Category Archives: Digital Health

eHealth Exchange selects InterSystems to support expansion

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InterSystems, a global leader in information technology platforms for health, business, and government applications, announced it has been selected by the eHealth Exchange and took the challenge.

The eHealth Exchange has unique roots and began as a federal program initiative under the Office of the National Coordinator for Health IT (ONC), which helped to incubate it as part of the NwHIN efforts. The eHealth Exchange is now supported by The Sequoia Project and has blossomed into a rapidly growing community of exchange partners.

Consider what happened with the DICOM protocol, radiology industry agreed the same image protocol to exchange radiological images through different platforms: High Resolution images flowing through the network, available also for post-processing tasks.
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This harmonization generated a branch industry from scratch: Carestream, GE Healthcare, Philips as well as Siemens Healthineers: eHealth Exchange is doing the same, put in place a kind of standard to share healthcare information accordingly.

Thinking future applications, an enourmous amount of healthcare data could be used for analysis via Deep Learning AI (Artificial Intelligence) to identify inferences not visibile to humans: diseases clusterization, possible risk causes and trends, the applications could be incredible.

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From a patient stand point, the advantage of a common health data protocol could be the possibility to access to any of the health information, collected in different facilities, avoiding any possible missing data: give to your own phisician access to your own data automatically, without taking old-fashioned CD could be fantastic.

I do think we’ll hear soon news from InterSystems whcih has the technological capabilities to reach the goal.

Apple Watch detects irregular heart beat in large U.S. study

apple_watch(Reuters) – The Apple Watch was able to detect irregular heart pulse rates that could signal the need for further monitoring for a serious heart rhythm problem, according to data from a large study funded by Apple Inc (AAPL.O), demonstrating a potential future role for wearable consumer technology in healthcare.

Researchers hope the technology can assist in early detection of atrial fibrillation, the most common form of irregular heart beat. Patients with untreated AF are five times more likely to have a stroke.

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Using Machine Learning to develop blood test for key Alzheimer’s biomarker

giornata-mondiale-alzheimer-2018-824-464Alzheimer’s disease, a terminal neurodegenerative disease, has historically been diagnosed based on observing significant memory loss. Recent research has shown that a biological marker associated with the disease, a peptide called amyloid-beta, changes long before any memory-related issues are apparent.

Examining the concentration of the peptide in an individual’s spinal fluid provides an indication of risk decades before any memory related issues occur. Unfortunately, accessing spinal fluid is highly invasive, requires an anaesthetist and is expensive to conduct on large segments of the population. Hence, there is a strong effort in the research community to develop a less invasive test, such as a blood test, that can yield information about Alzheimer’s disease risk.

A recent paper by my team at IBM Research – Australia, published today in Scientific Reports, used machine learning to identify a set of proteins in blood that can predict the concentration of amyloid-beta in spinal fluid. The models we built could one day help clinicians to predict this risk with an accuracy of up to 77%.

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Scientific ReportsA blood-based signature of cerebrospinal fluid Aβ1–42 status (Published: 11 March 2019)