Through a survey of 300 health care executives and a program of interviews with industry experts, startup leaders, and academic researchers, this report explores the best practices for success when implementing integrated digital solutions into health care, and how these can support decision-makers in a range of settings, including laboratories and hospitals. Health care is primed for digital adoption. The global pandemic underscored the benefits of value-based care and accelerated the adoption of digital and AI-powered technologies in health care. Overwhelmingly, 96% of the survey respondents say they are "ready and resourced" to use digital health, while one in four say they are "very ready." However, 91% of executives agree interoperability is a challenge, with a majority (59%) saying it will be "tough" to solve.

SmartCardia 7L Patch is a breakthrough 7/14 day patch that offers real-time 7-Lead ECG and vitals with AI SaaS* *SmartCardia solution approved as SCaAI patch and cloud platform in Europe (CE Class IIa) - ECG, respiration, SpO2, activity and cloud based arrhythmia detection.

New Delhi: Metaverse, the new buzzword amongst healthcare, a collective virtual shared space is no more a mystery box. This new emerging technology which is more prominent in the cryptocurrency market and gaming segment is now slowly proliferating in the healthcare domain. Some of the big hospitals are already adapting the digital virtual space of'metaverse'. ETHealthWorld explores what does this new technology'really' mean for healthcare? How will this technology make transformational changes, break the physical rules of the real world and redefine the future of the health domain.

A neck patch that monitors respiratory sounds may help manage asthma and chronic obstructive pulmonary disease (COPD) by detecting symptom flareups in real time, without compromising patient privacy. Asthma and COPD are two of the most common chronic respiratory diseases. In Europe, the combined prevalence is about 10 percent of the general population. In Canada, an estimated 3.8 million people experience asthma and two million people experience COPD. The chronic nature of asthma and COPD requires continuous disease monitoring and management.

In recent years, the Internet of Things (IoT), has seen more popularity in cyber markets. Statista projects the IoT industry worldwide. The Internet of Things (IoT), which is a collection of connected devices such as smart home and office gadgets and others, has seen more popularity in recent years. Statista predicts that the IoT market will exceed one trillion dollars worldwide by 2030. Businesses can now use IoT technology to increase their revenue.

Artificial intelligence has been in the spotlight for its ability to discriminate and reflect prejudices against groups of people, be it on the grounds of race, religion, or gender. This, of course, is a result of the prejudices held by the people behind the AI - artificial intelligence is susceptible to the prejudices and discriminatory attitudes held by its creators. Ageism - or prejudice and discrimination on the basis of age - is included in the list as the elderly are continuously neglected in the field of AI, thus excluding their experiences and concerns. This was exactly the point of concern in a recent policy brief by the World Health Organization, which warned that ageism, when exhibited by AI, could have serious impacts on the health of the elderly. "Specifically for older people, ageism is associated with a shorter lifespan, poorer physical and mental health and decreased quality of life," WHO says, adding that it "can limit the quality and quantity of health care provided to older people."

A new study by Juniper Research has found that smart hospitals will deploy 7.4 million connected Internet of Medical Things (IoMT) devices globally by 2026; over 3,850 devices per smart hospital. This global figure represents total growth of 231 per cent over 2021, when 3.2 million devices were deployed. The concept of the IoMT involves healthcare providers leveraging connected devices such as remote monitoring sensors and surgical robotics to improve patient care, staff productivity, and operational efficiency. The research identified smart hospitals in the US and China as leading the global adoption of IoMT devices; accounting for 21 per cent and 41 per cent of connected devices respectively, by 2026. It highlighted digital healthcare initiatives implemented during the ongoing pandemic and high levels of existing digitalisation within healthcare infrastructure as key to these countries' leading positions.

A patient in the ER, ICU, and other care environments is often connected to monitoring equipment such as cardiac monitors or ventilators, which capture a range of medical data points: heart rate, respiratory rate, oxygen saturation levels, body temperature, and more. Studying these numbers over time can yield vital information about the body's physiological patterns indicating imminent deterioration such as cardiac arrests, respiratory depression, and stroke. Unfortunately, in most cases, medical professionals are not able to leverage such data because most information from medical devices is transient. Very little of the bedside device data makes its way to the EHR, and the rest is deleted once a patient is taken off of the monitor. When a patient is transferred to a different unit, there is no easy way for members of the care team to relay historical data to the new care team.

If artificial intelligence in healthcare brings to mind visions of robot surgeons, BioIntellisense's stick-on sensor is bound to be a disappointment. Just 3 inches wide by 1 inch tall, this plastic and metal double hexagon was cleared last month by the US Food and Drug Administration for remote monitoring of vital signs with medical-grade accuracy. Doctors at UCHealth, which runs 12 Colorado hospitals, say the device will let them send patients home earlier while still monitoring their respiratory rate, resting heart rate, skin temperature and even body position. The data can then be fed into computers that use machine learning to spot people who might need more attention, allowing early intervention and avoiding emergency hospital visits. UCHealth has already used computer surveillance to fight sepsis, a potentially fatal complication from infection, on its wards.

Cardiovascular diseases and heart failures in particular are the main cause of non-communicable disease mortality in the world. Constant patient monitoring enables better medical treatment as it allows practitioners to react on time and provide the appropriate treatment. Telemedicine can provide constant remote monitoring so patients can stay in their homes, only requiring medical sensing equipment and network connections. A limiting factor for telemedical centers is the amount of patients that can be monitored simultaneously. We aim to increase this amount by implementing a decision support system. This paper investigates a machine learning model to estimate a risk score based on patient vital parameters that allows sorting all cases every day to help practitioners focus their limited capacities on the most severe cases. The model we propose reaches an AUCROC of 0.84, whereas the baseline rule-based model reaches an AUCROC of 0.73. Our results indicate that the usage of deep learning to improve the efficiency of telemedical centers is feasible. This way more patients could benefit from better health-care through remote monitoring.