Healthcare Failure Mode And Effect Analysis Research Articles

The application of healthcare failure mode and effect analysis in reducing medication errors in the emergency department of Regional Public Hospital

Introduction: Medication or medication error is an essential issue in the world of health, both in the world and in Indonesia. The emergency department is a health service unit that provides emergency services 24 hours non-stop, which is very likely for medication errors to occur. Most medication incident errors can be prevented. One method that can be used is of healthcare failure mode and effect analysis (HFMEA). Applying HFMEA in hospitals helps avoid and identify possible or potential problems or errors in healthcare services. Methods: This qualitative study used the HFMEA approach to conduct risk analysis regarding medication events and errors in the emergency department (ER), at Regional Public Hospital, Central Java. Brainstorming and focus group discussion were conducted with all members of the HFMEA team at the regional public hospitals’ ER in Central Java following HFMEA guidelines. Results: Four process flows in ER drug or medication administration services were identified, encompassing 14 service sub-processes and 57 failure modes. Of the 57 failure modes, only 34 were subjected to hazard analysis, and only 24 failure modes required control measures. From these data, four control strategies were obtained. Conclusion: This study found 24 failure modes need to be addressed and suggested four control strategies, including enterprise risk management formats according to the needs of ER’ health workers, improving ER service management, improving standard operational procedures for medical services, and improving pharmacy management.

Proactive patient safety: enhancing hospital readiness through simulation-based clinical systems testing and healthcare failure mode and effect analysis

BackgroundRecognizing and identifying latent safety threats (LSTs) before patient care commences is crucial, aiding leaders in ensuring hospital readiness and extending its impact beyond patient safety alone. This study evaluated the effectiveness of a combination of Simulation-based Clinical Systems Testing (SbCST) with Healthcare Failure Mode and Effect Analysis (HFMEA) with regard to mitigating LSTs within a newly constructed hospital.MethodsThree phases of the combined SbCST and HFMEA approach were implemented across all hospital settings. The scenarios tested system functionalities, team responses, and resource availability. The threats thus identified were categorized into system-related issues, human issues, and resource issues, after which they were prioritized and addressed using mitigation strategies. Reassessment confirmed the effectiveness of these strategies before hospital commissioning.ResultsMore than 76% of the LSTs were mitigated through the combined approach. System-related issues, such as nonfunctional communication devices and faulty elevators, were addressed by leadership. Human issues such as miscommunication and nonadherence to hospital policy led to improvements in interprofessional communication and teamwork. Resource issues, including missing equipment and risks of oxygen explosion, were addressed through procurement, maintenance, and staff training for equipment preparation.ConclusionThe SbCST and HFMEA were highly effective with regard to proactively identifying and mitigating LSTs across all aspects of hospital preparedness. This systematic and comprehensive approach offers a valuable tool for enhancing patient safety in new healthcare facilities, thereby potentially setting a new standard for proactive hazard identification and risk management in the context of healthcare construction and commissioning.

Translation and cultural adaptation of the HFMEA into European Portuguese

ABSTRACT Introduction Health organizations are faced with daily challenges, requiring them to provide a quality service, ensuring effectiveness and efficiency. Risk management is one of the conditioning factors to achieve this purpose, ensuring preventive actions for all processes, promoting the identification of risk to the mitigation of its consequences for the patient, professionals, or organization. Patient safety is a priority and healthcare organizations should be concerned with the implementation of methodologies and tools to promote risk management, such as Healthcare Failure Mode and Effects Analysis (HFMEA). HFMEA has a high potential for risk management in healthcare organizations, with a proactive, prospective, and continuous approach to improvement. Objective Translation and adaptation of the HFMEA instrument into European Portuguese. Materials and methods A methodological study was carried out based on the proposal presented by Beaton and followed the recommendations of the International Test Commission and World Health Organization. Results The HFMEA 2021 was linguistically translated and culturally adapted to the new context ensuring reliability, content validity was assured by a group of experts, which ensured semantic, idiomatic, experimental, and conceptual equivalence between the original instrument and the translation. Conclusions HFMEA 2021 was successfully translated and adapted to European Portuguese, allowing its application.

