A community-codesigned LLM-powered chatbot for primary care: a randomized controlled trial
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; <i>P</i> < 0.001). Codesign offers a scalable solution for deploying LLMs in resource-limited settings. Chinese Clinical Trial Registry identifier: ChiCTR2500098101.
Authors
- Frances S Mair (ORCID: https://orcid.org/0000-0001-9780-1135)
- Wangyue Chen (ORCID: https://orcid.org/0009-0003-2931-6121)
- Yaneng Li
- Sairan Li
- Boyou Wu
- Shuya Zhou
- En Meng
- Shasha Han
- Changjie Yu
- Jie Zhou
- Qirui Huang
- Lei Zou
- Xinge Tao
- Jinchao Zhang
- