Risk management of radioiodine treatment in differentiated thyroid cancer.

INTRODUCTION: Patient safety is paramount in providing quality healthcare and constitutes a global concern for healthcare systems. Radioiodine treatment to patients with well-differentiated thyroid cancer is not without risks. The aim of this study is to identify, evaluate and mitigate the risks associated with this procedure. MATERIALS AND METHODS: A single-centre descriptive study was conducted in which risk management was carried out by establishing a risk map using FMEA methodology. RESULTS: Based on the process map 6 sub-processes and 23 failure modes in the three phases of the treatment process were analysed. According to risk priority number (RPN), the sub-process with the highest risk was administrative management (RPN 82), followed by treatment per se and post-treatment imaging (both with RPN 70). An overall process RPN of 300 (156 pre-treatment, 74 treatment and 70 post-treatment) was obtained. Failures directly related to the patient pose a high risk. The implementation of verification systems, performing tasks earlier and providing quality medical information are the most relevant preventive measures to be implemented. CONCLUSIONS: The application of the FMEA methodology in the risk management for radioiodine treatment is a valuable tool for improving the quality and safety of this process. The risk map has been able to identify failures at different stages, assess their causes and effects, prioritise the risks identified and implement preventive and corrective measures that can be monitored, ensuring the effectiveness of the actions taken.

A method for empirically validating FMEA RPN scores in a radiation oncology clinic using physics QC data.

In failure modes and effects analysis (FMEA), the components of the risk priority number (RPN) for a failure mode (FM) are often chosen by consensus. We describe an empirical method for estimating the occurrence (O) and detectability (D) components of a RPN. The method requires for a given FM that its associated quality control measure be performed twice as is the case when a FM is checked for in an initial physics check and again during a weekly physics check. If instances of the FM caught by these checks are recorded, O and D can be computed. Incorporation of the remaining RPN component, Severity, is discussed. This method can be used as part of quality management design ahead of an anticipated FMEA or afterwards to validate consensus values.

Re-evaluation of the prospective risk analysis for artificial-intelligence driven cone beam computed tomography-based online adaptive radiotherapy after one year of clinical experience.

Cone-beam computed tomography (CBCT)-based online adaptation is increasingly being introduced into many clinics. Upon implementation of a new treatment technique, a prospective risk analysis is required and enhances workflow safety. We conducted a risk analysis using Failure Mode and Effects Analysis (FMEA) upon the introduction of an online adaptive treatment programme (Wegener et al., Z Med Phys. 2022). A prospective risk analysis, lacking in-depth clinical experience with a treatment modality or treatment machine, relies on imagination and estimates of the occurrence of different failure modes. Therefore, we systematically documented all irregularities during the first year of online adaptation, namely all cases in which quality assurance detected undesired states potentially leading to negative consequences. Additionally, the quality of automatic contouring was evaluated. Based on those quantitative data, the risk analysis was updated by an interprofessional team. Furthermore, a hypothetical radiation therapist-only workflow during adaptive sessions was included in the prospective analysis, as opposed to the involvement of an interprofessional team performing each adaptive treatment. A total of 126 irregularities were recorded during the first year. During that time period, many of the previously anticipated failure modes (almost) occurred, indicating that the initial prospective risk analysis captured relevant failure modes. However, some scenarios were not anticipated, emphasizing the limits of a prospective risk analysis. This underscores the need for regular updates to the risk analysis. The most critical failure modes are presented together with possible mitigation strategies. It was further noted that almost half of the reported irregularities applied to the non-adaptive treatments on this treatment machine, primarily due to a manual plan import step implemented in the institution's workflow.

Optimization of treatment workflow for 0.35T MR-Linac system.

PURPOSE: This study presents a novel and comprehensive framework for evaluating magnetic resonance guided radiotherapy (MRgRT) workflow by integrating the Failure Modes and Effects Analysis (FMEA) approach with Time-Driven Activity-Based Costing (TDABC). We assess the workflow for safety, quality, and economic implications, providing a holistic understanding of the MRgRT implementation. The aim is to offer valuable insights to healthcare practitioners and administrators, facilitating informed decision-making regarding the 0.35T MRIdian MR-Linac system's clinical workflow. METHODS: For FMEA, a multidisciplinary team followed the TG-100 methodology to assess the MRgRT workflow's potential failure modes. Following the mitigation of primary failure modes and workflow optimization, a treatment process was established for TDABC analysis. The TDABC was applied to both MRgRT and computed tomography guided RT (CTgRT) for typical five-fraction stereotactic body RT (SBRT) treatments, assessing total workflow and costs associated between the two treatment workflows. RESULTS: A total of 279 failure modes were identified, with 31 categorized as high-risk, 55 as medium-risk, and the rest as low-risk. The top 20% risk priority numbers (RPN) were determined for each radiation oncology care team member. Total MRgRT and CTgRT costs were assessed. Implementing technological advancements, such as real-time multi leaf collimator (MLC) tracking with volumetric modulated arc therapy (VMAT), auto-segmentation, and increasing the Linac dose rate, led to significant cost savings for MRgRT. CONCLUSION: In this study, we integrated FMEA with TDABC to comprehensively evaluate the workflow and the associated costs of MRgRT compared to conventional CTgRT for five-fraction SBRT treatments. FMEA analysis identified critical failure modes, offering insights to enhance patient safety. TDABC analysis revealed that while MRgRT provides unique advantages, it may involve higher costs. Our findings underscore the importance of exploring cost-effective strategies and key technological advancements to ensure the widespread adoption and financial sustainability of MRgRT in clinical practice.

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplantation is the only possible therapy, for instance for patients with end-stage liver diseases, and the preferred treatment, for instance for patients with end-stage renal diseases. However, this surgical procedure comes with inherent risks and effectively managing these risks to minimize the likelihood of complications arising from organ transplantation (maximizing life years from transplant and quality-adjusted life years) is crucial. To facilitate this process, risk ranking is used to identify and promptly address potential risks. Over recent years, considerable efforts have been made, and various approaches have been proposed to enhance Failure Modes and Effects Analysis (FMEA). In this study, taking into account the uncertainty in linguistic variables (F-FMEA), we introduce an approach based on Fuzzy Multi Criteria Decision Making (F-MCDM) for effectively evaluating scenarios and initial failure hazards. Nevertheless, the results of ranking failure modes generated by different MCDM methods may vary. This study is a retrospective study that suggests a comprehensive unified risk assessment model, integrating multiple techniques to produce a more inclusive ranking of failure modes. Exploring a broad spectrum of risks associated with organ transplant operations, we identified 20 principal hazards with the assistance of literature and experts. We developed a questionnaire to examine the impact of various critical factors on the survival of transplanted organs, such as irregularities in immunosuppressive drug consumption, inappropriate dietary habits, psychological disorders, engaging in strenuous activities post-transplant, neglecting quarantine regulations, and other design-related factors. Subsequently, we analyzed the severity of their effects on the durability of transplanted organs. Utilizing the Mamdani algorithm as a fuzzy inference engine and the Center of Gravity algorithm for tooling, we expressed the probability and severity of each risk. Finally, the failure mode ranking obtained from the F-FMEA method, three fuzzy MCDM methods, and the proposed combined method were identified. Additionally, the results obtained from various methods were evaluated by an expert team, demonstrating that the highest consistency and effectiveness among different methods are attributed to the proposed method, as it achieved a 91.67% agreement with expert opinions.

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.

Process failure mode and effects analysis for external beam radiotherapy: Introducing a literature-based template and a novel action priority.

PURPOSE: The first aim of the study was to create a general template for analyzing potential failures in external beam radiotherapy, EBRT, using the process failure mode and effects analysis (PFMEA). The second aim was to modify the action priority (AP), a novel prioritization method originally introduced by the Automotive Industry Action Group (AIAG), to work with different severity, occurrence, and detection rating systems used in radiation oncology. METHODS AND MATERIALS: The AIAG PFMEA approach was employed in combination with an extensive literature survey to develop the EBRT-PFMEA template. Subsets of high-risk failure modes found through the literature survey were added to the template where applicable. Our modified AP for radiation oncology (RO AP) was defined using a weighted sum of severity, occurrence, and detectability. Then, Monte Carlo simulations were conducted to compare the original AIAG AP, the RO AP, and the risk priority number (RPN). The results of the simulations were used to determine the number of additional corrective actions per failure mode and to parametrize the RO AP to our department's rating system. RESULTS: An EBRT-PFMEA template comprising 75 high-risk failure modes could be compiled. The AIAG AP required 1.7 additional corrective actions per failure mode, while the RO AP ranged from 1.3 to 3.5, and the RPN required 3.6. The RO AP could be parametrized so that it suited our rating system and evaluated severity, occurrence, and detection ratings equally to the AIAG AP. CONCLUSIONS: An adjustable EBRT-PFMEA template is provided which can be used as a practical starting point for creating institution-specific templates. Moreover, the RO AP introduces transparent action levels that can be adapted to any rating system.

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.

Development and characterization of novel surface engineered Depofoam: a QbD coupled failure modes and effects analysis risk assessment-based optimization studies.

This study aimed to design and develop novel surface-engineered Depofoam formulations to extend the drug delivery to the prescribed time. The objectives are to prevent the formulation from burst release, rapid clearance by tissue macrophages, and instability and to analyze the impact of process and material variables in the characteristics of formulations. This work employed a quality-by-design coupled failure modes and effects analysis (FMEA)-risk assessment strategy. The factors for the experimental designs were chosen based on the FMEA results. The formulations were prepared by the double emulsification method followed by surface modification and characterized in terms of critical quality attributes (CQAs). The experimental data for all these CQAs were validated and optimized using the Box-Behnken design. A comparative drug release experiment was studied by the modified dissolution method. Furthermore, the stability of the formulation was also assessed. In addition, the impact of critical material attributes and critical process parameters on CQAs was evaluated using FMEA risk assessment. The optimized formulation method yielded high encapsulation efficiency (86.24 ± 0.69%) and loading capacity (24.13 ± 0.54%) with an excellent zeta potential value (-35.6 ± 4.55mV). The comparative in vitro drug release studies showed that more than 90% of the drug's release time from the surface-engineered Depofoam was sustained for up to 168 h without burst release and ensured colloidal stability. These research findings revealed that Depofoam prepared with optimized formulation and operating conditions yielded stable formulation, protected the drug from burst release, provided a prolonged release, and sufficiently controlled the drug release rate.

Patient transfers from emergency departments to other in-hospital areas: a failure mode and effects analysis. / Análisis modal de fallos y efectos en las transferencias de pacientes de urgencias a hospitalización.

OBJECTIVES: To perform an in-depth analysis of the process of transferring patients from an emergency department (ED) to other areas inside a hospital and identify possible points of failure and risk so that strategies for improvement can be developed. MATERIAL AND METHODS: We formed a multidisciplinary group of ED and other personnel working with hospitalized adults. The group applied failure mode and effects analysis (FMEA) to understand the in-hospital transfer processes. A risk priority scoring system was then established to assess the seriousness of each risk and the likelihood it would appear and be detected. RESULTS: We identified 8 transfer subprocesses and 14 critical points at which failures could occur. Processes related to administering medications and identifying patients were the components that received the highest risk priority scores. Improvement strategies were established for all risks. The group created a specific protocol for in-hospital transfers and a checklist to use during handovers. CONCLUSION: The FMEA method helped the group to identify points when there is risk of failure during patient transfers and to define ways to improve patient safety.

OBJETIVO: Este estudio analiza en profundidad el proceso de transferencia de pacientes de urgencias a hospitalización y posibles fallos para evitar problemas de seguridad mediante la identificación de líneas de mejora. METODO: Se conformó un grupo de trabajo multidisciplinar compuesto por profesionales asistenciales de urgencias y hospitalización de adultos que, mediante la metodología de análisis modal de fallos y efectos (AMFE), analizó pormenorizadamente el proceso de transferencia de pacientes de urgencias a hospitalización. Para los puntos críticos identificados se estableció el índice de prioridad del riesgo (IPR) en base a su gravedad, probabilidad de aparición y de detección. RESULTADOS: Se identificaron 8 subprocesos y 14 puntos críticos que podrían generar fallos en el proceso de transferencia. Los aspectos relacionados con la administración de medicamentos y el proceso de identificación fueron los que obtuvieron mayores puntuaciones de IPR. Para todos ellos se establecieron acciones de mejora. Se elaboró un procedimiento específico de transferencia de pacientes entre estas áreas y un listado de verificación de ingresos en hospitalización. CONCLUSIONES: Con la metodología AMFE se ha conseguido desgranar un proceso de especial vulnerabilidad como es la transferencia de pacientes de urgencias a hospitalización y definir acciones de mejora en aras de incrementar la seguridad de los pacientes.

