Timeliness and Appropriateness of Cross-Sectional Study Design in the Study of Online Health Information Seeking Using AI

Abstract

Users using AI such as ChatGPT and Gemini for health consultation and self-diagnosis is becoming a popular way of health information-seeking for the public, which marks a noticeable change in the ways people interact with health information. Under such a status quo, it leads to a need to systematically study on the users' behaviour in key factors including perceived trust, motivations and levels of satisfaction. This study proposes that the Cross-sectional Study Design is the timely and appropriate approach to explore in this new-appearing phenomenon. In addition, to sufficiently explain the use of cross-sectional studies in the research of using AI for health information-seeking, this study also constructs a preliminary conceptual research framework based on the Planned Risk Information Seeking Model (PRISM) and Health Belief Model (HBM) with several exemplary research questions, aiming to discuss how factors from different groups including perceived trust on AI-generated information, usage motivations and the perceived accuracy of information affect their behavioural intentions.