Background: Patient safety is the first step to improve the quality of care. Objectives: Therefore, the present study aimed to examine the risk assessment of processes in a pediatric surgery department using the Health Failure Mode and Effect Analysis (HFMEA) in 2017 - 2018. Methods: In this research, a mixed-method design (qualitative action and quantitative descriptive cross-sectional study) was used to analyze failure mode and their effects. The nursing errors in the clinical management model were used to classify failure modes, and the theory of inventive problem solving was used to determine a solution for improvement. Results: According to the five procedures selected by the voting method and their rating, 25 processes, 48 sub-processes, and 218 failure modes were identified with HEMEA. Eight risk modes (3.6%) were found as non-acceptable risks and were transferred to the decision tree. The main root causes (hazard score ≥ 4) were as follows: Technical-related factors (14.34%), organizational-related factors (31.9%), human-related factors (45.3%), and other factors (7.6%). Conclusions: The HFMEA method is very effective in identifying the possible failure of treatment procedures, determining the cause of each failure mode, and proposing improvement strategies.

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For many years, researchers have studied high accuracy methods for recognizing the handwriting and achieved many significant improvements. However, an issue that has rarely been studied is the speed of these methods. Considering the computer hardware limitations, it is necessary for these methods to run in high speed. One of the methods to increase the processing speed is to use the computer parallel processing power. This paper introduces one of the best feature extraction methods for the handwritten recognition, called DPP (Derivative Projection Profile), which is employed for isolated Persian handwritten recognition. In addition to achieving good results, this (computationally) light feature can easily be processed. Moreover, Hamming Neural Network is used to classify this system. To increase the speed, some part of the recognition method is executed on GPU (graphic processing unit) cores implemented by CUDA platform. HADAF database (Biggest isolated Persian character database) is utilized to evaluate the system. The results show 94.5% accuracy. We also achieved about 5.5 times speed-up using GPU.

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