and effects analysis (FMEA), which is an effective tool for error prevention, has garnered considerable attention in radiotherapy. FMEA can be performed individually, by a group or committee, and online. PURPOSE: To meet the needs of FMEA for various purposes and improve its accessibility, we developed a simple, self-contained, and versatile web-based FMEA risk analysis worksheet. METHODS: We developed an FMEA worksheet using Google products, such as Google Sheets, Google Forms, and Google Apps Script. The main sheet was created in Google Sheets and contained elements necessary for performing FMEA by a single person. Automated tasks were implemented using Apps Script to facilitate multiperson FMEA; these functions were built into buttons located on the main sheet. RESULTS: The usability of the FMEA worksheet was tested in several situations. The worksheet was feasible for individual, multiperson, seminar, meeting, and online purposes. Simultaneous online editing, automated survey form creation, automatic analysis, and the ability to respond to the form from multiple devices, including mobile phones, were particularly useful for online and multiperson FMEA. Automation enabled through Google Apps Script reduced the FMEA workload. CONCLUSIONS: The FMEA worksheet is versatile and has a seamless workflow that promotes collaborative work for safety.

Advanced prescription of injectable anticancer drugs: Safety assessment in a European Comprehensive Cancer Centre using the risk matrix approach.

AIMS: The purpose of this work was to assess failures in the advanced prescription of parenteral anticancer agents in an adult day oncology care unit with more than 100 patients per day. METHODS: An a priori descriptive analysis was carried out by using the risk matrix approach. After defining the scope in a multidisciplinary meeting, we determined at each step the failure modes (FMs), their effects (E) and their associated causes (C). A severity score (S) was assigned to all effects and a probability of occurrence (O) to all causes. These S and O indicators, were used to obtain a criticality index (CI) matrix. We assessed the risk control (RC) of each failure in order to define a residual criticality index (rCI) matrix. RESULTS: During risk analysis, 14 FMs were detected, and 61 scenarios were identified considering all possible effects and causes. Nine situations (15%) were highlighted with the maximum CI, 18 (30%) with a medium CI, and 34 (55%) with a negligible CI. Nevertheless, among all these critical situations, only three (5%) had an rCI to process (i.e., missed dose adjustment, multiple prescriptions and abnormal biology data); the others required monitoring only. Clinicians' and pharmacists' knowledge of these critical situations enables them to manage the associated risks. CONCLUSIONS: Advanced prescription of injectable anticancer drugs appears to be a safe practice for patients when combined with risk management. The major risks identified concerned missed dose adjustment, prescription duplication and lack of consideration for abnormal biology data.

Reducing the Risk of Fall among Oncology Patients using Failure Modes and Effects Analysis.

OBJECTIVE: This project aimed to mitigate the risk of falls among oncology patients using Failure Modes and Effects Analysis (FMEA) in the outpatient setting.



Methods: The project was conducted within outpatient settings, specifically encompassing outpatient clinics, daycare, radiology and radiotherapy, and rehabilitation at the SQCCCRC. The project employed an observational analytical design to assess the fall risk assessment procedure in outpatient settings. The project integrated a 7-step procedure for conducting an FMEA methodology, including defining the system or process, identifying potential failure mode, evaluating the effects of each failure mode, Assigning severity, likelihood, and detection of occurrence ratings, and identifying and implement corrective actions. In addition, Risk Priority Numbers (RPNs) were used to identify the impact of the interventions in reducing the risk of patient fall assessment and management.



Result: In the patient fall screening process, interventions yielded substantial reductions in RPNs for failure modes like "Wrong assessment" (57% decrease) and "Complex risk assessment scale" (63% decrease), addressing knowledge gaps and simplifying risk assessment. Similarly, the "Missed fall assessment" failure mode saw an impressive 80% reduction in RPN, rectifying unclear processes and knowledge gaps. In the Fall risk precaution measures process, interventions led to noteworthy RPN reductions, such as 80% for "Unclear fall precaution measures-responsibilities" and 57% for "Missed bracelets for high risk," demonstrating successful risk mitigation. Moreover, interventions in the Patient Education process achieved significant RPN reductions (57% and 55%) for "No/improper education" and "Unuse of educational material and resources," enhancing staff education and patient awareness. The total reduction in RPNs was 62% in all failure modes in the fall assessment and management process.



