Monday, March 12, 2018

Thoracic Surgery Social Media Network #TSSMN

The next Thoracic Surgery Social Media Network #TSSMN Twitter chat is scheduled for Tues March 20 at 5 pm Eastern. Follow @TSSMN and the hashtag #TSSMN

The topic: resident assessment and autonomy in cardiothoracic surgery

Be sure to read the article titled, "Resident Autonomy in the Operating Room: Expectations Versus Reality" and "Teaching operative cardiac surgery in the era of increasing patient complexity: Can it still be done?"

It's great to see trainees leveraging social media to discuss important issues that need to be discussed openly.

You can read more about the spirit of #TSSMN in this article, titled "Thoracic Surgery Social Media Network: Bringing thoracic surgery scholarship to Twitter."

Wednesday, March 7, 2018

CMS announces MyHealthEData Initiative and Medicare Blue Button 2.0

Here is one of the biggest announcements that came out of HIMSS 2018: the MyHealthEData Initiative launched by CMS.

MyHealthEData aims to empower patients by ensuring that they control their healthcare data and can decide how their data is going to be used, all while keeping that information safe and secure. The overall government-wide initiative is led by the White House Office of American Innovation with participation from the U.S. Department of Health and Human Services (HHS) – including its Centers for Medicare & Medicaid Services (CMS), Office of the National Coordinator for Health Information Technology (ONC), and National Institutes of Health (NIH) – as well as the U.S. Department of Veterans Affairs (VA). MyHealthEData will help to break down the barriers that prevent patients from having electronic access and true control of their own health records from the device or application of their choice. This effort will approach the issue of healthcare data from the patient’s perspective.

Medicare’s Blue Button 2.0 is a new and secure way for Medicare beneficiaries to access and share their personal health data in a universal digital format. Medicare’s Blue Button 2.0 will allow a patient to access and share their healthcare information, previous prescriptions, treatments, and procedures with a new doctor which can lead to less duplication in testing and provide continuity of care. 

CMS launches “Blue Button 2.0” tool, calls on all health insurers to make data available to patients

Wednesday, February 14, 2018

FDA authorizes marketing of first blood test to aid in the evaluation of concussion in adults

Advances in technology are leading to a host of innovations around reducing and detecting concussions.

Today, the FDA authorized the marketing of the first blood test to evaluate mild traumatic brain injury (mTBI), commonly referred to as concussion, in adults.

The FDA reviewed and authorized for marketing the Banyan Brain Trauma Indicator in fewer than 6 months as part of its Breakthrough Devices Program.

The Brain Trauma Indicator works by measuring levels of proteins, known as UCH-L1 and GFAP, that are released from the brain into blood and measured within 12 hours of head injury. Levels of these blood proteins after mTBI/concussion can help predict which patients may have intracranial lesions visible by CT scan and which won’t. Being able to predict if patients have a low probability of intracranial lesions can help health care professionals in their management of patients and the decision to perform a CT scan. Test results can be available within 3 to 4 hours.

More information here.

Thursday, February 8, 2018

Using heart rate data (and machine learning) to detect diabetes?

A very interesting study seems to suggest that continuous heart data could identify patients who have diabetes. You can read more about this story on Wired:
... at the annual AAAI Conference on Artificial Intelligence in New Orleans, digital health-tracking startup Cardiogram presented research suggesting the Apple Watch’s heart rate sensor and step counter can make a good guess at whether or not a person has diabetes—when paired with the right machine-learning algorithms, of course.
In 2013, researchers at UCSF launched the Health eHeart study and registered close to 200,000 participants. About 40,000 opted to link their health information with their Cardiogram app. The DeepHeart neural network was trained to spot patterns and trends linked to human disease. Using semi-supervised sequence learning (artificial intelligence), the machine interpreted patterns of heart rate variability and was able to identify patients with diabetes 85% of the time.

I find myself wondering if some of this may be related to certain pharmacologic agents such as beta blockers...
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