درخت حوزه‌های تخصصی

Healthcare service institutions (HIS) seeking to motivate patient loyalty have identified Hospital Information Systems (HIS) as a potential solution to gather, measure, and analyze the healthcare data necessary for this goal. The purpose of this systematic review of the literature is to reveal how prevalent the use of HIS with respect to motivating patient loyalty, and to investigate the efficacy of HIS in doing so. To generate data, published empirical studies and conference papers from the past five years were compiled from the following online databases: Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, and Emerald Insight. The search results indicate that, while the use of HIS in motivating patient loyalty is rare relative to other topics within the general field of HIS, HIS use have a significant positive impact on patient satisfaction, which is understood in the literature to be directly related to patient loyalty. There remains a gap in empirical studies on the direct application of HIS with the purpose of increasing patient loyalty. Future research may be required on the development of an HIS focused on motivating patient loyalty, which can be empirically tested in a real-world HSI setting.

In recent decades, mentoring has been hailed as an important human resource management strategy, a career tool, and a learning activity in the workplace, and has played an important role in enhancing the positive outcomes of a high-performance work system. Therefore, the purpose of this study is to provide a model of mentoring system in the Ministry of Interior based on a mixed approach. First, based on the data theory of the foundation, 12 experts were interviewed. Sampling was done purposefully with a network of experts and continued until the theoretical saturation was reached. According to the research model, causal, pivotal, contextual, interventionist, strategic and consequences variables were identified. The results showed that mentoring will have consequences such as increasing knowledge sharing, organizational performance excellence, productivity, job satisfaction, psychological well-being, client satisfaction, increasing public trust in the organization, increasing trust in government and improving political understanding. For quantitative validation of the model and study of component relationships, the structural equation modeling approach was used using the partial least squares method using smart-PLS software. The statistical sample of this department was 237 managers and experts of the Ministry of Interior. The results of the quantitative step showed that the relationships in the model with appropriate impact coefficients were confirmed.

Deep learning techniques have become very popular among Artificial Intelligence (AI) techniques in many areas of life. Among many types of deep learning techniques, Convolutional Neural Networks (CNN) can be useful in image classification applications. In this work, a hybridized approach has been followed to classify lung nodule as benign or malignant. This will help in early detection of lung cancer and help in the life expectancy of lung cancer patients thereby reducing the mortality rate by this deadly disease scourging the world. The hybridization has been carried out between handcrafted features and deep features. The machine learning algorithms such as SVM and Logistic Regression have been used to classify the nodules based on the features. The dimensionality reduction technique, Principle Component Analysis (PCA) has been introduced to improve the performance of hybridized features with SVM. The experiments have been carried out with 14 different methods. It has been found that GLCM + VGG19 + PCA + SVM outperformed all other models with an accuracy of 94.93%, sensitivity of 90.9%, specificity of 97.36% and precision of 95.44%. The F1 score was found to be 0.93 and the AUC was 0.9843. The False Positive Rate was found to be 2.637% and False Negative Rate was 9.09%.