Conclusion: Overall, FMEA is a valuable strategy for reducing fall risks among oncology patients, but its success depends on addressing these limitations and ensuring the thorough execution and maintenance of the identified corrective actions.

Multi-phase failure modes and effects analysis for low dose bilateral whole lung irradiation of COVID-19 positive patients requiring respiratory ventilation.

PURPOSE: To identify high-priority risks in a clinical trial investigating the use of radiation to alleviate COVID-19 pneumonia using a multi-phase failure modes and effects analysis (FMEA). METHODS: A comprehensive FMEA survey of 133 possible causes of failure was developed for the clinical trial workflow (Phase I). The occurrence, severity, and detection risk of each possible cause of failure was scored by three medical physicists. High-risk potential failure modes were identified using the risk priority number (RPN) and severity scores, which were re-scored by 13 participants in radiation oncology (Phase II). Phase II survey scores were evaluated to identify steps requiring possible intervention and examine risk perception patterns. The Phase II participants provided consensus scores as a group. RESULTS: Thirty high-priority failure modes were selected for the Phase II survey. Strong internal consistency was shown in both surveys using Cronbach's alpha (αc ≥ 0.85). The 10 failures with the largest median RPN values concerned SARS-CoV-2 transmission (N = 6), wrong treatment (N = 3), and patient injury (N = 1). The median RPN was larger for COVID-related failures than other failure types, primarily due to the perceived difficulty of failure detection. Group re-scoring retained 8/10 of the highest-priority risk steps that were identified in the Phase II process, and discussion revealed interpretation differences of process steps and risk evaluation. Participants who were directly involved with the trial working group had stronger agreement on severity scores than those who were not. CONCLUSIONS: The high ranking of failures concerning SARS-CoV-2 transmission suggest that these steps may require additional quality management intervention when treating critically ill COVID-19+ patients. The results also suggest that a multi-phase FMEA survey led by a facilitator may be a useful tool for assessing risks in radiation oncology procedures, supporting future efforts to adapt FMEA to clinical procedures.

Administração de anti-infecciosos em pacientes críticos: gestão proativa de riscos / Administration of anti-infectives in critically ill patients: proactive risk management / Administración de antiinfecciosos en pacientes críticos: gestión proactiva del riesgo

Objetivo: analisar a gestão de riscos proativa do processo de administração de anti-infecciosos em Unidade de Terapia Intensiva. Método: estudo qualitativo, em pesquisa-ação, com observação participante e grupo focal, realizado de 2019 a 2021. Foi mapeado o processo, analisados os riscos, planejadas ações de melhorias e redesenhado o processo. Resultados: a prescrição ocorria em sistema eletrônico e os registros da administração em impressos. O processo de administração de anti-infecciosos possuía 19 atividades, dois subprocessos, 16 modos de falhas e 23 causas potenciais. Os modos de falhas foram relacionados à assepsia e erro de dose no preparo de anti-infecciosos e as causas apontadas foram a falha humana na violação das técnicas e o lapso de memória. Cinco especialistas redesenharam o processo resultando em alterações de atividades e no sistema. Conclusão: a gestão de riscos proativa aplicada ao processo de administração de anti-infecciosos propiciou identificar riscos, suas causas e priorizar ações de melhorias, o que pode viabilizar tomadas de decisões apropriadas(AU)

Objective: to analyze the proactive risk management of the anti-infective administration process in an Intensive Care Unit. Method: qualitative study, in action research, with participant observation and focus group, from 2019 to 2021. The process was mapped, risks analyzed, improvement actions planned and the process redesigned. Results: the prescription occurred in an electronic system and the administration records in printed form. The anti-infective administration process had 19 activities, two sub-processes, 16 failure modes and 23 potential causes. The failure modes were related to asepsis and dose error in the preparation of anti-infectives and the identified causes were human error in violating techniques and memory lapse. Five specialists redesigned the process resulting in changes in activities and in the system. Conclusion: proactive risk management applied to the anti-infective administration process was effective in identifying risks, their causes and prioritizing improvement actions(AU)

