International Journal of Hospital Research
Volume:11 Issue: 4, Autumn 2022

Traumatic brain injury (TBI) is one of the primary causes of trauma-related mortality and disability; while clinically important cases could be diagnosed by brain CT scan, high rates of false negative have raised cost-effectiveness controversies. To review the epidemiology of negative brain CT scans and their economic burden on healthcare systems.

This was a narrative review of literature, querying the online databases of PubMed, Science Direct, and Web of Science for cost-effectiveness studies of brain CT scan in mild TBI.

Based on our review, 12 studies were found to evaluate the cost-effectiveness of CT scans for mild trauma patients. Among the 6 studies with a study design of cost-effectiveness model, had more long time cost analysis based on the possibility of missing the diagnosis of an important CT scan finding for TBI patient and almost all of those studies revealed that requesting CT scan for all of the mild trauma patients is better than missing cases, even by costs. Some other studies compared conservative management versus early CT scan in the highest level of evidence, Norlund et al. revealed that CT scan of all patients is more cost-effective than observation of patients in ED. A high rate of false-positive results for the applied recommendations in most reviewed studies might address the weakness of existing guidelines in preventing unnecessary CT requests and also a high rate of true negative might show the incompatibility of clinicians with guidelines. These are all imposing high unnecessary costs on hospitals and the healthcare system. Traumatic brain injury if undiagnosed could lead to mortality and disability that contribute to much more economic losses than performing a negative CT scan. But, the exorbitance rate of these negative CT scans is not justifiable.

Due to the high cost of CT scan technologies and limited resources, there is an urgent need for systematic approaches to optimal allocation of CT requests for traumatic brain injury; but currently, requesting CT scans for most patients is favored over missing any important TBI; while further studies are needed to draw a conclusion.

The EFQM excellence model is one of the most well-known self-assessment models in organizations.This study aims to systematically review the experiences of Iranian hospitals in using the EFQM Excellence Model and conducting a meta-analysis on the results.

This study was conducted to retrieve published studies on the usage of the EFQM model in Iran's hospitals. After searching the Persian and English sources by using systematic review and removing repeated and non-related articles, 21 studies were entered into the meta-analysis phase. The Random effects1 model and Cochrane's Q2 test were used to control the studies' heterogeneity. Forty-two institutes were assessed in the 21 selected studies from 2005 to 2014.

Among the nine examined criteria, the partnership and resource criteria have received the highest scores. Processes, leadership, and Society results were among the highest scored criteria, respectively. In contrast, the results of the People results, the Key results, and the people had the lowest score. Overall, the hospitals scored 45% for Enablers and 41% for the results.

A review of the criteria in the studied hospitals revealed the differences between the scores of the Enablers and results criteria; as in most hospitals, one of the Enablers criteria had the highest scores, and one of the results criteria had the lowest. This issue revealed that People's results received lower scores because these analyses are obtained by self-assessment. Accordingly, reasons such as staff dissatisfaction with the system can cause lower scores

People with asthma are generally more susceptible to respiratory infections than the general population. As a result, patients with asthma are presumed to be at a higher risk of COVID-19 and health complications during the current pandemic. However, the relationship between asthma and COVID-19 remains unclear.

This cross-sectional study was done in Imam Hossein hospital of Shahroud. Considering the prevalence of 4.7% of asthma in Iran , the confidence interval of 95% and the power of 80%, 93 patients were entered in the study.. Based on pre-existing asthma, the study population was divided into two groups; the COVID-19 patients with asthma and the COVID-19 patients without asthma. Lastly, the study compared the groups in terms of clinical course and laboratory findings. Patients with a history of smoking, diabetes, cardiovascular disease, COPD, and hypertension were excluded from the study.

Among 93 COVID-19 patients, mean lymphocyte count (mean±SD=2.1±1.1, p-value=0.001) and serum glutamic oxaloacetic transaminase (SGOT) level (mean±SD=34.3±19.5, p-value=0.001) were higher in the patients without asthma. By contrast, asthma patients had a higher prevalence of heart rate disorders (27%, p value=0.04 ), positive C-reactive protein (CRP) results (40%, p value=0.0001). Also, a significantly higher frequency of high diastolic blood pressure (DBP) was present in the asthma group (p-value= 0.02). Other variables did not show any significant association.

Patients with mild to moderate asthma were not significantly different from non-asthmatic patients in terms of severity of the disease.

High prevalence of diabetes mellitus (DM) makes it an important comorbidity in patients with coronavirus disease (COVID-19). The objective of the current study was to compare morbidity and mortality between patients with diabetes and controls.

This cross-sectional study was conducted in Imam Hossein hospital of Shahroud. A total of 184 patients with confirmed COVID-19 were included. Individuals with chronic underlying diseases such as cardiovascular diseases, hypertension, and pulmonary diseases were excluded. Then, patients were divided into two groups: patients with COVID-19 who also had DM, and individuals with COVID-19 who did not have a history of DM.

