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.

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.

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.

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.

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.

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.

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.

Validation plan of bone marrow collection, processing and distribution using the failure mode and effect analysis methodology: a technical report.

BACKGROUND AIMS: Bone marrow (BM) is commonly used in the pediatric and adult setting as a source of hematopoietic stem cells (HSCs). The standards of the Joint Accreditation Committee of the International Society for Cell & Gene Therapy & European Society for Blood and Marrow Transplantation (JACIE) include specific requirements regarding BM collection, processing and distribution. To run this process, each transplant team develops a series of JACIE-compliant procedures, customizing them with regard to local settings and paths. Moreover, JACIE standards require that transplant teams validate and periodically revise their procedures to keep the entire process under control. In this article, the authors describe the methodology adopted in our center to fulfill the aforementioned JACIE requirements. METHODS: The authors developed a validation plan based on the failure mode and effect analysis (FMEA) methodology. According to the FMEA approach, the authors carefully revised activities and procedures connected to BM collection, processing and distribution at our institution. The entire process was initially divided into five main phases (assessment of donor eligibility, perioperative autologous blood donation, preparation of BM collection kit, BM harvesting and BM processing and distribution), comprising 17 subphases and 22 activities. RESULTS: For each activity, one or more failure modes were identified, for a total of 28 failure modes, and a risk priority number (RPN) was then assigned to each failure mode. Although many procedures were validated, others were subjected to substantial changes according to the RPN rating. Moreover, specific indicators were identified for subsequent monitoring to contain the risk of failure of steps emerging as critical at FMEA. CONCLUSIONS: This is the first study describing use of the FMEA methodology within an HSC transplant program. Shaping the risk analysis based on local experience may be a trustworthy tool for identifying critical issues, directing strict monitoring of critical steps or even amending connected procedures. Overall, the FMEA approach enabled the authors to improve our process, checking its consistency over time.

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.

Failure Mode and Effects Analysis (FMEA) at the preanalytical phase for POCT blood gas analysis: proposal for a shared proactive risk analysis model.

OBJECTIVES: Proposal of a risk analysis model to diminish negative impact on patient care by preanalytical errors in blood gas analysis (BGA). METHODS: Here we designed a Failure Mode and Effects Analysis (FMEA) risk assessment template for BGA, based on literature references and expertise of an international team of laboratory and clinical health care professionals. RESULTS: The FMEA identifies pre-analytical process steps, errors that may occur whilst performing BGA (potential failure mode), possible consequences (potential failure effect) and preventive/corrective actions (current controls). Probability of failure occurrence (OCC), severity of failure (SEV) and probability of failure detection (DET) are scored per potential failure mode. OCC and DET depend on test setting and patient population e.g., they differ in primary community health centres as compared to secondary community hospitals and third line university or specialized hospitals. OCC and DET also differ between stand-alone and networked instruments, manual and automated patient identification, and whether results are automatically transmitted to the patient's electronic health record. The risk priority number (RPN = SEV × OCC × DET) can be applied to determine the sequence in which risks are addressed. RPN can be recalculated after implementing changes to decrease OCC and/or increase DET. Key performance indicators are also proposed to evaluate changes. CONCLUSIONS: This FMEA model will help health care professionals manage and minimize the risk of preanalytical errors in BGA.

Risk assessment of failure during transitioning from in-centre to home haemodialysis.

BACKGROUND: Introducing a de-novo home haemodialysis (HHD) program often raises safety concerns as errors could potentially lead to serious adverse events. Despite the complexity of performing haemodialysis at home without the supervision of healthcare staff, HHD has a good safety record. We aim to pre-emptively identify and reduce the risks to our new HHD program by risk assessment and using failure mode and effects analysis (FMEA) to identify potential defects in the design and planning of HHD. METHODS: We performed a general risk assessment of failure during transitioning from in-centre to HHD with a failure mode and effects analysis focused on the highest areas of failure. We collaborated with key team members from a well-established HHD program and one HHD patient. Risk assessment was conducted separately and then through video conference meetings for joint deliberation. We listed all key processes, sub-processes, step and then identified failure mode by scoring based on risk priority numbers. Solutions were then designed to eliminate and mitigate risk. RESULTS: Transitioning to HHD was found to have the highest risk of failure with 3 main processes and 34 steps. We identified a total of 59 areas with potential failures. The median and mean risk priority number (RPN) scores from failure mode effect analysis were 5 and 38, with the highest RPN related to vascular access at 256. As many failure modes with high RPN scores were related to vascular access, we focussed on FMEA by identifying the risk mitigation strategies and possible solutions in all 9 areas in access-related medical emergencies in a bundled- approach. We discussed, the risk reduction areas of setting up HHD and how to address incidents that occurred and those not preventable. CONCLUSIONS: We developed a safety framework for a de-novo HHD program by performing FMEA in high-risk areas. The involvement of two teams with different clinical experience for HHD allowed us to successfully pre-emptively identify risks and develop solutions.

