It is impossible to meet the requirements of a community as a whole by developing the capacity to listen attentively to and make sense of the patient experience. Using natural language processing (NLP) and an in-house built artificial intelligence (AI) analytics engine, researchers extracted the most frequently used concepts and terms from social media data. AI technology was applied to social media discussions on the condition to learn about what gout patients and their communities have to say. Although it has been shown that gout is linked to mental health issues, the causes of this link are still poorly understood. This is an area where the gout community has a need that needs to be met. An artificial intelligence technology was utilized to learn about the gout community’s emotional well-being. Their system employed several natural language processing methods to determine what words and ideas were most frequently used in a conversation. Investigators looked at a closed Facebook support group called The Gout Support Group of America, which had 12,992 members from 99 countries and 8,500 posts/comments collected in 2021, and they looked at a public subreddit called r/gout, which had 100,000 posts/comments from 9,416 members over more than 10 years (2011-2022). The AI platform detected conversations with a high chance (score >0.90) of discussing ‘mental health.’ About 4% of posts from these massive gout forums were about mental health in general, with 38% of those posts referring to stress, 22% to depression, and 16% to anxiety. Then, they removed statements that did not have a high enough likelihood of discussing ‘management’ (score >0.99); this left them with around 25% of the original statements. Urgent and primary care were common themes in these “high-management” discussions, published on both Facebook and Reddit. About 0.5% (or about 1/200) of the “high-management” utterances made reference to “urgent care,” and 0.6% (or about 1/150) made reference to “primary care.” Comparing interactions in “urgent care” and “primary care,” they looked at the speakers’ emotional expressions. The results showed that statements about “primary care” were more upbeat than those about “urgent care,” with a 2.5:1 positive-to-negative word ratio in the former vs. a 1:2 ratio in the latter. What’s more, ‘trust’ was the most often identified affective term in ‘primary care’ talks (6% of all words), whereas ‘fear’ was the most frequently identified affective word in ‘urgent care’ (11% of a ll words). Study group used an advanced AI system to categorize the topics and emotions discussed in 2 major social media groups dedicated to the topic of gout. Stress, depression, and anxiety were the most commonly reported forms of mental health, making up about 4% of all posts/comments. Care at a primary care clinic was related to “trust,” while care at an urgent care clinic was most connected with “fear” among gout patients’ comments about their emotional state. Traditional disease treatment methods sometimes need to be revised when it comes to understanding the junction of mental health and disease management; this is where analyzing real-world information from social media talks about gout can be extremely useful.

 

Source: acrabstracts.org/abstract/real-world-evidence-from-social-media-provides-insights-into-patient-mental-health-outcomes-in-the-management-of-gout/