Today, adults are using social media to seek health information. Evidence suggests that hospitals using Instagram reported better patient engagement and in turn increased profit and reputation. Yet... Show More

Today, adults are using social media to seek health information. Evidence suggests that hospitals using Instagram reported better patient engagement and in turn increased profit and reputation. Yet, little is known about how public and private hospitals are leveraging Instagram. This study aims to analyze the presence of hospitals on Instagram using Kuwait as a case study. Hospitals were identified using the Ministry of Health's website and Instagram. Posts collected from seven odd months were analyzed using the Constant Comparison method. A total of 3,439 posts were distributed across six categories: health advice and education, operations and services, current events, hospital community, seasonal occasions, and trivia. Public and private hospitals differed in their activity on Instagram in terms of health topics covered, post categories, and interactions. Hospitals should improve their presence on Instagram to promote healthy lifestyles, augment public health campaigns, and be a source of reliable and accessible health information online.

Modern healthcare organizations try to leverage their IT infrastructures to enhance the efficiency of processes and the quality of patient services. The flexibility of the IT infrastructure is a... Show More

Modern healthcare organizations try to leverage their IT infrastructures to enhance the efficiency of processes and the quality of patient services. The flexibility of the IT infrastructure is a critical factor in the process of establishing strategic and operational value. The authors examine how applied principles of Conceptual Independence (CI) in information systems (IS) influence the flexibility of IT infrastructures. Furthermore, it is presumed that IT outsourcing plays a role in IT flexibility. The second question asks whether IT outsourcing configurations change when CI has been applied or not. Quantitative and qualitative data have been collected in 9 mental healthcare organizations. Findings – based on integration of the data with a mixed-method approach - suggest that the healthcare organizations that apply the principles of CI are better equipped to adapt their IT infrastructure to changing demands, requests and needs. Likewise, results suggest that they have changed the government of IT outsourcing thereby increasing IT flexibility even further.

Intelligent predictive systems are showing a greater level of accuracy and effectiveness in early detection of critical diseases like cancer and liver and lung disease.Predictive models assist... Show More

Intelligent predictive systems are showing a greater level of accuracy and effectiveness in early detection of critical diseases like cancer and liver and lung disease.Predictive models assist medical practitioners in identifying the diseases based on symptoms and health indicators like hormone,enzymes,age,bloodcounts,etc.This study proposes a framework to use classification models to accurately detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics techniques.The article proposes an enhanced framework on the original study by Ramana et al. (2011).It uses evaluation measures like Precision and Balanced Accuracy to choose the most efficient classification algorithm in INDIA and USA patient datasets using various factors like enzymes,age,etc.Using Youden’s Index, individual thresholds for each model were identified to increase the power of sensitivity and specificity.A framework is proposed for highly accurate automated disease detection in the medical industry,and it helps in strategizing preventive measures for patients with liver diseases.

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world... Show More

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.