Quality Measurement of Consumer Health Questions: Content and Language Perspectives

Abstract

BackgroundHealth 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. ObjectiveThis study aims to improve our understanding of the quality of health questions within web-based Q&A communities. MethodsWe 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. ResultsHigh-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). ConclusionsOur 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.