Objetivo: analizar la gestión proactiva de riesgos del proceso de administración de antiinfecciosos en una Unidad de Cuidados Intensivos. Método: estudio cualitativo, en investigación-acción, con observación participante y grupo focal, que tuvo lugar del 2019 al 2021. Se mapeó el proceso, se analizaron los riesgos, se planificaron acciones de mejora y se rediseñó el proceso. Resultados: la prescripción ocurrió en sistema electrónico y los registros de administración en forma impresa. El proceso de administración de antiinfecciosos tuvo 19 actividades, dos subprocesos, 16 modos de falla y 23 causas potenciales. Los modos de falla estuvieron relacionados con la asepsia y error de dosis en la preparación de antiinfecciosos y las causas identificadas fueron error humano por violación de técnicas y lapsus de memoria. Cinco especialistas rediseñaron el proceso generando cambios en las actividades y en el sistema. Conclusión: la gestión proactiva de riesgos aplicada al proceso de administración de antiinfecciosos fue efectiva para identificar riesgos, sus causas y priorizar acciones de mejora, lo que puede factibilizar la toma de decisiones adecuadasa(AU)

Improved safety and quality in intravascular brachytherapy: A multi-institutional study using failure modes and effects analysis.

PURPOSE: Highlight safety considerations in intravascular brachytherapy (IVBT) programs, provide relevant quality assurance (QA) and safety measures, and establish their effectiveness. METHODS AND MATERIALS: Radiation oncologists, medical physicists, and cardiologists from three institutions performed a failure modes and effects analysis (FMEA) on the radiation delivery portion of IVBT. We identified 40 failure modes and rated the severity, occurrence, and detectability before and after consideration of safety practices. Risk priority numbers (RPN) and relative risk rankings were determined, and a sample QA safety checklist was developed. RESULTS: We developed a process map based on multi-institutional consensus. Highest-RPN failure modes were due to incorrect source train length, incorrect vessel diameter, and missing prior radiation history. Based on these, we proposed QA and safety measures: ten of which were not previously recommended. These measures improved occurrence and detectability: reducing the average RPN from 116 to 58 and median from 84 to 40. Importantly, the average RPN of the top 10% of failure modes reduced from 311 to 172. With QA considered, the highest risk failure modes were from contamination and incorrect source train length. CONCLUSIONS: We identified several high-risk failure modes in IVBT procedures and practical safety and QA measures to address them.

Implementing failure mode and effect analysis to improve the safety of volumetric modulated arc therapy for total body irradiation.

PURPOSE: Volumetric-modulated arc therapy for total body irradiation (VMAT-TBI) is a novel radiotherapy technique that has been implemented at our institution. The purpose of this work is to investigate possible failure modes (FMs) in the treatment process and to develop a quality control (QC) program for VMAT-TBI following TG-100 guidelines. METHODS: We formed a multidisciplinary team to map out the complete treatment process of VMAT-TBI following the AAPM TG-100 guidelines. This process map gives a visual representation of the VMAT-TBI workflow from the CT simulation, image processing, contouring, treatment planning, to treatment delivery. From the process map, potential FMs were identified. The occurrence (O), detectability (D), and severity of impact (S) of each FM were assigned according to scoring criteria (1-10) by the multidisciplinary team. A risk priority number (RPN) was calculated from average O, S, and D of each FM (RPN = O x S x D). High risk FMs were identified as 20% of the FMs having the highest RPN scores. After the FMEA analysis, fault-tree analysis (FTA) was performed for each major step of the treatment process to determine the effects of potential failures to the treatment outcome. Effective QC methods were identified to prevent the high risk failures and to improve the safety of the VMAT-TBI program. RESULTS: We identified a total of 55 sub-processes and 128 FMs from the VMAT-TBI workflow. The top five high-risk FMs were: (1) Prescription and/or OAR constraints changed during planning and not communicated to the planner, (2) Patient moves or breathes too heavily during the upper body CT scan (3) Patient moves during the lower body CT scan, (4) Treatment planning system not calculating total body DVH metrics correctly for TBI, (5) Improper optimization criteria used or not sufficient optimization, resulting in suboptimal dose coverage, OAR sparing or excessive hotspots during treatment planning. Two FMs have average severity scores ≥8: Incorrect PTV subdivision/isocenter placement and Prescription and/or OAR constraints changed during planning and not communicated to the planner. Quality assurance and QC interventions including staff training, standard operating procedures, and quality checklists were implemented based on the FMEA and FTA. CONCLUSION: FM and effect analysis was performed to identify high-risk FMs of our VMAT-TBI program. FMEA and FTA were effective in identifying potential FMs and determining the best quality management (QM) measures to implement in the VMAT-TBI program.

