Ved å gjøre en del målinger hjemme blir det også mulig å følge med på utviklingen og oppdage en uheldig utvikling, noe som i mange tilfeller også er en forutsetning for at behandlingen skal være forsvarlig. Oppdage og varsle epileptiske anfall. Johns Hopkins EpiWatch: App and Research Study What is EpiWatch?
Johns Hopkins EpiWatch™ is an app for Apple Watch™ and research study. EpiWatch helps you manage your epilepsy by tracking your seizures and possible triggers, medications and side effects. You can view this information at any time, and a dashboard lets you share a summary of the data with your doctor or caregiver if you want. With EpiWatch, you can also send a message to family members or caregivers to let them know when you are tracking a seizure. Track your Seizures as You Contribute Vital Data to Epilepsy Research EpiWatch gives you a chance to help epilepsy research by sharing the data about your seizures. A new feature to EpiWatch enables participants to schedule continuous recording using a timer or to turn on continuous tracking in order to collect seizure data overnight or if they may be having seizures without warning symptoms. Please note: EpiWatch is not a seizure detector. How the App and Study Work Learn More. Oppdage og varsle epileptiske anfall. Varsle risiko for akutte reinnleggelser av eldre med flere koniske sykdomer.
Background: The most common cause of emergency transports/admissions in the aging population is deterioration in their health status due to multiple chronic conditions. To meet the needs of this population, healthcare systems are seeking cost-effective ways to monitor, diagnose, and treat patients, based on connected solutions that seamlessly integrate data and provide actionable insights. The Philips Lifeline’s CareSage program for elderly and frail people utilizes a Personal Emergency Response Service (PERS) to detect medical emergencies and to promote independent living. The system tracks the types and outcomes of incidents, in particular the emergency transport-related events. Their timely detection is critical in optimizing clinical and financial outcomes.
Objective: The study objectives are to evaluate (1) healthcare utilization and expenditure and (2) the CareSage predictive model on patients of Partners Healthcare at Home who have been using the Philips Lifeline service. Oppdage risiko for nyreskade hos diabetikere. Artificial intelligence start-up Medial EarlySign in a new study has shown how the combination of AI and EHR data can facilitate early detection and treatment of kidney problems and can help slow down – or even prevent – progression to end-stage renal disease. Medial EarlySign's machine learning-based model analyzed dozens of factors residing in electronic health records, including laboratory test results, demographics, medications, diagnostic codes and others, to predict who might be at high risk for having renal dysfunction within one year.
By isolating less than 5 percent of the 400,000 diabetic population selected among the company's database of 15 million patients, the algorithm was able to identify 45 percent of patients who would progress to significant kidney damage within a year, prior to becoming symptomatic, the start-up reported. This represents 25 percent more patients than would have been identified by commonly used clinical tools and judgment, the company contended. Egenhåndtering av KOLS. By Matt Reynolds Oxford University Hospitals NHS Foundation Trust The future of healthcare could be in your pocket.
Two new medical apps that help people monitor their health at home, reducing their need to visit a doctor, are set to be rolled out to as many as four UK National Health Service trusts over the next year. The apps, which are currently being trialled in four hospitals in Oxfordshire, UK, transmit patient data from a tablet or smartphone directly to clinicians. According to Ilan Lieberman, a member of the Royal Society of Medicine’s council on telemedicine and e-health, such apps will have a huge impact on the management of chronic diseases. One system, called GDm-health, helps manage the treatment of gestational diabetes – a condition that affects about 1 in 10 pregnant women. Advertisement App-solutely fine “We’ve got three trusts wanting to install this for their gestation diabetes management,” Tarassenko says, but his research group is too small to support any more trials. Robot som fanger om omgivelser for å predikere uhell eller uønsket utvikling av helse.
English version of this page Antallet eldre mennesker som bor hjemme er økende, og denne trenden forventes å fortsette.
Utfordringen vil da være hvordan tilby teknologi som kan håndtere de komplekse og ulike miljøer som finnes i hjemmene. Videre kan teknologien lett bli sett på som en trussel mot personvern og mangel på mellommenneskelig kontakt. Dette prosjektet adresserer disse problemene gjennom brukersentrert design av robotsystemer og utvikling av tilpasningsdyktig teknologi. En del av dette vil være å demonstrere fordelene både med hensyn på ytelse og at personvernet blir forbedret ved å bruke sensorer som kameraer på en robotfølgesvenn i stedet for å ha dem permanent montert i en bolig. Les mer i et innlegg i Dagens Næringsliv 23. september 2016. Vi har også publisert en vitenskapelig artikkel om etiske sider ved robotikk og kunstig intelligens, og den oppsummert i en kronikk i Dagens Næringsliv 14. juli 2018.
Les mer på den engelske prosjektsiden.