Leveraging Online Patient Forums to Understand Breast Reduction Concerns: A Machine-Learning Analysis of 3,078 Patient Questions Over 15 Years.

Background: Patients are increasingly using social media and online forums to learn about plastic surgery, which can influence their expectations. Understanding patient concerns on these platforms will facilitate productive clinic discussion and ensure patients are receiving accurate, evidence-based information.

Objective: We analyzed breast reduction questions posted on RealSelf (Seattle, WA), an online plastic surgery forum.

Methods: The website https://www.realself.com/questions/breast-reduction was accessed on June 9, 2023. Posting date and poster self-reported location were extracted. Question header and text were manually reviewed. Questions were categorized by timing (preoperative vs. postoperative) and topic. Regional and temporal trends were assessed. A machine-learning (ML) algorithm was applied to identify the top (most representative) preoperative and postoperative questions.

Results: 3,078 questions from August 2008 to May 2023 were analyzed. Questions most frequently originated from the Southern United States (34.5%) and were asked preoperatively (58.4%). The most common question topics were Postoperative Care (24.9%), Postoperative Appearance/Sensation (15.7%), and Surgical Logistics (10.2%). The distribution of topics varied significantly between location (p<0.01), with topics like Insurance (p<0.01) more likely to be asked in the South.

Conclusions: This is the first study to leverage ML workflows to analyze a large volume of patient questions about breast reduction from an online plastic surgery forum. Analyzing patient questions on social media and online forums like RealSelf with ML techniques can provide valuable insight into common concerns and informational gaps surrounding plastic surgery. Plastic surgeons should consider these results to guide patient conversations, combat misinformation, and facilitate deliverance of efficient care.