eHIT leverages mobile technologies to enhance remote self-care services

eHIT Health GatewayeHIT Health Gateway platform offers a mobile solution to collect measurements directly from the measuring devices and seamlessly transfer the collected data to the healthcare personnel for further analysis. According to the type of monitoring instrument used, measured data are easily transferred to the patient's mobile device via Bluetooth, infrared or cable connection.

A single mobile device can collect, store and transfer information from different measuring devices. This also makes it possible to integrate devices from different manufacturers. For example, a blood pressure monitor, a weighing scale and a glucometer can be used to collect and register key information in diabetes care.

The patient can browse the results from a list or have them displayed in a clear graphical form directly on the screen of the mobile device. This gives the patients an immediate overview of the treatment progress.

Results are linked together and immediately forwarded by using GPRS, GSM or 3G technologies to the healthcare provider, where they are available for review. Following the analysis, the doctor or other healthcare professional can send feedback to the patient. As a result, patients not only remain informed about their health status via the information displayed on the mobile device, but can also quickly adapt their treatment, diet or exercise programme in response to the medical advice they receive from their healthcare provider.

The combination of self-monitoring devices with mobile technology presents several advantages in comparison with traditional monitoring methods. Remote measurement and monitoring is made possible regardless of a patient's location. Accurate measurement results are available in real time and in the required format. The patients' treatment can be monitored and quickly adapted to a change in health status as they can receive feedback regarding their treatment almost immediately. Furthermore, by being able to follow the progress of their treatment, patients are more motivated to comply with prescribed therapy. Additionally, evidence-based process traceability is provided.

For further information please contact:
eHIT Ltd
Microkatu 1
P.O. Box 1199
Kuopio 70211
Finland
http://www.ehit.fi
This email address is being protected from spambots. You need JavaScript enabled to view it.

Most Popular Now

Is Your Marketing Effective for an NHS C…

How can you make sure you get the right message across to an NHS chief information officer, or chief nursing information officer? Replay this webinar with Professor Natasha Phillips, former...

Welcome Evo, Generative AI for the Genom…

Brian Hie runs the Laboratory of Evolutionary Design at Stanford, where he works at the crossroads of artificial intelligence and biology. Not long ago, Hie pondered a provocative question: If...

We could Soon Use AI to Detect Brain Tum…

A new paper in Biology Methods and Protocols, published by Oxford University Press, shows that scientists can train artificial intelligence (AI) models to distinguish brain tumors from healthy tissue. AI...

Telehealth Significantly Boosts Treatmen…

New research reveals a dramatic improvement in diagnosing and curing people living with hepatitis C in rural communities using both telemedicine and support from peers with lived experience in drug...

AI can Predict Study Results Better than…

Large language models, a type of AI that analyses text, can predict the results of proposed neuroscience studies more accurately than human experts, finds a new study led by UCL...

Research Study Shows the Cost-Effectiven…

Earlier research showed that primary care clinicians using AI-ECG tools identified more unknown cases of a weak heart pump, also called low ejection fraction, than without AI. New study findings...

Using AI to Treat Infections more Accura…

New research from the Centres for Antimicrobial Optimisation Network (CAMO-Net) at the University of Liverpool has shown that using artificial intelligence (AI) can improve how we treat urinary tract infections...

New Guidance for Ensuring AI Safety in C…

As artificial intelligence (AI) becomes more prevalent in health care, organizations and clinicians must take steps to ensure its safe implementation and use in real-world clinical settings, according to an...

Remote Telemedicine Tool Found Highly Ac…

Collecting images of suspicious-looking skin growths and sending them off-site for specialists to analyze is as accurate in identifying skin cancers as having a dermatologist examine them in person, a...

Philips Aims to Advance Cardiac MRI Tech…

Royal Philips (NYSE: PHG, AEX: PHIA) and Mayo Clinic announced a research collaboration aimed at advancing MRI for cardiac applications. Through this investigation, Philips and Mayo Clinic will look to...

New Study Reveals Why Organisations are …

The slow adoption of blockchain technology is partly driven by overhyped promises that often obscure the complex technological, organisational, and environmental challenges, according to research from the University of Surrey...

Deep Learning Model Accurately Diagnoses…

Using just one inhalation lung CT scan, a deep learning model can accurately diagnose and stage chronic obstructive pulmonary disease (COPD), according to a study published today in Radiology: Cardiothoracic...