Medication Error Prevention Using Healthcare Failure Mode And Effect Analysis At Clinical Pharmacy Installation

Medication error is the second most common patient safety incident worldwide. Medication errors can be defined as unintentional failures in medication services that have the potential to cause harm to patients. Maintaining safe health services is highly dependent on the ability of service providers to proactively conduct patient safety risk analysis, one of which is using the Healthcare Failure Mode and Effect Analysis (HFMEA) method. This study was aimed at obtaining the HFMEA design as an effort to prevent medication errors at Company X Clinical Pharmacy Installation. The research method used is qualitative research with a specific type of Operations Research. Data collected by in-depth interviews, observation, secondary data analysis, and focus group discussion. The results of this study found factors that cause medication errors to occur from organizational factors and staff factors. These results then analyzed for the HFMEA design which obtained 11 risks that required attention and then 14 action plans are made to overcome them. This study successfully developed HFMEA design to prevent medication error in Company X Clinical Pharmacy Installation.

Healthcare Failure Mode and Effect Analysis in Surgery Setting: A Bibliometrics Analysis and Literature Review.

In the ever-evolving healthcare landscape, the imperative to ensure patient safety has driven the adoption of robust safety approaches. One of the most popular methods is Healthcare Failure Mode and Effect Analysis (HFMEA). This study aims to conduct a bibliometric analysis of HFMEA research in a surgery setting to identify relevant authors, topics, and journals, highlighting the scholarly collaboration trends in this area. This analysis can provide valuable insights into the current state of research, emerging trends, and future research directions. The data was acquired online from the PubMed database on December 16th, 2023. The downloaded data were analyzed using Google Sheets to predict trends. The publication output was then analyzed using VOSviewer. Subsequently, Biblioshiny was used to analyze the characteristics of the study, productive author, institution, and country; thematic map analysis; and topic timeline analysis. This study analyzes 25 documents spanning 2005 to 2022, reflecting a collaborative effort involving 155 contributors. The upward trend in annual scientific publications, notably the surge from 2014 onwards and significant peaks in 2019 and 2022, underscores the growing recognition of HFMEA in enhancing patient safety within surgical environments. The prominent influence from prolific authors like Ahmed K, leading institutions such as King's College London, and the substantial contributions from Norway and the USA in shaping the HFMEA research landscape. This study contributes valuable insights that can shape the trajectory of HFMEA in the evolving landscape of surgical healthcare, ensuring enhanced patient safety and quality improvement.

Effect Analysis of the HFMEA Model Applied in Sputum Specimen Management Among Patients with Tuberculosis.

Accurate diagnosis is very important to block the transmission of tuberculosis. The quality of sputum culture affects the diagnostic accuracy. The quality of sputum samples is not optimistic. Therefore, this study investigated whether health care failure mode and effect analysis (HFMEA) can improve specimen quality and detection efficiency in sputum specimen management in tuberculosis departments. This study is a non-randomized controlled trial study. A convenience sampling method was used to select 110 patients who visited the Department of Tuberculosis of the Second Hospital of Nanjing from September to November 2022 and December 2022 to February 2023 as the control group and the experimental group. Control groups followed standard operating procedures for sputum specimen collection. In the experimental group, HFMEA model was used to control the quality on this basis. After 3 months of intervention, the qualified rate and positive detection rate of sputum samples were compared between the two groups. A total of 634 sputum specimens were included in the experimental group and 647 in the control group. Compared with the control group, the qualification rate of sputum specimens was higher in the experimental group (84.54% vs 79.13%); the positive detection rates of the X-Pert assay (27.88% vs 16.19%), sputum culture (20.29% vs 12.68%), and sputum smear (22.29% vs 15.81%) were all higher in the experimental group (all P < 0.05). Patients in the experimental group had higher knowledge mastery and nurse sputum sample management scores (P < 0.05). However, patient satisfaction with sputum specimen management in the experimental group was lower than in the control group (7.72 ± 0.74 vs 8.38 ± 0.85, P < 0.001). The application of the HFMEA model in sputum specimen management can effectively improve specimen quality and positive detection rates.

