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AI and behavioral sciences to pair people with healthcare

August 9, 2018 News

Posted Aug 9, 2018 by Tim Sandle
Change Healthcare has developed a new solution that uses AI and behavioral sciences to accurately identify and pair the right people with the medical benefits suitable for them.

In the U.S. for-profit, insurance-driven healthcare system a conundrum arises, which presents a complex challenge, with health plans enroll beneficiaries who are covered by both Medicare and Medicaid. These people are known as dually eligible beneficiaries.

The proportion of people who are Medicare beneficiaries and who are also enrolled in Medicaid varies at any given time, but it runs into several million people. Medicare is a national health insurance program, administered by the Centers for Medicaid and Medicare Services of the U.S. federal government. It provides health insurance for Americans aged 65 and older who have worked and paid into the system through the payroll tax. Continue reading →

EHR analytics help identify new antidepressant users

April 30, 2018 News

By Jennifer Bresnick

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April 30, 2014 – By using clinical analytics to scan EHR records from patients in Minnesota, a team of researchers at the Mayo Clinic were able to identify new users of common antidepressant drugs, a prerequisite step in the quest for more accurate, automated ways to stratify patients for population health management.  With sharp increases in antidepressant prescriptions over the past few decades, and a greater focus on integrating mental health care into primary practice, researchers are interested in gaining insight into the underlying reasons why patients are taking these medications, along with their effectiveness, side effects, and economic impacts.

In Olmstead County, Minnesota, the vast majority of patient records are linked together by the Rochester Epidemiology Project (REP), which contains data on inpatient and outpatient visits for those patients who have approved use of their data for research.  While the REP holds a wealth of data, the information was collected in EHRs during routine medical care, and may not be optimized or properly categorized for use by researchers conducting clinical trials or clinical analytics.  To understand how algorithms could help pick out relevant information, Dr. William V. Bobo, of the Department of Psychiatry and Psychology at the Mayo Clinic, and his team conducted a computerized sweep of the files, followed by a manual review, to identify antidepressant users in the patient population. Continue reading →

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