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.

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

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. Método: 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. (AU)

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. 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. (AU)

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.

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.

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.

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.

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.

Reducing perioperative red blood cell unit issue orders, returns, and waste using failure modes and effects analysis.

BACKGROUND: Surgical transfusion has an outsized impact on hospital-based transfusion services, leading to blood product waste and unnecessary costs. The objective of this study was to design and implement a streamlined, reliable process for perioperative blood issue ordering and delivery to reduce waste. STUDY DESIGN AND METHODS: To address the high rates of surgical blood issue requests and red blood cell (RBC) unit waste at a large academic medical center, a failure modes and effects analysis was used to systematically examine perioperative blood management practices. Based on identified failure modes (e.g., miscommunication, knowledge gaps), a multi-component action plan was devised involving process changes, education, electronic clinical decision support, audit, and feedback. Changes in RBC unit issue requests, returns, waste, labor, and cost were measured pre- and post-intervention. RESULTS: The number of perioperative RBC unit issue requests decreased from 358 per month (SD 24) pre-intervention to 282 per month (SD 16) post-intervention (p < .001), resulting in an estimated savings of 8.9 h per month in blood bank staff labor. The issue-to-transfusion ratio decreased from 2.7 to 2.1 (p < .001). Perioperative RBC unit waste decreased from 4.5% of units issued pre-intervention to 0.8% of units issued post-intervention (p < .001), saving an estimated $148,543 in RBC unit acquisition costs and $546,093 in overhead costs per year. DISCUSSION: Our intervention, designed based on a structured failure modes analysis, achieved sustained reductions in perioperative RBC unit issue orders, returns, and waste, with associated benefits for blood conservation and transfusion program costs.

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.

Failure mode and effect analysis applied to improve the medication management process in a pharmacy of a teaching hospital and a proposal for a simplified rating system.

BACKGROUND: Healthcare is not as safe as it should be and medication error remains a significant source of preventable morbidity and mortality among patients. OBJECTIVES: To present a failure mode and effect analysis (FMEA) of the medication management process in the pharmacy of the largest teaching hospital in Tunisia. Secondly, to examine the validity of a proposed simplified risk rating method by comparing the degree of concordance with the FMEA rating system in classifying failure modes related to the studied process. METHODS: The FMEA method was applied to the medication management process in the pharmacy for 5 months from January 2020. For the traditional FMEA rating system, failure modes were prioritised according to the risk priority number, which considers severity, occurrence and non-detectability. Failure modes were classified for the traditional method considering three categories: accepted, requiring control and critical. The proposed rating system was based on two indices: the number of parts, which reflected severity, and the number of causes according to the 5M method (manpower, machines, material, methods and medium), which reflected occurrence. Failure modes were classified for the proposed method considering three categories: low, medium and high. Failure modes were independently analysed to determine the degree of agreement in ranking of risk between the two studied methods. Prioritised failure modes were targeted by decisions and solutions aiming to reduce risk and enhance safety. RESULTS: Twenty-four failure modes were identified for the six-step process of medication management in a pharmacy (overall criticality=2607). The most critical failure modes were: data error in drugs reception (risk priority number (RPN)=432), break in the cold chain (RPN=320) and non-optimal pharmaceutical analysis (RPN=280). A good agreement was found between the classic FMEA and the proposed rating methods (κ=0.795). A high correlation was shown between the two scorings (r=0.785). Three failure modes were underestimated by the proposed rating method. CONCLUSIONS: An FMEA study on the medication management process in a teaching pharmacy showed that FMEA is an effective, proactive risk assessment that enables a better understanding of the studied process. The proposed risk scoring permits a good concordance with the classic method, with the advantage of being fast. Targeting the identified risks will allow integration into a continuous process of improvement and increase patient safety.

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.

Evaluation and Enhancement of a Comprehensive Controlled Substances Management Process at an Academic Medical Center.

PURPOSE: Controlled substances management is highly regulated, and requires institutions to have processes in place to maintain a closed-loop. This study was conducted to comprehensively evaluate the current state of controlled substances management, propose optimization opportunities, and implement steps to align the medication use process (MUP) to a defined desired state. METHODS: This evaluation was conducted in 2 phases. In phase 1, the current state of controlled substances management was assessed in order to develop a gap analysis tool and failure mode and effects analysis (FMEA). In phase 2, a work group was assembled to address opportunities within the FMEA. The work group prioritized opportunities using the risk priority number (RPN), and formulated action steps to align processes with the defined desired state. RESULTS: Through the literature evaluation, a desired state, consisting of 86 segments, was defined and compared with a gap analysis tool. Direct observation of the MUP allowed for development of 13 process maps depicting current state. Of the 86 segments, it was determined the study institution had a compliance rate of 62%. The remaining 38% correlated with 55 actionable process opportunities that were included in the FMEA. To date, 31 of the 55 (56%) opportunities have been successfully addressed by the work group. CONCLUSION: Use of direct observation to formulate a gap analysis tool and FMEA is an effective modality to evaluate controlled substances processes. These tools allow for pharmacy departments to identify and prioritize opportunities to optimize controlled substances management within an academic medication center.

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.