Current Issue : January - March Volume : 2021 Issue Number : 1 Articles : 5 Articles
Mobile health (m-health) is the termofmonitoring the health usingmobile phones and patientmonitoring devices etc. It has been\noften deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) and big data\nanalytics have been applied within the m-health for providing an effective healthcare system. Various types of data such as\nelectronic health records (EHRs), medical images, and complicated text which are diversified, poorly interpreted, and extensively\nunorganized have been used in themodernmedical research. This is an important reason for the cause of various unorganized and\nunstructured datasets due to emergence of mobile applications along with the healthcare systems. In this paper, a systematic\nreview is carried out on application of AI and the big data analytics to improve the m-health system. Various AI-based algorithms\nand frameworks of big data with respect to the source of data, techniques used, and the area of application are also discussed. This\npaper explores the applications of AI and big data analytics for providing insights to the users and enabling themto plan, using the\nresources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for\nm-health. Findings of this paper will guide the development of techniques using the combination of AI and the big data as source\nfor handling m-health data more effectively....
This study sought to determine whether the implementation of regular and structured\nfollow-up of patients with chronic heart failure (CHF), combined with therapeutic education and\nremote monitoring solution, leads to better management. This was a single-center retrospective study\nconducted in a cohort of patients with proven CHF who were followed up in the Mulhouse region\n(France) between January 2016 and December 2017 by the Unité de Suivi des Patients Insuffisants\nCardiaques (USICAR) unit. These patients received regular protocolized follow-up, a therapeutic\neducation program, and several used a telemedicine platform for a two-year period. The primary\nendpoint was the number of days hospitalized for heart failure (HF) per patient per year....
The explanation of behaviors concerning telemedicine acceptance is an evolving area of\nstudy. This topic is currently more critical than ever, given that the COVID-19 pandemic is making\nresources scarcer within the health industry. The objective of this study is to determine which model,\nthe Theory of Planned Behavior or the Technology Acceptance Model, provides greater explanatory\npower for the adoption of telemedicine addressing outlier-associated bias. We carried out an online\nsurvey of patients. The data obtained through the survey were analyzed using both consistent partial\nleast squares path modeling (PLSc) and robust PLSc. The latter used a robust estimator designed\nfor elliptically symmetric unimodal distribution. Both estimation techniques led to similar results,\nwithout inconsistencies in interpretation....
Since the first case of COVID-19 was reported in Spain, almost 22% of healthcare professionals\nhave been infected. Among the main causes are exposure during the care of suspected patients and\nasymptomatic patients, which caused a greater lack of protection in some cases, and to the global\nshortage of personal protective equipment due to the strong demand for it. The main objective of\nthis study was to evaluate the effectiveness of a teleconsultation protocol with patients who had\nrespiratory symptoms in the reduction of the consumption of personal protective equipment (PPE)\nin a hospital emergency service (HES) during the COVID-19 pandemic. This is a descriptive and\nretrospective study that analyzes the implementation of a teleconsultation protocol with patients with\nrespiratory problems treated in the HES at the Hospital de Poniente (Almeria), between 18 March and\n30 April 2020....
This study sought to find out the effects of Information and Communication\nTechnology (ICT) on health service delivery at Tafo Government Hospital. A\ndescriptive survey design was used. Data were collected through the use of\nsemi-structured questionnaire and administered to 50 respondents where\nstratified random sampling technique was used by ranking position as strata.\nData were analyzed using descriptive statistics. From the findings, 56% of the\nrespondents overwhelmingly agreed to the fact that the applications of ICT\nprovide quicker medical diagnoses, reduced workload among users, improvement\nin patientsâ?? waiting time and information accessibility....
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