Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal.

In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future.

GENE2D: A NoSQL Integrated Data Repository of Genetic Disorders Data.

There are few sources from which to obtain clinical and genetic data for use in research in Saudi Arabia. Numerous obstacles led to the difficulty of integrating these data from silos and scattered sources to provide standardized access to large data sets for patients with common health conditions. To this end, we sought to contribute to this area and offer a practical and easy-to-implement solution. In this paper, we aim to design and implement a "not only SQL" (NoSQL) based integration framework to generate an Integrated Data Repository of Genetic Disorders Data (GENE2D) to integrate data from various genetic clinics and research centers in Saudi Arabia and provide an easy-to-use query interface for researchers to conduct their studies on large datasets. The major components involved in the GENE2D architecture consists of the data sources, the integrated data repository (IDR) as a central database, and the application interface. The IDR uses a NoSQL document store via MongoDB (an open source document-oriented database program) as a backend database. The application interface called Query Builder provides multiple services for data retrieval from the database using a custom query to answer simple or complex research questions. The GENE2D system demonstrates its potential to help grow and develop a national genetic disorders database in Saudi Arabia.

G3DMS: Design and Implementation of a Data Management System for the Diagnosis of Genetic Disorders.

Current health information systems used in genetic research centers and clinics in the Kingdom of Saudi Arabia have failed to enable researchers and health care physicians to utilize genetic and clinical data in their research. In this paper, we aim to design and implement a Genetic Disorders Diagnosis Data Management System (G3DMS) to support clinicians in the process of diagnosing genetic diseases and conducting genetic studies. A case study was undertaken to analyze a health information system in Saudi to understand its design problems via a brainstorming method. We then used the Barker's system design method and a prototype to validate our proposed system via usability testing. This research has resulted in the development of the G3DMS that comprises: electronic data-capture forms for data entry; a customized query builder to display and modify patient data as well as form research queries; a module that allows historical data to be uploaded in the form of bulk data using a template; export data options to Excel and JavaScript Object Notation (JSON) format; and authorization access for healthcare researchers and clinicians. The G3DMS was implemented in the Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, Jeddah, KSA.

Utilisation of hospital information systems for medical research in Saudi Arabia: A mixed-method exploration of the views of healthcare and IT professionals involved in hospital database management systems.

BACKGROUND: Although in recent times the Saudi government has paid much attention to the adaptation of hospital information systems (HIS) and electronic medical records (EMR), the importance of utilising HIS to enhance medical research has been neglected. OBJECTIVE: We aimed to (i) investigate the current state of medical research in Saudi Arabia, (ii) identify possible issues that hinder improvement of medical research and (iii) identify possible solutions to enhance the role of HIS in medical research in Saudi Arabia. METHOD: We used a questionnaire and structured interview approach. Questionnaires were distributed to Saudi healthcare professionals. One hundred responses to our questionnaire were captured by the online Google Form designed specifically for our survey. Structured interviews with two IT professionals were conducted regarding technical aspects of their hospital data management systems. RESULTS: Six themes contributing to the inefficacy of HIS in medical research in Saudi Arabia emerged from the data: incorrect datasets, difficult data collection and storage, poor data analytics, a lack of system interoperability across different HIS for universal access and negative perception of the usefulness of HIS for medical research. CONCLUSION AND IMPLICATIONS: Our findings suggest (i) cloud-based HIS would support efficient, reliable and integrated data collection and storage across all hospitals in Saudi Arabia; (ii) EMR data sources should be seamlessly linked to avoid incomplete, fragmented or erroneous EMR in Saudi Arabia; and (iii) collaboration between all hospitals in Saudi Arabia to adopt a uniform standard to support interoperability and improve data exchange and integration is necessary.