The prevalence of high diastolic blood pressure (DBP) and fever were significantly more in the non-diabetes patients (prevalence DBP=10%, p value=0.05/ prevalence fever=65%, p value=0.01). Also, the mortality rate was slightly higher in non-DM patients (P =0.5). There was not any statistically significant difference between other clinical features and laboratory tests between the groups.

We found that DM patients with COVID-19 infection were not at a higher risk of mortality or poor outcome compared to the non-DM patients.

Breast cancer is the most common cancer in women worldwide. Whether surviving a longer or shorter time, all women with advanced breast cancer in hospitals, and their families, are facing psychological problems that require interventions at hospitals to prevent psycho-social sequels.

Our systematic review aimed at reviewing current literature about the psychological interventions in Breast cancer in hospitals‎.

This study is a systematic review of published studies on the psychological interventions in Breast cancer patients in hospitals conducted in accordance with PRISMA. electronic databases MEDLINE, Embase, ScienceDirect and PsycINFO and SID and Persian sources were searched with appropriate keywords.

Our study revealed that there are totally four types of psychological interventions in hospitals available for these patients. Mindfulness-based interventions, Meaning-making interventions, Written expression of positive emotions, Psycho-spiritual interventions, and some other interventions as well as Hope intervention. All these interventions were showing good outcomes that necessitate further analysis to determine patient specific intervention.

psychological interventions in Breast cancer are aimed to teach the skills needed to alleviate stress by improving the ability to be present in the moment without criticizing or trying to modify thoughts and feelings.

Artificial intelligence and machine learning methods have proved to be able to solve both data analysis and classification problems in many fields like medical diagnoses. With development of technology in many areas like processing units and waste memory storage in recent years, many new approaches have come into reality from prolepses such as deep neural networks and gradient boosting machines. These new models are now able to classify any type of data with high precision and accuracy. They are also able to face many challenges, including imbalance data and nonlinear dependencies in high dimensional spaces. These abilities make new methods a lot more reliable and popular.

In this study, an imbalance medical dataset is used to detect heart disease by ensembling three different models including deep neural networks (DNN), light gradient boosting machine (LightGBM) and XGBoost.

As implementation results show, these methods are effective and robust while they reach an accuracy of 91.75% and f1_score 94.4.

In this study, an imbalance medical data set is classified using an ensemble method to diagnose heart disease with high accuracy.

Chronic kidney disease is a progressive and irreversible disorder in which the kidneys are unable to excrete metabolic wastes and uremia occurs . Chronic kidney disease is an important cause of mortality and morbidity in the world .

which, in addition to physical health, also threatens other dimensions of health . Despite the widespread use of hemodialysis, this method has many problems and complications. Various approaches have been proposed to improve the quality of life of hemodialysis patients admitted in hospitals , the most important of which is the use of health-promoting behaviors and lifestyle modification. Several studies have highlighted the need of developing and executing appropriate training programs to improve hemodialysis patients' lifestyles and consequently their quality of life. An educational program that is designed based on a planned behavior pattern, can be applied as an effective method to improve all aspects of lifestyle in hemodialysis patients.

The findings of these studies show that researchers have employed a variety of behavioral interventions to enhance the lifestyle of hemodialysis patients, demonstrating the impact of educational behavioral patterns on improving patients' lifestyle and quality of life.

In hemodialysis patients, an educational program based on a Theory of planned behavior can be used as an effective technique to enhance all aspects of their lives.

Background and Objective:

Left atrium is a heart chamber which volume changes has much importance for identifying, controlling and treatment of cardiovascular diseases. In the current methods, left atrium chamber volume (LAV) is estimated from echocardiography images.

For this purpose, the image segmentation and feature extraction tasks have been performed. The accuracy of these methods highly depends on the quality and performance of the method used for image segmentation and the expertise of the specialist. Therefore, left atrium chamber quantification using automatic image analysis methods is necessitated. In this study, a novel automatic approach by combining convolutional neural network with Convolutional Block Attention Module is proposed for left atrium chamber quantification in echocardiography images with an end-to-end fashion without requiring any prior image segmentation. Two different channel and spatial attention modules are embedded in the designed CNN for identifying the key properties of the output feature map and finding important regions for improving the CNN performance.

The proposed model in this study estimates LAV in end-of-systole and end-of-diastole frames with the average R2 of 96.25% and 88.76%, respectively. Our experimental results show that using attention module in CNN architecture improves the performance of CNN for Left atrium chamber quantification with feature extraction focusing on identifying the key properties and discriminating regions.

The proposed method in this study can be used in computer assisted systems (CAD) for automatic chamber quantification with improving the accuracy and speed compared to manual Left atrium chamber quantification.