Combining failure modes and effects analysis and cause-effect analysis: a novel method of risk analysis to reduce anaphylaxis due to contrast media.

BACKGROUND: Contrast media agents are essential for computed tomography (CT)-based diagnoses. However, they can cause fatal adverse effects such as anaphylaxis in patients. Although it is rare, the chances of anaphylaxis increase with the number of examinations. OBJECTIVE: We aimed to design a quality improvement initiative to reduce patient risk to contrast media agents. METHODS: We analysed CT processes using contrast iodine in a tertiary-care academic hospital that performs approximately 14 000 CT scans per year in Japan. We applied a combination of failure modes and effects analysis (FMEA) and cause-effect analysis to reduce the risk of patients developing allergic reactions to iodine-based contrast agents during CT imaging. RESULTS: Our multidisciplinary team comprising seven professionals analysed the data and designed a 56-process flowchart of CT imaging with iodine. We obtained 177 failure modes, of which 15 had a risk-probability number higher than 100. We identified the two riskiest processes and developed cause-and-effect diagrams for both: one was related to the exchange of information between the radiation and hospital information system regarding the patient's allergy, the other was due to education and structural deficiencies in observation following the exam. CONCLUSION: The combined method of FMEA and cause-and-effect analysis reveals high-risk processes and suggests measures to reduce these risks. FMEA is not well-known in healthcare but has significant potential for improving patient safety. Our findings emphasise the importance of adopting new techniques to reduce patient risk and carry out best practices in radiology.

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.

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.

Failure modes and effects analysis for surface-guided DIBH breast radiotherapy.

Despite breast cancer prevalence and widespread adoption of deep inspiration breath-hold (DIBH) radiation techniques, few data exist on the error risks related to using surface-guided (SG) DIBH during breast radiation therapy (RT). Due to the increasingly technical nature of these methods and being a paradigm shift from traditional breast setups/treatments, the associated risk for error is high. Failure modes and effects analysis (FMEA) has been used in identifying risky RT processes yet is time-consuming to perform. A subset of RT staff and a hospital patient-safety representative performed FMEA to study SG-DIBH RT processes. After this group (cohort 1) analyzed these processes, additional scoring data were acquired from RT staff uninvolved in the original FMEA (cohort 2). Cohort 2 received abbreviated FMEA training while using the same process maps that cohort 1 had created, which was done with the goal of validating our results and exploring the feasibility of expedited FMEA training and efficient implementation elsewhere. An extensive review of the SG-DIBH RT process revealed 57 failure modes in 16 distinct steps. Risks deemed to have the highest priority, large risk priority number (RPN), and severity were addressed with policy changes, checklists, and standardization; of these, most were linked with operator error via manual inputs and verification. Reproducibility results showed that 5% of the average RPN between cohorts 1 and 2 was statistically different. Unexpected associations were noted between RPN and RT staff role; 12% of the physicist and therapist average scores were statistically different. Different levels of FMEA training yielded similar scoring within one RT department, suggesting a time-savings can be achieved with abbreviated training. Scores between professions, however, yielded significant differences suggesting the importance of involving staff across disciplines.

Application of failure mode and effects analysis (FMEA) to improve medication safety in the dispensing process - a study at a teaching hospital, Sri Lanka.

BACKGROUND: Failure mode and effects analysis (FMEA) is a prospective, team based, structured process used to identify system failures of high risk processes before they occur. Medication dispensing is a risky process that should be analysed for its inherent risks using FMEA. The objective of this study was to identify possible failure modes, their effects, and causes in the dispensing process of a selected tertiary care hospital using FMEA. METHODS: Two independent teams (Team A and Team B) of pharmacists conducted the FMEA for two months in the Department of Pharmacy of a selected teaching hospital, Colombo, Sri Lanka. Each team had five meetings of two hours each, where the dispensing process and sub processes were mapped, and possible failure modes, their effects, and causes, were identified. A score for potential severity (S), frequency (F) and detectability (D) was assigned for each failure mode. Risk Priority Numbers (RPNs) were calculated (RPN=SxFxD), and identified failure modes were prioritised. RESULTS: Team A identified 48 failure modes while Team B identified 42. Among all 90 failure modes, 69 were common to both teams. Team A prioritised 36 failure modes, while Team B prioritised 30 failure modes for corrective action using the scores. Both teams identified overcrowded dispensing counters as a cause for 57 failure modes. Redesigning of dispensing tables, dispensing labels, the dispensing and medication re-packing processes, and establishing a patient counseling unit, were the major suggestions for correction. CONCLUSION: FMEA was successfully used to identify and prioritise possible failure modes of the dispensing process through the active involvement of pharmacists.

