Managing HIV During the COVID-19 Pandemic: A Study of Help-Seeking Behaviors on a Social Media Forum.

Although numerous editorials claim the COVID-19 pandemic has disproportionately impacted vulnerable populations, particularly those affected by HIV, these claims have received limited empirical evaluation. We analyzed posts to Reddit's r/HIVAIDS from January 3, 2012 through April 30, 2022 to (a) assess changes in the volume of posts during the pandemic and (b) determine the needs of HIV affected communities. There were cumulatively 100% (95%CI: 75-126) more posts than expected since the US declared a pandemic emergency. The most prevalent themes in these posts were for obtaining an HIV + diagnosis (representing 34% (95%CI:29-40) of all posts), seeking HIV treatment (20%; 95%CI:16-25), finding psychosocial support (16%; 95%CI:12-20), and tracking disease progression (8%; 95%CI:5-11). Discussions about PrEP and PEP were the least common, representing less than 6% of all posts each. Social media has increasingly become an important health resource for vulnerable populations seeking information, advice, and support. Public health organizations should recognize how the lay public uses social media and collaborate with social media companies to ensure that the needs of help-seekers on these platforms are met.

Pharmacovigilance in the Age of Legalized Cannabis: Using Social Media to Monitor Drug-Drug Interactions Between Immunosuppressants and Cannabis-Derived Products.

INTRODUCTION: A clinical trial of Epidiolex®, the only US FDA-approved cannabis-derived consumer product (CDP), discovered an interaction with an immunosuppressant (tacrolimus) that led to drug toxicity, highlighting the unique intersection of prescription and commonly unregulated consumer products. OBJECTIVE: We aimed to identify if similar drug-drug interactions (DDIs) are occurring among the consumer CDP market, even though they cannot be identified through trials. METHODS: We searched Reddit for subreddits related to CDPs or health, resulting in 63,561,233 posts. From these, we identified 190 posts discussing both immunosuppressants and CDPs. Two blinded investigators evaluated the following. (1) Was there a concern about a potential DDI between consumer CDPs and immunosuppressants? (2) Was there a unique adverse event attributed to a DDI between consumer CDPs and immunosuppressants? RESULTS: Of these, 66 posts (35%) expressed concern about a potential DDI, such as "Hey, my partner wants to try my edibles … she's on Prograf [tacrolimus] and wants to talk to a stoner who's had a heart transplant." Four posts (2%) reported a unique DDI, such as "I have clinical results that are semi-anecdotal, showing the coordination to my halting substance use … It's the CBD. Shot my prograf to 30 at like 4 mg." Two of the four reported DDIs are similar to those first reported for Epidiolex. The remaining two reported DDIs include a potential cannabidiol (CBD)/sirolimus or delta-9-tetrahydrocannabinol (THC)/sirolimus interaction and a THC/tacrolimus interaction, both resulting in drug toxicity. CONCLUSION: This case study is the first to report on DDIs involving consumer CDPs, including both CBD and THC products, as well as a broader class of immunosuppressants. This demonstrates the risks associated with using consumer CDPs alongside prescription medications while highlighting the need for development of increased surveillance to monitor consumer CDPs for drug safety signals, as well as comprehensive regulations that take into account the unique characteristics of the consumer marketplace.

Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum.

Importance: The rapid expansion of virtual health care has caused a surge in patient messages concomitant with more work and burnout among health care professionals. Artificial intelligence (AI) assistants could potentially aid in creating answers to patient questions by drafting responses that could be reviewed by clinicians. Objective: To evaluate the ability of an AI chatbot assistant (ChatGPT), released in November 2022, to provide quality and empathetic responses to patient questions. Design, Setting, and Participants: In this cross-sectional study, a public and nonidentifiable database of questions from a public social media forum (Reddit's r/AskDocs) was used to randomly draw 195 exchanges from October 2022 where a verified physician responded to a public question. Chatbot responses were generated by entering the original question into a fresh session (without prior questions having been asked in the session) on December 22 and 23, 2022. The original question along with anonymized and randomly ordered physician and chatbot responses were evaluated in triplicate by a team of licensed health care professionals. Evaluators chose "which response was better" and judged both "the quality of information provided" (very poor, poor, acceptable, good, or very good) and "the empathy or bedside manner provided" (not empathetic, slightly empathetic, moderately empathetic, empathetic, and very empathetic). Mean outcomes were ordered on a 1 to 5 scale and compared between chatbot and physicians. Results: Of the 195 questions and responses, evaluators preferred chatbot responses to physician responses in 78.6% (95% CI, 75.0%-81.8%) of the 585 evaluations. Mean (IQR) physician responses were significantly shorter than chatbot responses (52 [17-62] words vs 211 [168-245] words; t = 25.4; P < .001). Chatbot responses were rated of significantly higher quality than physician responses (t = 13.3; P < .001). The proportion of responses rated as good or very good quality (≥ 4), for instance, was higher for chatbot than physicians (chatbot: 78.5%, 95% CI, 72.3%-84.1%; physicians: 22.1%, 95% CI, 16.4%-28.2%;). This amounted to 3.6 times higher prevalence of good or very good quality responses for the chatbot. Chatbot responses were also rated significantly more empathetic than physician responses (t = 18.9; P < .001). The proportion of responses rated empathetic or very empathetic (≥4) was higher for chatbot than for physicians (physicians: 4.6%, 95% CI, 2.1%-7.7%; chatbot: 45.1%, 95% CI, 38.5%-51.8%; physicians: 4.6%, 95% CI, 2.1%-7.7%). This amounted to 9.8 times higher prevalence of empathetic or very empathetic responses for the chatbot. Conclusions: In this cross-sectional study, a chatbot generated quality and empathetic responses to patient questions posed in an online forum. Further exploration of this technology is warranted in clinical settings, such as using chatbot to draft responses that physicians could then edit. Randomized trials could assess further if using AI assistants might improve responses, lower clinician burnout, and improve patient outcomes.

Evaluating Artificial Intelligence Responses to Public Health Questions.

This cross-sectional study analyzes the quality of ChatGPT responses to public health questions.