[Application of health failure mode and effect analysis for the airbag pressure management of patients with artificial airways].

OBJECTIVE: To analyze the application effect of health failure mode and effect analysis (HFMEA) model in patients with artificial airways in the cardiovascular surgery intensive care unit (CSICU) by establishing a HFMEA project team, and to develop targeted improvement measures and processes. METHODS: The patients undergoing cardiovascular surgeries and with established artificial airways in the Shandong Provincial Hospital Affiliated to Shandong First Medical University were recruited from October 2021 to March 2022. The enrolled patients were divided into the conventional management group and the HFMEA model management group according to random number table method. The conventional management group applied the conventional procedures for monitoring the air bag pressure. The HFMEA model management group used the HFMEA model to implement and improve the airbag pressure monitoring process. The efficacy of HFMEA was assessed by comparing the incidence of ventilator-associated pneumonia (VAP), the pass rate of airbag pressure monitoring, the duration of endotracheal intubation and the length of CSICU stay between two groups. The practicability of HFMEA model was evaluated by analyzing the theoretical assessment scores and practical skill scores of nurses and their satisfaction scores with HFMEA. RESULTS: Compared with the conventional management group, the patients in the HFMEA mode management group had a significantly higher rate of passing airbag pressure monitoring [94.99% (2 994/3 152) vs. 69.97% (1 626/2 324), P < 0.01], shorter duration of endotracheal intubation and length of CSICU stay [duration of endotracheal intubation (hours): 6 (7, 12) vs. 6 (8, 13), length of CSICU stay (hours): 40 (45, 65) vs. 41 (46, 85), both P < 0.05], but the incidences of VAP between the two groups were similar. The theoretical assessment scores and practical skill scores of nurses were significantly higher (theoretical assessment score: 44.47±2.72 vs. 37.59±6.56, practical skill score: 44.56±2.66 vs. 40.03±4.32, total score: 89.03±3.07 vs. 77.63±9.56, all P < 0.05) in the HFMEA mode management group. And the satisfaction scores with airbag pressure management were also significantly higher in the HFMEA mode management group (7.72±1.11 vs. 6.44±1.32, P < 0.05). CONCLUSIONS: The application of the HFMEA can improve the airbag pressure measures and standardize the monitoring procedures in patients with artificial airways, and reduce the risk of clinical nursing. It is safe and effective for patients with invasive mechanical ventilation in the CSICU.

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.

Prioritizing clinical trial quality assurance for photons and protons: A failure modes and effects analysis (FMEA) comparison.

