Performance Improvement in Hospital Management using RFID and ZigBee Technologies for Tracking and Monitoring Patients and Assets in Saudi Arabia

Abstract— This paper outlines a developed framework for presenting and visualising data from RFID and ZigBee technologies. The data is gathered from sensors that track and monitor the location and statuses of patients, medical staff and medical equipment by using visualisation concepts to assist in creating, transforming and sharing knowledge to improve hospital management performance and help and to support decision making in the Saudi Arabian healthcare sector

Relationship Between Evidence Requirements, User Expectations, and Actual Experiences : Usability Evaluation of the Twazon Arabic Weight Loss App

Acknowledgments: This research project was supported by a grant from the Research Center of the Female Scientific and Medical Colleges, Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia.Peer reviewedPublisher PD

A detailed review of blockchain-based applications for protection against pandemic like COVID-19

The recent corona virus disease (COVID-19) pandemic has brought the issues of technological deficiencies and challenges of security and privacy, validating and maintaining anonymity, user control over records while fully utilizing the available records etc., that can be encountered in an emergency or pandemic condition. Blockchain technology has evolved as a promising solution in conditions that necessitate immutability, record integrity, and proper records authentication. Blockchain can effectively resolve the technical barriers and effectively utilize the available resources and infrastructure in pandemic situations like the current COVID-19. This paper provides an extensive review of various possible use cases of blockchain and available solutions for protection against the COVID-19 like situation. It gives an insight into the benefits and shortcomings of available solutions. It further provides the issues and challenges of adopting blockchain in a situation like COVID-19 and suggest future directions that can offer a platform for further improved and better solutions

Sehaa: A big data analytics tool for healthcare symptoms and diseases detection using Twitter, Apache Spark, and Machine Learning

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual aicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics

A review of wearable sensors based monitoring with daily physical activity to manage type 2 diabetes

Globally, the aging and the lifestyle lead to rabidly increment of the number of type two diabetes (T2D) patients. Critically, T2D considers as one of the most challenging healthcare issue. Importantly, physical activity (PA) plays a vital role of improving glycemic control T2D. However, daily monitoring of T2D using wearable devices/ sensors have a crucial role to monitor glucose levels in the blood. Nowadays, daily physical activity (PA) and exercises have been used to manage T2D. The main contribution of the proposed study is to review the literature about managing and monitoring T2D with daily PA through wearable devices and sensors. Finally, challenges and future trends are also highlighted

Smart e-Health System for Real-time Tracking and Monitoring of Patients, Staff and Assets for Healthcare Decision Support in Saudi Arabia

Healthcare in Saudi Arabia has been lagging behind the developed countries of the world, due to the insufficient number of healthcare practitioners and the lack of applications of tracking and monitoring technology. These shortages have contributed to problems such as patient misidentification, long patient waiting times, and the inability to locate medical equipment efficiently. The country’s Vision 2030 plan outlines ways to solve the deficient workforce problem by promoting more local health-related educational outlets, and by funding this expanding sector. Consequently, Saudi Arabia needs to adapt to the demanding nature of modern healthcare, which presents major problems that this research aims to help solve. The literature has shown that Information Technology systems have begun to be implemented in some hospitals across Saudi Arabia, but even in those hospitals these technologies are being under-utilised. The intention of this thesis is to provide an appropriate choice for a real-time tracking and monitoring technology in healthcare, in the form of an integrated RFID/ZigBee system. This thesis develops a holistic framework for healthcare institutions, to be followed for customised solutions in improving staff efficiency and productivity, and for better patient care, while minimising long-term costs. This holistic framework incorporates contextual elements from both the Information System Strategy Triangle (ISST) and the Human, Organisation and Technology-fit factors (HOT-fit) frameworks, in a way that ensures the new framework addresses technology, organisational, human and business factors. The holistic model is refined through Communities of Practice (CoPs), one of which was developed and utilised for the research purposes of this thesis, and assisted in the creation of a questionnaire for assessing the requirements and challenges of the KSA healthcare system. This questionnaire was based on 220 usable responses. It also helped to refine the framework for its final version, which included all identified factors relevant to the decision a healthcare institution faces in choosing a health information technology system. Various cases were analysed to improve the hospitals workflow, using the proposed technology and including processes such as relocating staff and medical assets. This led to the need for visualisation and knowledge management, to support real-time data analysis for business intelligence decision making. The end goal of this analysis is to provide interactive platforms to healthcare staff for use in improving efficiency and productivity. The outcomes of these improvements will be to ensure better patient care, lower patient waiting time, reduced healthcare costs, and to allow more time for staff to provide improved patient-centric care in the Saudi healthcare sector. Keywords: e-Health, Health Information Technology, Tracking and Monitoring System, Kingdom of Saudi Arabia, Holistic Framework, Communities of Practice, Knowledge Management, Visualisation, KFM

Artificial Intelligence and Internet of Things Enabled Intelligent Framework for Active and Healthy Living

Obesity poses several challenges to healthcare and the well-being of individuals. It can be linked to several life-threatening diseases. Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss. State-of-the-art technologies have the potential for long-term benefits in post-surgery living. In this work, an Internet of Things (IoT) framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight. The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients. It also attempts to automate the data analysis and represent the facts about a patient. The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system. The proposed IoT framework also benefits from machine learning based activity classification systems, with relatively high accuracy, which allow the communicated data to be translated into meaningful information

A Review of Physical Human Activity Recognition Chain Using Sensors

In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.