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)

Fall risk management in interventional prenatal diagnosis perioperative pregnant women based on tracking methodology and failure mode and effect analysis application.

The aim was to explore the effectiveness of a tracing methodology combined with failure mode and effect analysis (FMEA) for managing falls in pregnant women during the perioperative period of interventional prenatal diagnosis. Using the tracing methodology, the process was evaluated and analyzed using FMEA after reviewing data, on-site interview, case tracking and on-site inspection, and improvement measures were proposed for the existing risk factors, and the fall-related quality indicators, satisfaction with fall-related health education, and risk priority number were compared before and after implementation. Effectiveness analysis for interventional prenatal diagnosis of perioperative maternal falls risk management resulted in a significant decrease in risk priority number (P < .01), a significant increase in the rate of correct fall risk identification and assessment, correct handover rate of pregnant women at risk of falls, correct intervention rate of pregnant women at high risk of falls, and effective coverage of falls-related health education (P < .01), a significant increase in satisfaction with falls-related health education (P < .001), and the incidence of falls among pregnant women decreased from 0.12% to 0%. The use of tracking methodology combined with FMEA can reduce the risk of perioperative maternal falls in interventional prenatal diagnosis and improve the safety of maternal visits.

Improving the quality of hospital sterilization process using failure modes and effects analysis, fuzzy logic, and machine learning: experience in tertiary dental centre.

Activities practiced in the hospital generate several types of risks. Therefore, performing the risk assessment is one of the quality improvement keys in the healthcare sector. For this reason, healthcare managers need to design and perform efficient risk assessment processes. Failure modes and effects analysis (FMEA) is one of the most used risk assessment methods. The FMEA is a proactive technique consisting of the evaluation of failure modes associated with a studied process using three factors: occurrence, non-detection, and severity, in order to obtain the risk priority number using fuzzy logic approach and machine learning algorithms, namely the support vector machine and the k-nearest neighbours. The proposed model is applied in the case of the central sterilization unit of a tertiary national reference centre of dental treatment, where its efficiency is evaluated compared to the classical approach. These comparisons are based on expert advice and machine learning performance metrics. Our developed model proved high effectiveness throughout the results of the expert's vote (she agrees with 96% fuzzy-FMEA results against 6% with classical FMEA results). Furthermore, the machine learning metrics show a high level of accuracy in both training data (best rate is 96%) and testing data (90%). This study represents the first study that aims to perform artificial intelligence approach to risk management in the Moroccan healthcare sector. The perspective of this study is to promote the application of the artificial intelligence in Moroccan health management, especially in the field of quality and safety management.

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.

Using Failure Mode and Effect Analysis to Identify Potential Failures in a Psychiatric Hospital Emergency Department.

OBJECTIVES: Failure mode and effect analysis (FMEA) is a powerful tool for accessing potential failures, but the participants are limited. It has not been used in psychiatric hospitals. Objectives were to implement FMEA in a psychiatric hospital and determine whether the FMEA process can be expanded by including participants who are familiar with the emergency department (ED) admission process and those who are not. METHODS: In this prospective, questionnaire-based study, a multidisciplinary team experienced in ED admissions was trained in FMEA and determined potential failures in the process. They developed a questionnaire regarding the failures, which were ranked by 17 ED and 28 non-ED healthcare providers. Risk priority numbers were calculated for each. RESULTS: By applying FMEA, we found 6 steps of the ED admission process, with 32 potential failures. Risk priority numbers ranged from 91 to 225. The most notable potential failure identified was during a patient's initial telephone call to the ED, before arrival. Emergency department and non-ED workers ranked 94% of the potential failures similarly. CONCLUSIONS: Failure mode and effect analysis can be implemented in psychiatric hospitals and can be a useful tool for anticipating potential failures. The number of participants in an FMEA can be increased to include those who are not directly involved in the process and should involve several specialists from diverse fields. Increasing the number of participants allows more detailed analyses. A checklist detailing the actions to take when processing a patient's initial phone call should be implemented to decrease hazards related to ED admissions.

[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.