More than Decision Support: Exploring Patients' Longitudinal Usage of Large Language Models in Real-World Healthcare-Seeking Journeys
Abstract
Large language models (LLMs) have been increasingly adopted to support patients’ healthcare-seeking in recent years. While prior patient-centered studies have examined the capabilities and experience of LLM-based tools in specific health-related tasks such as information-seeking, diagnosis, or decision-supporting, the inherently longitudinal nature of healthcare in real-world practice has been underexplored. This paper presents a four-week diary study with 25 patients to examine LLMs’ roles across healthcare-seeking trajectories. Our analysis reveals that patients integrate LLMs not just as simple decision-support tools, but as dynamic companions that scaffold their journey across behavioral, informational, emotional, and cognitive levels. Meanwhile, patients actively assign diverse socio-technical meanings to LLMs, altering the traditional dynamics of agency, trust, and power in patient-provider relationships. Drawing from these findings, we conceptualize future LLMs as a longitudinal boundary companion that continuously mediates between patients and clinicians throughout longitudinal healthcare-seeking trajectories.
Sahiti Dharmavaram
Master’s Research Intern
I am an MS in Computer Science student at Columbia University on the Vision, Graphics, Robotics, and Interaction track, and a part-time Forward Deployed Engineer at AIntropy building LLM agent systems for real-world workflows. At the SEA Lab I study how people and AI coexist in healthcare and wellness, with work at CHI 2026 on patients’ longitudinal use of LLMs and on personalizing mindfulness meditation through expert-aligned multi-agent systems.
