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December 20, 2019 -PrecISE Study Featured on UNC-Chapel Hill Podcast Episode

Dr. Michael Kosorok
Dr. Michael Kosorok

The relationship between machines and medicine has been growing stronger over the past century, according to Dr. Michael Kosorok, who serves as chairperson of the Biostatistics Department at the University of North Carolina Gillings School of Global Public Health. He noted that "clinical trials started around the mid-1900s" while discussing the evolution of patient care and other pertinent topics on Well Said, a weekly podcast produced by his collegiate colleagues. The distinguished professor believes that humanity is "at the very early stages of a precision medicine revolution" after decades of discrepant problem solving.

"We now have some analytical tools, including machine learning, that allow us to identity subgroups in a way that's reproducible," explained Kosorok. Those tools have become a crucial part of precision medicine, which he describes as "the study of finding ways to be able to treat different individuals differently in a way that’s best for them individually." The efficacy of medical treatments often varies between patients, and researchers that include Kosorok are using new methods made possible by technology to account for those discrepancies while tailoring treatment plans to individual medical histories.

A new clinical study that acknowledges the importance of precision medicine is the Precision Interventions for Severe and/or Exacerbation-Prone Asthma Network (PrecISE), which has been made possible by funding from the National Heart, Lung, and Blood Institute. Kosorok is currently serving as a senior consultant on that study, but his reasons for doing so may be personal as well as professional. As an asthmatic himself, he acknowledges that "there isn't a lot of knowledge available to help decide what treatment to give in certain situations."

With approximately 2.5 million people currently suffering from asthma in the United States, Kosorok and his cohorts intend to keep using machines to make the medicine work better for each of those individuals in what he describes as a "data-driven approach." That approach is expected to yield information on which treatments are most effective and which patients should receive them. According to Kosorok, "there's a lot of evidence to suggest that this is really important."

Listen to the podcast and see a text transcript here.

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