Tag Archives: Digital Health

Pack Health won Salesforce Healthcare Experience Trailblazer Award

pack-healthpngPack Health, a leading digital health coaching platform for chronic care monitoring, has been named the winner of Salesforce’s Healthcare Experience Trailblazer Award for the success of its Health Advising program and commitment to improving the patient experience in healthcare.

Pack Health customized the Salesforce platform to develop and deliver diagnosis-specific, one-on-one coaching programs for individuals with chronic conditions. The company then integrated evidence-based content, metrics and devices into the platform to optimize and augment its human-to-human engagement model.

phfeature2With weekly coaching calls and personalized follow up, Pack Health members enjoy a better and more connected healthcare experience, and improve their health behaviors and outcomes as measured by validated Patient Reported Outcomes Measures and clinical measures.

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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)