Medicare’s Program Integrity Transformation

Medicare has been considered a “High Risk program” by the Government Accountability Office since 1990 due to its size, complexity and susceptibility to improper payments. In 2018, improper payments accounted for 5% of the total $616.8 million of Medicare’s net costs.


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Go Beyond the Cloud with Rachel Wang

Meet Rachel Wang, a data scientist at Softheon.

Born in the province of Shendong in northeastern China, Rachel came to the United States to pursue education and professional development. She already studied mathematics and economics at a college in China but sought further mastery in mathematics. Thinking mathematically comes easily to her and she enjoys how “every problem is a math problem.”


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MESC Recap: Day 3 & 4

The Medicaid Enterprises Systems Conference (MESC) ended on a positive note by discussing how analytics has been used by state Medicaid agencies to solve the opioid crisis and how Florida has successfully modernized its member service experience.

In case you were not able to attend, here is our recap of the key topics discussed:


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Debating A.I.’s Role in Health Care

No longer a hypothetical tool, artificial intelligence (A.I.) has planted its feet in health care globally. It is already being used by Google in some of India’s hospitals to fight diabetic blindness, and the Food and Drug Administration (FDA) approved the first machine learning (ML) algorithm to measure how much blood flows through the heart in 2018.  


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The Rise of Digital Health Technology

In the hype of technological advancements, healthcare innovation presents exciting opportunities to enhance quality of life. As individuals age, an intensely personal experience, people can get lost in the rapid transformation. It is vital to put consumers at the center of the member experience, provide a “one stop shop” for all, integrate member validation and enrollment, and drive innovation through iterative development.  


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Artificial Intelligence and Machine Learning Made Simpler

From rumors of radiologists being replaced to Twitter bots going rogue, artificial intelligence (AI) and machine learning (ML) have become buzzwords we’ve all heard about. There has been much discussion on the breakthroughs both could bring to how fields traditionally function, like patient diagnosis in health care. Many have also warned of their potential and history of bias and abuse. Given the gravity surrounding AI and ML, and with much help from an excellent Ars Technica piece by Haomiao Huang, we’ve created a guide geared towards non-technical individuals to help them learn more about the basics of these technologies.


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