BACKGROUND AND PURPOSE: The Global Clinical Trials RTQA Harmonization Group (GHG) set out to evaluate and prioritize clinical trial quality assurance. METHODS: The GHG compiled a list of radiotherapy quality assurance (QA) tests performed for proton and photon therapy clinical trials. These tests were compared between modalities to assess whether there was a need for different types of assessments per modality. A failure modes and effects analysis (FMEA) was performed to assess the risk of each QA failure. RESULTS: The risk analysis showed that proton and photon therapy shared four out of five of their highest-risk failures (end-to-end anthropomorphic phantom test, phantom tests using respiratory motion, pre-treatment patient plan review of contouring/outlining, and on-treatment/post-treatment patient plan review of dosimetric coverage). While similar trends were observed, proton therapy had higher risk failures, driven by higher severity scores. A sub-analysis of occurrence × severity scores identified high-risk scores to prioritize for improvements in RTQA detectability. A novel severity scaler was introduced to account for the number of patients affected by each failure. This scaler did not substantially alter the ranking of tests, but it elevated the QA program evaluation to the top 20th percentile. This is the first FMEA performed for clinical trial quality assurance. CONCLUSION: The identification of high-risk errors associated with clinical trials is valuable to prioritize and reduce errors in radiotherapy and improve the quality of trial data and outcomes, and can be applied to optimize clinical radiotherapy QA.

Application of the Failure Mode and Effects Analysis (FMEA) to identify vulnerabilities and opportunities for improvement prior to implementing a computerized prescription order entry (CPOE) system in a university hospital oncology clinic.

INTRODUCTION: Prior to implementing a new computerized prescription order entry (CPOE) application, the potential risks associated with this system were assessed and compared to those of paper-based prescriptions. The goal of this study is to identify the vulnerabilities of the CPOE process in order to adapt its design and prevent these potential risks. METHODS AND MATERIALS: Failure mode and effects analysis (FMEA) was used as a prospective risk-management technique to evaluate the chemotherapy medication process in a university hospital oncology clinic. A multidisciplinary team assessed the process and compared the critical steps of a newly developed CPOE application versus paper-based prescriptions. The potential severity, occurrence and detectability were assessed prior to the implementation of the CPOE application in the clinical setting. RESULTS: The FMEA led to the identification of 24 process steps that could theoretically be vulnerable, therefore called failure modes. These failure modes were grouped into four categories of potential risk factors: prescription writing, patient scheduling, treatment dispensing and patient follow-up. Criticality scores were calculated and compared for both strategies. Three failure modes were prioritized and led to modification of the CPOE design. Overall, the CPOE pathway showed a potential risk reduction of 51% compared to paper-based prescriptions. CONCLUSION: FMEA was found to be a useful approach to identify potential risks in the chemotherapy medication process using either CPOE or paper-based prescriptions. The e-prescription mode was estimated to result in less risk than the traditional paper mode.

Failure modes and effects analysis study for accelerator-based Boron Neutron Capture Therapy.

BACKGROUND: Boron Neutron Capture Therapy (BNCT) has recently been used in clinical oncology thanks to recent developments of accelerator-based BNCT systems. Although there are some specific processes for BNCT, they have not yet been discussed in detail. PURPOSE: The aim of this study is to provide comprehensive data on the risk of accelerator-based BNCT system to institutions planning to implement an accelerator-based BNCT system. METHODS: In this study, failure mode and effects analysis (FMEA) was performed based on a treatment process map prepared for the accelerator-based BNCT system. A multidisciplinary team consisting of a medical doctor (MD), a registered nurse (RN), two medical physicists (MP), and three radiologic technologists (RT) identified the failure modes (FMs). Occurrence (O), severity (S), and detectability (D) were scored on a scale of 10, respectively. For each failure mode (FM), risk priority number (RPN) was calculated by multiplying the values of O, S, and D, and it was then categorized as high risk, very high risk, and other. Additionally, FMs were statistically compared in terms of countermeasures, associated occupations, and whether or not they were the patient-derived. RESULTS: The identified FMs for BNCT were 165 in which 30 and 17 FMs were classified as high risk and very high risk, respectively. Additionally, 71 FMs were accelerator-based BNCT-specific FMs in which 18 and 5 FMs were classified as high risk and very high risk, respectively. The FMs for which countermeasures were "Education" or "Confirmation" were statistically significantly higher for S than the others (p = 0.019). As the number of BNCT facilities is expected to increase, staff education is even more important. Comparing patient-derived and other FMs, O tended to be higher in patient-derived FMs. This could be because the non-patient-derived FMs included events that could be controlled by software, whereas the patient-derived FMs were impossible to prevent and might also depend on the patient's condition. Alternatively, there were non-patient-derived FMs with higher D, which were difficult to detect mechanically and were classified as more than high risk. In O, significantly higher values (p = 0.096) were found for FMs from MD and RN associated with much patient intervention compared to FMs from MP and RT less patient intervention. Comparing conventional radiotherapy and accelerator-based BNCT, although there were events with comparable risk in same FMs, there were also events with different risk in same FMs. They could be related to differences in the physical characteristics of the two modalities. CONCLUSIONS: This study is the first report for conducting a risk analysis for BNCT using FMEA. Thus, this study provides comprehensive data needed for quality assurance/quality control (QA/QC) in the treatment process for facilities considering the implementation of accelerator-based BNCT in the future. Because many BNCT-specific risks were discussed, it is important to understand the characteristics of BNCT and to take adequate measures in advance. If the effects of all FMs and countermeasures are discussed by multidisciplinary team, it will be possible to take countermeasures against individual FMs from many perspectives and provide BNCT more safely and effectively.

