Sairan Li, Speaker at Epidemiology Conferences
Master's Researcher

Sairan Li

Chinese Academy of Medical Sciences and Peking Union Medical College, China

Abstract:

With a global shortage of primary healthcare physicians—particularly in resource-limited settings—large language models (LLMs) have the potential to support and enhance patients’ health awareness. Here we developed P&P Care (Population Medicine and Public Health), an LLM-powered primary care chatbot using a dual-track role-play codesign framework where community stakeholders and researchers simulated each another’s perspectives across four phases: contextual understanding; cocreation; testing and refinement; and implementation and evolution. The codesigned chatbot was integrated with e-learning modules and tested in a randomized controlled trial. The trial included 2,113 participants (1,052 women and 1,061 men) from urban and rural areas across 11 Chinese provinces who were randomly assigned to receive a consultation either with preparatory e-learning via the P&P Care or without. The study met its primary endpoint with the e-learning group showing significantly higher objective health awareness (mean score 2.95 ± 1.22) compared with the consultation-only group (mean score 2.34 ± 1.02; P < 0.001). Codesign offers a scalable solution for deploying LLMs in resource-limited settings. Chinese Clinical Trial Registry identifier: ChiCTR2500098101.

Biography:

Sairan Li is a Master's researcher at Peking Union Medical College, where their work sits at the intersection of statistics, artificial intelligence, and healthcare. With a background in Statistics from Beijing Normal University, they have developed a deep curiosity for using data-driven methods to understand complex medical Problems — From how diseases evolve and interact over time, to how AI can make healthcare more accessible and equitable. Their research spans two complementary directions: mining large-scale health data to uncover hidden patterns of multimorbidity progression, and implementing large language models in real-world clinical workflows through rigorous randomized trials.

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