Heart failure patients monitoring using IoT-based remote monitoring system

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy

New perspectives for hypertension management: progress in methodological and technological developments

: Hypertension is the most common and preventable risk factor for cardiovascular disease (CVD), accounting for 20% of deaths worldwide. However, 2/3 of people with hypertension are undiagnosed, untreated, or under treated. A multi-pronged approach is needed to improve hypertension management. Elevated blood pressure (BP) in childhood is a predictor of hypertension and CVD in adulthood; therefore, screening and education programmes should start early and continue throughout the lifespan. Home BP monitoring can be used to engage patients and improve BP control rates. Progress in imaging technology allows for the detection of preclinical disease, which may help identify patients who are at greatest risk of CV events. There is a need to optimize the use of current BP control strategies including lifestyle modifications, antihypertensive agents, and devices. Reducing the complexity of pharmacological therapy using single-pill combinations can improve patient adherence and BP control and may reduce physician inertia. Other strategies that can improve patient adherence include education and reassurance to address misconceptions, engaging patients in management decisions, and using digital tools. Strategies to improve physician therapeutic inertia, such as reminders, education, physician-peer visits, and task-sharing may improve BP control rates. Digital health technologies, such as telemonitoring, wearables, and other mobile health platforms, are becoming frequently adopted tools in hypertension management, particularly those that have undergone regulatory approval. Finally, to fight the consequences of hypertension on a global scale, healthcare system approaches to cardiovascular risk factor management are needed. Government policies should promote routine BP screening, salt-, sugar-, and alcohol reduction programmes, encourage physical activity, and target obesity control

DICOM metadata: a useful resource for big data analytics

Postprint (published version

Future bathroom: A study of user-centred design principles affecting usability, safety and satisfaction in bathrooms for people living with disabilities

Research and development work relating to assistive technology 2010-11 (Department of Health) Presented to Parliament pursuant to Section 22 of the Chronically Sick and Disabled Persons Act 197

Exploring Adoption and Satisfaction with Self-Service Health Technology in Older Age: Perspectives of Healthcare Professionals and Older People

(1) Background. A range of self-service technologies (SST) have been adapted to support the health of older people. Factors involved in older people’s and health professionals’ perceptions of SST in older age were investigated. (2) Methods. Customer Dominant Logic guided this prospective mixed-methods study, including surveys with people 70 years and over and health professionals and individual semi-structured interviews in a sample of survey respondents. Survey data were descriptively analysed, while interview themes were derived inductively. (3) Results. Surveyed (n = 12) people 70 years and over placed higher value, expressed more positive user experience, were more satisfied and had greater recognition of the benefits of SST, compared with (n = 10) health professionals (p = 0.001), who considered them to be inferior to traditional healthcare. All seven interviewees agreed that despite accessibility issues and complexity, they valued SST support of older people’s health, thereby confirming the relevance of Customer Dominant Logic in SST offerings. (4) Conclusions. Since older participants were positive and satisfied in using SSTs that are health-supporting, health professionals have a role in encouraging and assisting older people in their use. This requires targeted SST education for health professionals, and more accessible, user-friendly SST and technological support for older people

The Empirical Foundations of Telemedicine Interventions for Chronic Disease Management

The telemedicine intervention in chronic disease management promises to involve patients in their own care, provides continuous monitoring by their healthcare providers, identifies early symptoms, and responds promptly to exacerbations in their illnesses. This review set out to establish the evidence from the available literature on the impact of telemedicine for the management of three chronic diseases: congestive heart failure, stroke, and chronic obstructive pulmonary disease. By design, the review focuses on a limited set of representative chronic diseases because of their current and increasing importance relative to their prevalence, associated morbidity, mortality, and cost. Furthermore, these three diseases are amenable to timely interventions and secondary prevention through telemonitoring. The preponderance of evidence from studies using rigorous research methods points to beneficial results from telemonitoring in its various manifestations, albeit with a few exceptions. Generally, the benefits include reductions in use of service: hospital admissions/re-admissions, length of hospital stay, and emergency department visits typically declined. It is important that there often were reductions in mortality. Few studies reported neutral or mixed findings.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140284/1/tmj.2014.9981.pd

Integration of Smart Wearable Devices and Cloud Computing in the Kenyan Public Health Care System

The utilization of smart wearable devices and cloud computing in the Kenyan public health care system will facilitate real-time patient monitoring and management. The shortage of certified healthcare professionals and the limited access to quality specialized care for individuals in remote settings has prompted the adoption of wearable devices and cloud computing strategies in Kenya. However, there lacks a clear framework design of integrating the technologies in the public health sector. This article evaluates the current status of healthcare systems in Kenya. It also investigates the existing mobile health and cloud computing services in the country while evaluating the main legal concerns inherent to the utilization of the technologies. The document further outlines a framework design for a mobile application named GB Health. The application incorporates cloud computing and smart wearable devices in the Kenyan public health care system. The design will enhance workflow and patient outcomes in the sector. Keywords: Smart wearable devices, cloud computing, GB Health DOI: 10.7176/IKM/11-4-04 Publication date:June 30th 2021

Addressing the Health Needs of an Aging America: New Opportunities for Evidence-Based Policy Solutions

This report systematically maps research findings to policy proposals intended to improve the health of the elderly. The study identified promising evidence-based policies, like those supporting prevention and care coordination, as well as areas where the research evidence is strong but policy activity is low, such as patient self-management and palliative care. Future work of the Stern Center will focus on these topics as well as long-term care financing, the health care workforce, and the role of family caregivers

Predictive Internet of Things Based Detection Model of Comatose Patient using Deep Learning

The needs and demands of the healthcare sector are increasing exponentially. Also, there has been a rapid development in diverse technologies in totality. Hence varied advancements in different technologies like Internet of Things (IoT) and Deep Learning are being utilised and play a vital role in healthcare sector. In health care domain, specifically, there is also increasing need to find the possibility of patient going into coma. This is because if it is found that the patient is going into coma, preventive steps could be initiated helping patient and this could possibly save the life of the patient. The proposed work in this paper is in this direction whereby the advancement in technology is utilised to build a predictive model towards forecasting the chances of a patient going into coma state. The proposed system initially consists of different medical devices like sensors which take inputs from the patient and helps aid to monitor the condition of the patient. The proposed system consists of varied sensing devices which will help to record patient’s details such as blood pressure (B.P.), pulse rate, heart rate, brain signal and continuous monitoring the motion of coma patient. The various vital parameters from the patient are taken in continuously and displayed across a graphical display unit. Further as and when even if one vital parameter exceeds certain thresholds, the probability that patient will go into coma increases. Immediately an alert is given in. Further, all such records where there are chances that patient goes into coma state are stored in cloud. Subsequently, based on the data retrieved from the cloud a predictive model using Convolutional Neural Network (CNN) is built to forecast the status of the coma patient as an output for any set of health-related parameters of the patient. The effectiveness of the built predictive model is evaluated in terms of performance metrics such as accuracy, precision and recall. The built forecasting model displays high accuracy up to 98%. Such a system will greatly benefit health sector and coma patients and enable build futuristic and superior predictive and preventive model helping in reducing cases of patient going into coma state