For years medical researchers have used the online world to recruit remote study participants, search for relevant studies and get other data points.  But now, with machine-learning a hot topic, researchers are turning to social media posts to improve patient care.  Machine learning algorithms can cull social media postings to get insight into patient experiences which might not otherwise be disclosed.  “Collecting abundant social media data is cost-effective, does not involve burdening participants, and is available in real time,” Graciela Gonzalez-Hernandez, an associate professor at the University of Pennsylvania’s Perelman School of Medicine, told The Wall Street Journal .  One recent study, for example, analyzed social media posts about a drug called buprenorphine, which is used to help opioid users from going through withdrawal.  The study showed a concern among Reddit subscribers that the drug would cause extreme withdrawal symptoms—known as precipitated withdrawal—for people who had used fentanyl, a drug that is often mixed with heroin and is helping fuel the opioid crisis.  Many of the social media posts showed frustration that their doctor did not understand precipitated withdrawal.  This is information which would likely not come out, particularly if the study was focusing on questioning doctors about their patient’s reaction to withdrawals.


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