The Effect of Nursing Intervention Based on HFMEA Model on the Prevention of Radiation Dermatitis: A Randomized Controlled Study

Radiationdermatitis is one of the most common complications in patients received radiotherapy. Previous studies shown that incidence of radiation induced dermatitis (RD) is 95%. HFMEA (Healthcare Failure Mode and Effect Analysis) is a new model of nursing management. Many studies reported that HFMEA could reduce incidence of complications and adverse events, and significantly improve patient satisfaction. From March 2023 to October 2022, patients received radiotherapy in our center were recruited in this study. Participants were randomly divided into control group (N = 60) and observation group (N = 60) in a ratio of 1:1. Patients in the control group were given routine skin care, health education before radiotherapy and skin care manual during radiotherapy. Based on the standard care, a HFMEA-based nursing intervention was adopted in the observation group by the following methods: Set up HFMEA care team. HFMEA team is composed of head nurses, wound specialist nurses, psychotherapists, etc. The members of the group searched the literature, case analysis and brainstorming to find out the potential failure reasons in every process of nursing radiotherapy patients in the past. Implementation of targeted measures to improve the corresponding: all patients with radiation dermatitis baseline assessment, focus on high-risk patients to shift, regular guidance medication, responsible nurses check the management of patients' skin every day to strengthen the attention of medical staff and patients' family members to radiation dermatitis. The degree of skin injury, pain and psychological status were compared between the two groups at the end of radiotherapy. A total of 120 patients were enrolled in this study. Grade 1 RD was the most common in the observation group at the end of radiotherapy, and Grade 2 and 3 skin RD were less in the observation group than in the control group. There was not significant difference in the scores of SAS and SDS between the two groups before and after nursing (P > 0.05). In the observation group, the SAS scores (44.10±11.25 vs. 32.29 ± 7.72, P = 0.016) and SDS scores (40.98 ± 9.12 vs. 30.11 ± 5.23, P = 0.013) were significantly higher than the control group after nursing(P<0.05). The scores of SAS and SDS in the Observation Group were significantly lower than those in the control group. The scores of SAS in the Observation Group VS the control group were 32.29 ± 7.72 VS 39.09 ± 9.37 after nursing, SDS scores of Observation Group VS control group (30.11 ± 5.23 VS 38.76 ± 7.52, P<0.05). The visual analogue scale (Vas) score in the observation group was significantly lower than that in the control group (2.37 ± 0.45 VS 4.02 ± 0.53, P & Lt; 0.001), and the satisfaction degree of patients in the observation group was significantly higher than that in the control group (96.67% VS 80%, p = 0.004). HFMEA model could effectively reduce incidence of RD, eliminate negative emotion, relieve pain and improve nursing satisfaction.

Applying the healthcare failure mode and effects analysis approach to improve the quality of an organised colorectal cancer screening programme.

The first level of a colorectal cancer (CRC) screening process was systematically analysed using the Healthcare Failure Mode and Effects Analysis (HFMEA) approach by a multidisciplinary team aiming to improve the programme quality. The study was conducted at the Local Health Authority of Bologna, Northern Italy. Seven brainstorming sessions were conducted and all the activities performed were recorded on a FMEA worksheet consisting of individual records reporting the specific phases of the analysed process along with associated activities, possible failure modes, their causes and effects, the obtained risk priority numbers (RPNs) and the control measures to plan. Twenty-three failure modes, 14 effects and 12 possible causes were identified. Nine failure modes were prioritised according to the RPN obtained; most resulted in possible false-negative faecal immunochemical test (FIT) results (66.7%), followed by sample loss (22.2%) and not reaching the entire target population (11.1%). This leads to 66.7% of corrective/preventive actions being applied to the phase of returning the stool sample by the citizen. For this phase reorganisation, the local pharmacies were involved not only as FIT kit delivery points but also as specimen collection and sending points to the laboratory. These organisational changes allowed the introduction of complete traceability of kits and specimens flow, as well as temperature control. A re-evaluation of the prioritised failure modes 6 months after launching the implemented screening process showed that HFMEA application decreased the risk of potential errors by 75.9%. HFMEA application in CRC screening programme is a useful tool to reduce potential errors.

Identifying Failure Modes in Telemedicine: An Instructional Needs Assessment.

