Fidest – Agenzia giornalistica/press agency

Quotidiano di informazione – Anno 31 n° 344

Health Care Analytics Case Competition

Posted by fidest press agency su martedì, 19 novembre 2019

The name in the first paragraph, the third paragraph and the photo caption is corrected to read: Ozgur Cetinok (instead of: Ozguk Cetinok).The corrected release reads:The student team of Ozgur Cetinok, Leah Kelly, and Erica Millwater from the University of California, Los Angeles (UCLA) has won the $30,000 First Place prize in the Humana-Mays Health Care Analytics 2019 Case Competition sponsored by health and well-being company Humana Inc. (NYSE: HUM) and Mays Business School at Texas A&M University.
Over 1,300 masters level students representing over 80 major universities in the U.S. registered for the national competition to compete for $52,500 in total prizes. The third annual competition was open to all accredited educational institutions based in the United States. Full-time and part-time master’s students from accredited Master of Science, Master of Arts, Master of Information Systems, Master of Public Health, Master of Business Administration programs, or other similar master’s programs in business, healthcare, or analytics, were eligible to enter.Ozgur Cetinok, Leah Kelly, and Erica Millwater received the top prize following a presentation Thursday, Nov. 14 to an executive panel of judges at Texas A&M’s Mays Business School’s CityCentre Houston location. The Second Place prize of $15,000 was awarded to Saurabh Annadate and Tanya Tandon from Northwestern University, while the Third Place prize of $7,500 was presented to Hong Gao, Shuyu Wang and Jie Yang from New York University (NYU). The analytics case received by the students was designed to be multi-faceted and complex, similar to a real-world business problem. This year’s competition focused on chronic pain and the treatment of this condition through long-term opioid therapy, which has increased dramatically over the past two decades. According to the Centers for Disease Control and Prevention, as many as 1 in 4 patients receiving long-term opioid therapy in a primary care setting will struggle with opioid disorder. Using de-identified data, the students were asked to predict long-term opioid therapy post initial treatment. The goal is to identify patients at risk for continued long-term use of opioid therapies allowing for early intervention.

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