Algorithmic Short-Video Platforms and Health Decision-Making in Rhinitis: A Multi-Platform Digital Health Analysis

Background Short-form video platforms have become major sources of health information, yet concerns persist regarding misinformation quality and its influence on patient decision-making, particularly within algorithm-driven digital ecosystems where popularity and informational quality may diverge. Methods A multi-platform cross-sectional analysis was conducted on 179 short videos from TikTok and Bilibili using five validated assessment instruments: Global Quality Scale (GQS), modified DISCERN (mDISCERN), Patient Education Materials Assessment Tool (PEMAT), Video Information Quality Index (VIQI), and JAMA benchmark criteria. In parallel, a questionnaire survey of 106 patients with rhinitis assessed social media exposure and its impact on healthcare decisions. Multivariable regression analyses identified predictors of video quality and behavioral influence. This study integrates digital content analytics with patient-reported behavioral outcomes. Results Videos produced by professional creators demonstrated significantly higher quality, transparency, and reliability than those by non-professionals (all p < .001), while engagement metrics showed weak correlations with quality indicators. Patient experience–sharing and symptom-focused videos exerted the strongest influence on healthcare decisions despite lower informational quality. Higher educational attainment was independently associated with increased susceptibility to video-influenced decisions. Engagement metrics were weakly associated with informational quality, indicating a decoupling between algorithmic popularity and evidence-based credibility. Conclusion Short-form video platforms function as algorithmically mediated health environments in which popularity poorly reflects informational rigor, and experiential narratives disproportionately shape patient behavior. These findings underscore the need for platform governance reforms, clinician participation in digital ecosystems, and evidence-based digital health communication strategies to mitigate misinformation-driven behavioral risks.