Technology failures in telehealth are common, and clinicians need the skills to diagnose and manage them at the point of care. However, there are issues beyond technology failures mediating the effective use of telehealth. We must teach best-practice procedures for conducting telemedicine visits and include in instructional simulations commonly encountered failure modes so students can build their skills. To this end, we recruited medical students to conduct a Healthcare Failure Modes and Effects Analysis (HFMEA) to predict failures in telemedicine, their potential causes, and the consequences to develop and teach prevention strategies. Sixteen students observed telehealth appointments independently. Based on their observations, we identified four categories of failures in telemedicine: technical issues, patient safety, communication, and social and structural determinants. We proposed a normalized workflow that included management and prevention strategies. Our findings can inform the creation of new curricula.

Mitigating the risk of COVID-19 infection in the operating room: Using healthcare failure mode and effect analysis

The process of caring for patients requiring surgery exposes them to a high risk of COVID-19 infection during the pre-operative, operative, and post-operative phases, as these procedures are known to be modes of transmission for the virus. We conducted this study to prevent the risk of COVID-19 infection by identifying potential failure modes during the patient care process, determining critical activities, and defining mitigation measures to reduce transmission. Through a quality and a priori risk management method, which is Healthcare Failure Mode and Effect Analysis (HFMEA), applied in the patient care process in the Central Operating Room of Mohammed VI University Hospital in Morocco. We identified 38 potential failure modes in the patient care process during the three phases (preoperative, operative, and postoperative) that could increase the risk of COVID-19 infection. Of these, 61% are classified as critical, and we have identified all their possible causes. To reduce the risk of transmission, we have proposed 16 mitigation actions. The use of HFMEA has been effective in the new pandemic context, improving patient safety during the care process in the operating room and reducing the risk of COVID-19 infection.

Bibliometric Analysis of Healthcare Failure Mode and Effect Analysis Research

Healthcare Failure Mode and Effect Analysis (HFMEA) has been widely used as a proactive risk assessment for healthcare processes since its development in 2001. This study performs a bibliometric analysis of published papers on HFMEA to map its utilization and evolutionary nuances. The dataset was obtained from the Scopus database for English-language papers published between 2002 and 2022 using “HFMEA” or “Healthcare Failure Mode and Effect Analysis” in the article title, abstract, or keywords. Microsoft Excel and VOSviewer were used to perform the analysis. The authors found 230 relevant publications during the study period. The year 2020 has the most publications, with 34 papers. Articles written by DeRosier et al. received the most citations (470). The United States had the highest number of published papers (84), while the United Kingdom had the most international collaboration. Co-occurrence analysis of author keywords showed that patient safety, risk assessment, and risk management were high-rank words. This study emphasizes scholarly collaboration trends and identifies relevant authors, topics, and journals on HFMEA research. It may assist health practitioners and healthcare facility managers in understanding the focus of HFMEA research. Nonetheless, the number of HFMEA publications is still sparse. Further studies on HFMEA in underrepresented medical specialties should be explored.

Overview of Failure Mode and Effects Analysis (FMEA): A Patient Safety Tool.

Overview of Failure Mode and Effects Analysis (FMEA): A Patient Safety Tool.

Applying the Healthcare Failure Mode and Effect Analysis (HFMEA) Method for the Risk Management of Self-Developed Electronic Medical Records (EMRs) in Hospitals

The organization of electronic medical records (EMR) in hospitals is vital. In cases where EMRs are developed independently, failures that hamper the hospital's operations could sometimes occur. Risk management is needed to identify, reduce, and eliminate potential failures. This study aimed to analyze a risk management by using the Healthcare Failure Mode and Effects Analysis (HFMEA) method to reduce the risk of failure in the outpatient EMR development process at Baitul Hikmah General Hospital (RSBH). This observational research applied an action research design and was conducted using the HFMEA method. It analyzed the outpatient EMR development process at RSBH. Research data was obtained through a mixed method where information was collected from focus group discussions (FGD) and calculated risk priority numbers (RPN). The research subjects were determined by the purposive sampling technique (HFMEA team). The HFMEA form was used as the research instrument. At least 33 failure modes were identified. These failure modes were evenly distributed throughout the outpatient EMR development process at RSBH. The top five failure modes received treatment actions and their outcomes were measured. Four out of the five failure modes showed a significant decrease in RPN value. In short, risk management analysis using the FMEA method could significantly reduce the potential for failure in the outpatient EMR development process at RSBH

Using Healthcare Failure Mode and Effect Analysis in prospective medication safety risk management in secondary care inpatient wards

ObjectivesThe evaluation and improvement of medication management processes is an essential part of preventive medication risk management strategies in hospitals. The aim of the present study was to identify and...

