To capture the complete patient experience, knowledge and perceptions among women with stress urinary incontinence (SUI) by conducting a large-scale digital ethnographic analysis of anonymous online posts.
Online posts were identified through data mining. First, 200 randomized posts were analyzed using grounded theory qualitative methods. To ensure full thematic discovery, we also applied a Latent Dirichlet Allocation (LDA) probabilistic topic modeling approach to the entire dataset of identified posts. LDA topics are represented as a distribution of words, similar to a “word cloud” which were manually reviewed to identify themes.
985 online posts by 762 unique users were extracted from 98 websites. There was significant overlap between the grounded theory and LDA identified themes. Our analysis suggests that these online communities help women manage the quality-of-life impact of their SUI, navigate specialty care, and reach a decision regarding surgical versus non-surgical management. Additionally, we identified risk factors, prevention strategies, and treatment recommendations discussed online.
Findings demonstrated patient values that may influence decision making when seeking care for SUI and choosing a treatment. Social media interactions provide insight into patient behaviors that are important to improve patient-centered care and decision making.
Women’s Experience with Stress Urinary Incontinence: Insights from Social Media Analytics.