Validation of the Algorithmic Prediction of Failure Modes in Health Care Methodology: Applied to the Department of Sterile Supply and Equipment.

Failure mode and effect analysis (FMEA) is a leading tool for risk management in health care. The term "blanket" approach FMEA describes a comprehensive simultaneous look at the variety of interrelated factors that may directly and indirectly affect patient safety. Applying FMEA with the "blanket" approach is not common, due to FMEA's limitations. Algorithmic prediction of failure modes in health care (APFMH) is leaner and enables the application of the "blanket" approach, but, like FMEA, it lacks formal validation. The authors set out to validate the APFMH method while applying a "blanket" approach. They analyzed the sterile supply handling at a 1900-bed academic medical center. The study's first step took place in the operating room (OR) aspect of the process. An APFMH analysis was performed using the "blanket" approach, to identify the hazards and define the common root causes for predicted hazards. The second step took place a year later at the sterile supply and equipment department (SSED) and aimed to validate these root causes, thus validating the reliability of APFMH. The "blanket" approach analysis with the APFMH method consisted of categorization into 3 risk-dimensions: patient safety, equipment damage, and time management. Root causes were defined for 8 high-ranking hazards. All the root causes for failures, identified by APFMH at the OR department, were revealed as actual hazards in the processes of the SSED. The independent findings at the SSED level validated the list of identified hazards that was formed at the target department (ie, the OR). APFMH methodology is a lean in time and human resources process that ensures comprehensive hazard analysis, which can include the "blanket" approach, and which was validated in this study. The authors suggest using the APFMH methodology for any organizational analysis method that requires the inclusion of "blanket" approaches.

Failure Mode and Effects Analysis for Experimental Use of FLASH on a Clinical Accelerator.

PURPOSE: The use of a linear accelerator (LINAC) in ultrahigh-dose-rate (UHDR) mode can provide a conduit for wider access to UHDR FLASH effects, sparing normal tissue, but care needs to be taken in the use of such systems to ensure errors are minimized. The failure mode and effects analysis was carried out in a team that has been involved in converting a LINAC between clinical use and UHDR experimental mode for more than 1 year after the proposed methods of TG100. METHODS AND MATERIALS: A team of 9 professionals with extensive experience were polled to outline the process map and workflow for analysis, and developed fault trees for potential errors, as well as failure modes that would result. The team scored the categories of severity magnitude, occurrence likelihood, and detectability potential in a scale of 1 to 10, so that a risk priority number (RPN = severity×occurrence×detectability) could be assessed for each. RESULTS: A total of 46 potential failure modes were identified, including 5 with an RPN >100. These failure modes involved (1) patient set up, (2) gating mechanisms in delivery, and (3) detector in the beam stop mechanism. The identified methods to mitigate errors included the (1) use of a checklist post conversion, (2) use of robust radiation detectors, (3) automation of quality assurance and beam consistency checks, and (4) implementation of surface guidance during beam delivery. CONCLUSIONS: The failure mode and effects analysis process was considered critically important in this setting of a new use of a LINAC, and the expert team developed a higher level of confidence in the ability to safely move UHDR LINAC use toward expanded research access.