Nursing risk management of COVID-19 pneumonia isolation wards using HFMEA method

To explore the application effect of Health care failure mode and effect analysis (HFMEA) in nursing risk management in COVID-19 pneumonia isolation ward, the risk management of nursing in isolation wards was evaluated by HFMEA method. According the six highest risk factor score, the improvement measures were formulated to intervene, and then nurse satisfaction was checked. After using HFMEA method, Risk Priority Number (RPN) of key indicators was lower than 18 points, the operating compliance rate was increased from 87 5% up to 98. 6%, and the satisfaction of nursing-related indicators was better than that of pre-implementation (P&lt;0. 001). In a conclusion, applying HFMEA to the risk management of isolation wards in the early stage of the COVD-19 pneumonia epidemic can optimize the nursing management process, improve the quality of nursing care, reduce the safety risk and improve the job satisfaction of nurses.

Development and validation of patients\u2019 surgical safety checklist

BackgroundPoor uptake and understanding of critical perioperative information represent a major safety risk for surgical patients. Implementing a patient-driven surgical safety checklist might enhance the way critical information is given and increase patient involvement in their own safety throughout the surgical pathway. The aim of this study was to develop and validate a Surgical Patient Safety Checklist (PASC) for use by surgical patients.MethodThis was a prospective study, involving patient representatives, multidisciplinary healthcare professionals and elective surgical patients to develop and validate PASC using consensus-building techniques in two Norwegian hospitals. A set of items intended for PASC were rated by patients and then submitted to Content Validation Index (CVI) analyses. Items of low CVI went through a Healthcare Failure Mode and Effect Analysis (HFMEA) Hazard Scoring process, as well as a consensus process before they were either kept or discarded. Reliability of patients’ PASC ratings was assessed using Intraclass Correlation Coefficient analysis. Lastly, the face validity of PASC was investigated through focus group interviews with postoperative patients.ResultsInitial development of PASC resulted in a checklist consisting of two parts, one before (32 items) and one after surgery (26 items). After achieving consensus on the PASC content, 215 surgical patients from six surgical wards rated the items for the CVI analysis on a 1-4 scale and mostly agreed on the content. Five items were removed from the checklist, and six items were redesigned to improve PASCs’ user-friendliness. The total Scale-level index/Average (S-CVI/Ave) before revision was 0.83 and 0.86 for pre- and post-operative PASC items, respectively. Following revision, these increased to 0.86 and 0.93, respectively. The PASC items reliability score was 0.97 (95% confidence interval 0.96 to 0.98). The qualitative assessment identified that patients who used PASC felt more in control of their situation; this was achieved when PASC was given to them at what they felt was the right time and healthcare professionals took part in its usage.ConclusionMultidisciplinary perioperative care staff and surgical patients agreed upon PASC content, the checklist ratings were reliable, and qualitative assessment suggested good face validity. PASC appears to be a usable and valid checklist for elective surgical patients across specialties.

A Simulation Systems Testing Program Using HFMEA Methodology Can Effectively Identify and Mitigate Latent Safety Threats for a New On-Site Helipad.

