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Digitizing the Pre-consultation Experience: Impacts and Design Recommendations

Brenna Li, Liam of Bakar, Anna Kirik, Jiaqi Guo, Alex Mariakakis, Khai N. Truong

2026Year
2Citations

Abstract

Clinical pre-consultation, where patients share health information prior to an appointment, can offer better patient-centered care by freeing time for meaningful patient–physician conversations. Conversational agents powered by large language models (LLMs) can automate this process to make it more scalable and consistent, but doing so can cause information overload that exacerbates physicians’ workload as they spend time parsing through data. This paper examines the opportunities and challenges of using pre-consultation agents to mediate information transfer between patients and physicians, with the aim of producing clinically useful yet patient-driven summaries that capture their histories and concerns. Through sessions with both patients and physicians, we show that automatically generated pre-consultation summaries can increase patients’ confidence and sense of control over their health information while fostering a more collaborative dynamic. We conclude with design recommendations for integrating pre-consultation agents into clinical workflows.

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