Use of Failure Mode and Effects Analysis (FMEA) for Risk Analysis of Drug Use in Patients with Lung Cancer.

It is crucial to investigate the risk factors inherent in the medication process for cancer patients since improper antineoplastic drug use frequently has serious consequences. As a result, the Severity, Occurrence, and Detection rate of each potential failure mode in the drug administration process for patients with lung cancer were scored using the Failure Mode and Effect Analysis (FMEA) model in this study. Then, the risk level of each failure mode and the direction of improvement were investigated using the Slacks-based measure data envelopment analysis (SBM-DEA) model. According to the findings, the medicine administration process for lung cancer patients could be classified into five links, with a total of 60 failure modes. The risk of failure modes for patient medication and post-medication monitoring ranked highly, with unauthorized use of traditional Chinese medicine and folk prescription and unauthorized drug addition (incorrect self-medication) ranking first (1/60); doctor prescription was also prone to errors. The study advises actively looking at ways to decrease the occurrence and difficulty of failure mode detection to continually enhance patient safety when using medications.

Application of failure mode and effects analysis to validate a novel hybrid Linac QC program that integrates automated and conventional QC testing.

A hybrid quality control (QC) program was developed that integrates automated and conventional Linac QC, realizing the benefits of both automated and conventional QC, increasing efficiency and maintaining independent measurement methods. Failure mode and effects analysis (FMEA) was then applied in order to validate the program prior to clinical implementation. The hybrid QC program consists of automated QC with machine performance check and DailyQA3 array on the TrueBeam Linac, and Delta4 volumetric modulated arc therapy (VMAT) standard plan measurements, alongside conventional monthly QC at a reduced frequency. The FMEA followed the method outlined in TG-100. Process maps were created for each treatment type at our center: VMAT, stereotactic body radiotherapy (SBRT), conformal, and palliative. Possible failure modes were established by evaluating each stage in the process map. The FMEA followed semiquantitative methods, using data from our QC records from eight Linacs over 3 years for the occurrence estimates, and simulation of failure modes in the treatment planning system, with scoring surveys for severity and detectability. The risk priority number (RPN) was calculated from the product of the occurrence, severity, and detectability scores and then normalized to the maximum and ranked to determine the most critical failure modes. The highest normalized RPN values (100, 90) were found to be for MLC position dynamic for both VMAT and SBRT treatments. The next highest score was 35 for beam position for SBRT, and the majority of scores were less than 20. Overall, these RPN scores for the hybrid Linac QC program indicated that it would be acceptable, but the high RPN score associated with the dynamic MLC failure mode indicates that it would be valuable to perform more rigorous testing of the MLC. The FMEA proved to be a useful tool in validating hybrid QC.

Proactive Risk Assessment through Failure Mode and Effect Analysis (FMEA) for Perioperative Management Model of Oral Anticoagulant Therapy: A Pilot Project.

INTRODUCTION: Correct perioperative management of anticoagulant therapy is essential to prevent thromboembolic events and reduce the risk of bleeding. The lack of universally accepted guidelines makes perioperative anticoagulant therapy management difficult. The present study aims to identify the perioperative risks of oral anticoagulant therapy and to reduce adverse events through Failure Mode and Effect Analysis (FMEA). MATERIALS AND METHODS: A multidisciplinary working group was set up, and four main phases of the process were identified. Each of these phases was divided into micro-activities to identify the related possible failure modes and their potential consequences. The Risk Priority Number was calculated for each failure mode. RESULTS AND DISCUSSION: Seventeen failure modes were identified in the entire perioperative period; those with a higher priority of intervention concern the incorrect timing between therapy suspension and surgery, and the incorrect assessment of the bleeding risk related to the invasive procedure. CONCLUSION: The FMEA method can help identify anticoagulant therapy perioperative failures and implement the management and patient safety of surgical procedures.