Fundamental changes in critical systems within hospitals present safety risks. Some threats can be identified prospectively, others are only uncovered when the system goes live. Simulation and Healthcare Failure Mode and Effect Analysis (HFMEA) can be used together to prospectively test a system without endangering patients. The research team combined iterative simulations and HFMEA methodologies to conduct simulation-based clinical systems testing (SbCST) to detect and mitigate latent safety threats (LSTs) prior to opening a hospital helipad. This study was conducted in three phases. In Phase I, an interprofessional team created a process map and conducted a tabletop exercise, identifying LSTs that could theoretically occur during patient transfer from the new helipad. Using HFMEA methodology, steps predicted to be affected by the new helipad were probed. Identified LSTs were assigned a hazard score. Mitigation solutions were proposed. Results from Phase I were used to plan Phase II, which used low-fidelity simulation to test communication processes and travel paths. High-fidelity simulation was used in Phase III to test previously identified LSTs. Over three testing phases, 31 LSTs were identified: 15 in Phase I, 7 in Phase II, and 9 in Phase III. LSTs fell under the categories of care coordination, facilities, and equipment, and devices. Eighteen (58.1%) were designated "critical" (hazard score ≥ 8). A three-phase SbCST program using HFMEA methodology was an effective tool to identify LSTs. An iterative approach, using results of each phase to inform the structure of the next, facilitated testing of proposed mitigation strategies.

The Healthcare Failure Mode and Effect Analysis as a tool to evaluate care protocols.

to identify, classify, and analyze modes of failure in the medication process. evaluative research that used the Healthcare Failure Mode and Effect Analysis (HFMEA) in a service of bone marrow transplant from June to September 2018, with the participation of 35 health workers. 207 modes of failure were identified and classified as mistakes in verification (14%), scheduling (25.6%), administration (29%), dilution (16.4%), prescription (2.4%), and identification (12.6%). The analysis of risk showed a moderate (51.7%) and high (30.9%) need of intervention, leading to the creation of an internal quality assurance group and of continued education activities. the Healthcare Failure Mode and Effect Analysis showed itself to be a tool to actively identify, classify, and analyze failures in the process of medication, contributing for the proposal of actions aimed at patient safety.

Development of On-Site Medical System for Mass-Gathering Events During TOKYO 2020: Vulnerability Analysis Using Healthcare Failure Mode and Effect Analysis.

At mass-gathering events of the Olympic and Paralympic Games, a well-organized, on-site medical system is essential. This study evaluated the vulnerabilities of the prehospital medical system of the TOKYO 2020 Olympic and Paralympic Games (TOKYO2020) to propose corrections that can be generalized to other mass gatherings. The healthcare failure mode and effect analysis (HFMEA) was adopted to analyze vulnerabilities of the on-site medical system proposed by the organizing committee of TOKYO2020. Processes from detecting a patient on the scene to completing transport to a hospital were analyzed. Ten processes with 47 sub-processes and 122 possible failure modes were identified. HFMEA revealed 9 failure modes as vulnerabilities: misidentification of patient, delayed immediate care at the scene, misjudgment of disposition from the on-site medical suite, and inappropriate care during transportation to hospital. Proposed corrections included surveillance to decrease blind spots, first aid brochures for spectators, and uniform protocol for health care providers at the scene. The on-site medical system amended by HFMEA seemed to work appropriately in TOKYO2020.

Reporting of Unsafe Conditions at an Academic Women and Children's Hospital.

Unsafe conditions (UCs) are circumstances that increase the probability of a patient safety event occurring. Each UC identified presents an opportunity to prevent a near miss or adverse patient event through proactive mitigation. The aim of this study was to describe the frequency, characteristics, contributing factors, and potential for harm of reported UCs. This is a retrospective descriptive analysis of UC incident reports voluntarily entered into an electronic medical event reporting system at a single tertiary care women and children's hospital. Reports were reviewed and categorized using a previously published classification scheme and a modified Healthcare Failure Mode and Effects Analysis (HFMEA). Reporter role, hospital location, and time to incident resolution were also described. Between July 1, 2016, and June 30, 2019, 348 UCs were entered, representing 3.4% of all reports. Predominant categories of UCs were equipment (43.7%), medication (20.7%), and environmental safety (14.4%). A contributing factor was identified for >99.4% of all UCs, with 77.6% having more than one. Nurses (70.1%) submitted the highest numbers of UCs. The majority of UCs were of mild severity (79.9%) but had the potential to recur frequently (73.3%). UCs represented a small proportion of all reported events across the hospital. Equipment and medication issues were important causes of UCs, and most UCs had one or more contributing factors. Though most UCs were of mild severity, they had a predicted potential to recur frequently, representing significant opportunities for improvement.