March 2018

Vets Views

Page 3 


Retirement Services Officers (RSOs)

Do you have questions on benefits, SBP, Retiree Appreciation Days or anything else retirement-related? Then contact the RSO for your area or go to the Army Retirement Services website http://www.armyg1.army.mil/retire (Note: That’s the number 1 after the g).

Sister Service Retiree Publications

Air Force Afterburner: http://www.retirees.af.mil/afterburner/

Coast Guard Evening Colors: http://www.uscg.mil/ppc/retnews/

Marine Corps Semper Fi: https://www.manpower.usmc.mil, then click on “Semper Fidelis Online” under “News and Features” Navy Shift Colors: http://www.npc.navy.mil/ReferenceLibrary/Publications


February 22, 2018


'We want to connect with Veterans before they know they need us’: VA

launches Concierge for Care program

WASHINGTON — Today the U.S. Department of Veterans Affairs (VA) announced the launch of Concierge for Care, a health-care enrollment initiative that connects with former service members shortly after they separate from the service.

“Our goal is to give transitioning service members one less thing to worry about,” said VA Secretary David J. Shulkin. “We know that more than a third of Veterans who haven’t yet visited our facilities indicated they are not aware of VA health care benefits, while a quarter report they do not know how to apply.”

As part of Concierge for Care, VA staff members are personally contacting recently separated service members to answer questions, process their health-care enrollment applications over the phone and help schedule eligible Veterans’ first VA medical appointment, if needed.

Each week, VA receives a list of separating service members from the Department of Defense. The goal is to contact them within a month of discharge.

Certain Veterans who served in a theater of combat operations are eligible to enroll and receive cost-free health care for medical conditions related to their military service during the five-year period after discharge.

Information about VA health care and the application process can be found at https://www.vets.gov/health-care/apply/.



February 21, 2018


VA Partners With DeepMind to Build Machine Learning Tools to

Identify Health Risks for Veterans

WASHINGTON — Today the Department of Veterans Affairs (VA) announced that it has approved a medical research partnership with DeepMind to address the global issue of patient deterioration during hospital care, which accounts for 11 percent of in-hospital deaths around the world. The partnership will focus on analyzing patterns from approximately 700,000 historical, de-personalized health records to develop machine learning algorithms that will accurately identify risk factors for patient deterioration and predict its onset. Initial work will be focused on identifying the most common signs of risk, like acute kidney injury, a problem that can lead to dialysis or death, but is preventable if detected early.

“Medicine is more than treating patients’ problems,” said VA Secretary David J. Shulkin. “Clinicians need to be able to identify risks to help prevent disease. This collaboration is an opportunity to advance the quality of care for our nation’s Veterans by predicting deterioration and applying interventions early.”

Eventually, similar approaches will be applied to other signs of patient deterioration, leading to improved care for many more patients, with fewer people developing serious infections and conditions — ultimately saving lives.

“We are proud to partner with the Department of Veterans Affairs on this important challenge,” said Mustafa Suleyman, co-founder of DeepMind. “This project has great potential intelligently to detect and prevent deterioration before patients show serious signs of illness. Speed is vital when a patient is deteriorating: The sooner the right information reaches the right clinician, the sooner the patient can be given the right care.”

DeepMind is the world leader in artificial intelligence research. It has already partnered with leading hospitals in the United Kingdom to apply its innovative machine-learning algorithms to research projects looking at eye disease, head and neck cancer, and mammography.