Healthcare failure mode and effect analysis in the chemotherapy preparation process.

PURPOSE: To conduct a Health Care Failure Mode and Effects Analysis (HFMEA) of the chemotherapy preparation process to identify the steps with the potential to cause errors, and to develop further strategies to improve the process and thus minimize the risk of errors. METHODS: An HFMEA was conducted to identify and reduce preparation errors during the chemotherapy preparation process. A multidisciplinary team mapped the preparation process, formally identified all the steps, and then conducted a brainstorming session to determine potential failure modes and their potential effects. A severity and probability score for each failure mode, a hazard score (HS) and a total HS were calculated. A hazard analysis was conducted for each HS equal to or more than 8. Finally, an action plan was identified for each failure mode. After the action plan was implemented, failure modes were revaluated and a new HS score was calculated as well as the percentage decrease in risk. RESULTS: The team identified five main steps in the chemotherapy preparation process and nine potential failure modes. After implementing the control measures, all the HSs decreased. The total HS associated with the chemotherapy preparation process decreased from 54 to 26 (-52%). This reduction in the total HS was mainly achieved by updating the Standard Operating Procedures (SOPs) and implementing bar code and gravimetric control system. CONCLUSION: The application of HFMEA to the chemotherapy preparation process in centralized chemotherapy units can be very useful in identifying actions aimed at reducing errors in the healthcare setting.

Application of Failure Mode and Effect Analysis (FMEA) to improve medication safety: a systematic review.

Medication safety is a phenomenon of interest in most healthcare settings worldwide. Failure Mode and Effect Analysis (FMEA) is a prospective method to identify failures. We systematically reviewed the application of FMEA in improving medication safety in the medication use process. Electronic databases were searched using keywords ((failure mode and effect analysis) AND (pharmacy OR hospital)). Articles that fulfilled prespecified inclusion criteria were selected and were then screened independently by two researchers. Studies fulfilling the inclusion criteria and cited in articles selected for the study were also included. Selected articles were then analysed according to specified objectives. Among 27€706 articles obtained initially, only 29 matched the inclusion criteria. After adding four cited articles, a total of 33 articles were analysed. FMEA was used to analyse both existing systems and new policies before implementing. All participants of FMEA reported that this process was an effective group activity to identify errors in the system, although time-consuming and subjective.

Algorithmic prediction of failure modes in healthcare.

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

Evaluation of failure modes and effect analysis for routine risk assessment of lung radiotherapy at a UK center.

PURPOSE: Explore the feasibility of adopting failure modes and effects analysis (FMEA) for risk assessment of a high volume clinical service at a UK radiotherapy center. Compare hypothetical failure modes to locally reported incidents. METHOD: An FMEA for a lung radiotherapy service was conducted at a hospital that treats ~ 350 lung cancer patients annually with radical radiotherapy. A multidisciplinary team of seven people was identified including a nominated facilitator. A process map was agreed and failure modes identified and scored independently, final failure modes and scores were then agreed at a face-to-face meeting. Risk stratification methods were explored and staff effort recorded. Radiation incidents related to lung radiotherapy reported locally in a 2-year period were analyzed to determine their relation to the identified failure modes. The final FMEA was therefore a combination of prospective evaluation and retrospective analysis from an incident learning system. RESULTS: Thirty-six failure modes were identified for the pre-existing clinical service. The top failure modes varied according to the ranking method chosen. The process required 30 h of combined staff time. Over the 2-year period chosen, 38 voluntarily reported incidents were identified as relating to lung radiotherapy. Of these, 13 were not predicted by the identified failure modes, with six relating to delays in the process, three issues with appointment times, one communication error, two instances of a failure to image, and one technical fault deemed unpredictable by the manufacturer. Four additional failure modes were added to the FMEA following the incident analysis. CONCLUSION: FMEA can be effectively applied to an established high volume service as a risk assessment method. Facilitation by an individual familiar with the FMEA process can reduce resource requirement. Prospective evaluation of risks should be combined with an incident reporting and learning system to produce a more comprehensive analysis of risk.