Quality Measurement of Consumer Health Questions: Content and Language Perspectives.

Background: Health information consumers increasingly rely on question-and-answer (Q&A) communities to address their health concerns. However, the quality of questions posted significantly impacts the likelihood and relevance of received answers. Objective: This study aims to improve our understanding of the quality of health questions within web-based Q&A communities. Methods: We develop a novel framework for defining and measuring question quality within web-based health communities, incorporating content- and language-based variables. This framework leverages k-means clustering and establishes automated metrics to assess overall question quality. To validate our framework, we analyze questions related to kidney disease from expert-curated and community-based Q&A platforms. Expert evaluations confirm the validity of our quality construct, while regression analysis helps identify key variables. Results: High-quality questions were more likely to include demographic and medical information than lower-quality questions (P <.001). In contrast, asking questions at the various stages of disease development was less likely to reflect high-quality questions (P <.001). Low-quality questions were generally shorter with lengthier sentences than high-quality questions (P <.01). Conclusions: Our findings empower consumers to formulate more effective health information questions, ultimately leading to better engagement and more valuable insights within web-based Q&A communities. Furthermore, our findings provide valuable insights for platform developers and moderators seeking to enhance the quality of user interactions and foster a more trustworthy and informative environment for health information exchange. [ABSTRACT FROM AUTHOR]