Failure modes and effects analysis of pediatric I-131 MIBG therapy: Program design and potential pitfalls.

BACKGROUND: There is growing interest among pediatric institutions for implementing iodine-131 (I-131) meta-iodobenzylguanidine (MIBG) therapy for treating children with high-risk neuroblastoma. Due to regulations on the medical use of radioactive material (RAM), and the complexity and safety risks associated with the procedure, a multidisciplinary team involving radiation therapy/safety experts is required. Here, we describe methods for implementing pediatric I-131 MIBG therapy and evaluate our program's robustness via failure modes and effects analysis (FMEA). METHODS: We formed a multidisciplinary team, involving pediatric oncology, radiation oncology, and radiation safety staff. To evaluate the robustness of the therapy workflow and quantitatively assess potential safety risks, an FMEA was performed. Failure modes were scored (1-10) for their risk of occurrence (O), severity (S), and being undetected (D). Risk priority number (RPN) was calculated from a product of these scores and used to identify high-risk failure modes. RESULTS: A total of 176 failure modes were identified and scored. The majority (94%) of failure modes scored low (RPN <100). The highest risk failure modes were related to training and to drug-infusion procedures, with the highest S scores being (a) caregivers did not understand radiation safety training (O = 5.5, S = 7, D = 5.5, RPN = 212); (b) infusion training of staff was inadequate (O = 5, S = 8, D = 5, RPN = 200); and (c) air in intravenous lines/not monitoring for air in lines (O = 4.5, S = 8, D = 5, RPN = 180). CONCLUSION: Through use of FMEA methodology, we successfully identified multiple potential points of failure that have allowed us to proactively mitigate risks when implementing a pediatric MIBG program.

Value of HFMEA-based Predictive Care Combined With Multimodal Analgesia in Improving Rehabilitation After Orthopedic Internal Fixation Implantation.

Context: Orthopedic internal fixation implantation (OIFI) is a frequently adopted surgery for fractures, but it can trigger various adverse reactions and increase patients' risks of postoperative complications. Reducing those risks is paramount for obtaining better therapeutic effects for OIFI. Objective: The study intended to analyze the value of predictive nursing, based on healthcare failure modes and effects analysis (HFMEA), and combined with multimodal analgesia for improving postoperative rehabilitation after orthopedic internal fixation (OIFI), with the aim of offering reliable, accurate, and novel ideas and directions for future clinical OIFI and prognosis improvement for patients. Design: The research team designed a retrospective analysis. Setting: The study took place in the Department of the Operating Room at Hefei First People's Hospital in Hefei, Anhui, China. Participants: Participants were150 patients who needed OIFI at the hospital between January and December 2020. Intervention: Participants were assigned to one of two groups, 87 to the intervention group, who received treatment with HFMEA-based predictive care combined with multimodal analgesia after OIFI, and 63 to a control group who received routine nursing combined with multimodal analgesia after OIFI. Outcome Measures: Postintervention, the study measured the effective treatment rate, risk priority number (RPN)-the severity, possibility, and detectable degree of the risk, analgesic effects, self-controlled delivery times, tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6) levels, and incidence of adverse symptoms. Also postintervention, the participants completed a visual analogue scale (VAS) to indicate their satisfaction with the nursing as well as the Exercise of Self-care Agency (ESCA) scale and the Spielberger State-trait Anxiety Inventory (STAI). Results: The study found significant differences between the groups. The intervention group showed significantly lower RPN values, VAS scores for analgesia, TNF-α and IL-6 levels, and incidence of adverse symptoms and also indicated greater satisfaction with the nursing, a significantly higher ESCA score, and a significantly better psychological state. Conclusions: HFMEA-based predictive care combined with multimodal analgesia can substantially lower the risk and pain levels of patients undergoing OIFI and can improve their nursing experience and self-care ability, so it's worthy of clinical application, having great significance for patients' rehabilitation.