Introduction: Medical errors are among significant health system problems. The best method to detect errors is to identify the root and systemic causes of errors. Objective: This study aimed to investigate the Failures of clinical care in emergency departments using the Healthcare Failure modes and Effects Analysis (HFMEA). Materials and Methods: This was an analytical and cross-sectional study. The required data were collected qualitatively and quantitatively using focus group discussions in emergency departments. The study population consisted of all emergency department nurses. Study samples were selected by purposive sampling technique. By the HFMEA method, Risk Priority Number (RPN) was calculated to reach Failure modes and those with RPN >216 were identified as the most frequent and risky errors. Results: A total number of 67 Failure modes were identified for 26 clinical care processes. The errors with the highest RPN were “ the lack of oxygenation during airway suctioning” and “ the lack of airway suctioning during ventilator weaning process” ; they related to the respiratory system care with RPN=336. The error with the lowest RPN was “ the lack of signing verbal orders by two nurses” ; related to the general practice with RPN=8. Conclusion: Investigating the causes and effects of these errors, controlling, and suggesting measures indicate the high efficiency of the HFMEA method. It also suggested the preventability of these errors by increasing the knowledge and awareness of the training staff by providing training courses.

In this paper, we consider linear degradation in the case of multiple Failure modes and obtain the graph of survival function, traumatic Failure probability function, natural Failure probability function by simulating data. We compare traumatic Failure probability function, natural Failure probability function and survival probability in different sample sizes. Also sample path is simulated by using noise.

The aim of this study is analyzing the Failure modes of sandstones related to Markazi Province using compressive strength changes. For this purpose, the fracture modes of five different types of sandstones with different compressive strength values have been studied experimentally and numerically. Also, The effect of Failure modes on uniaxial compressive strength was quantified. block samples were collected from different sandstone formations located in northern part of Markazi province. These sandstones were divided into five types based on the textural and mineralogical characteristics. These samples were subjected to uniaxial compressive strength testing. Examination of the fracture modes showed that simple shear mode occurred at low values of strength and it changes to multiple extension mode with increasing the compressive strength. The dominant Failure mode for types 1, 2 and 4 is simple shear and for types 3 and 5 is multiple extension. Investigation of Failure modes in different strength ranges showed that the dominant Failure modes up to 140 and over 140 MPa are simple shear and multiple extension, respectively. Therefore, strength of 140 MPa was considered as the transisson point. Also, in this study, the Failure modes of the experimental samples were numerically simulated using PFC2D software. The results of numerical modeling showed a good agreement with the experimental results.

One of the methods widely used by researchers to analyze the risk of maintenance operations is Failure modes and Effects Analysis (FMEA). However, the conventional approach of the FMEA faces serious drawbacks to rank equipment and Failure modes. The purpose of this paper is to dynamically rank equipment in an Interval-Valued Intuitionistic Fuzzy environment to identify critical equipment, So that the main drawbacks of the conventional FMEA are eliminated. To this end, we present the Interval-Valued Intuitionistic Fuzzy condition based dynamic weighing method (IVIF-CBDW). In following, by improving the interval-valued intuitionistic fuzzy power weight Heronian aggregation (IVIFPWHA) operator and improving the approach of multi attributive border approximation area comparison (MABAC) method, these two improved methods by Interval-Valued Intuitionistic Fuzzy condition based dynamic weighing method (IVIF-CBDW) have been merged and a robust FMEA model has been proposed in which the main drawbacks of the conventional FMEA have been eliminated. In order to prove its applicability, this model was used in a case study to rank the equipment of a HL5000 crane barge. Finally, the results are compared with the traditional FMEA methods. It is indicated that the proposed model is much more flexible and provides more rational results.

Eddy current dynamometers are one of the most important equipment of internal combustion engine test cells in powertrain companies. Failure of rolling element bearings in these assets is a common reason for downtime hours of engine test beds. Insufficient information in this field not only imposes unwanted expenses, but also would result in wrong engine testing data. The purpose of this article is the identification and classification of rolling element bearings Failure modes in the eddy current dynamometers. This is an essential research for choosing an appropriate maintenance plan in engine testing laboratories. More than 30 faulty ball bearings of different eddy current dynamometers are collected to study different modes in which the bearings actually fail to operate properly. In addition, metallurgical studies have been applied on the failed parts to specify possible root causes of Failure. The dynamometers whose bearings are under study have been employed in different engine testing procedures that an eddy current dynamometer is capable of performing e.g. endurance tests, over-speed tests, etc. so that a diversified collection of modes is available which would happen in any eddy current dynamometer. The recognized cases are then classified to prepare a practical basis for condition monitoring of dynamometers using vibration analysis or other methods.

Background: Healthcare statistics, issued by various international organizations, show that medical errors in health centers impose high costs on patients and hospitals and increase the rates of morbidity and mortality around the world. Due to the potential risks of cardiovascular diseases, the occurrence of any errors can potentially endanger the patients' lives and incur costs on them, as well as hospitals. On the other hand, anesthesia is one of the priorities for risk management in clinical care. Objectives: This study aimed to identify, classify, and evaluate anesthesia Failures in open heart surgeries, using the healthcare Failure mode and effects analysis (HFMEA) technique. Methods: The anesthesia process in open heart surgery was reviewed using the HFMEA technique, and four processes, 25 subprocesses, 95 activities, and 204 risks were extracted. The causes of Failure were also identified, and four Failure modes were determined as the most important Failures, based on the qualitative and quantitative methods,finally, some solutions were proposed. Changes in the level of healthcare workers,knowledge and competence, computer use and timing, and the amount of administered medications were identified as the potential risk factors and errors. Results: The inadequate awareness and knowledge of healthcare workers, non-use of computers, prescription errors, technique errors, and timing and amount of medication administration were identified as the errors and risk factors. Based on the present findings, another expert needs to evaluate the design, feasibility, and prioritization of techniques, including continuing medical education for anesthesia professionals and experts, statutory documentation, and control of the individuals' activities. Conclusions: Based on the present findings, establishing a risk management committee seems essential to identify errors and improve the design and plan of different techniques so as to execute, monitor, control, and review errors in a cycle of continuous improvement.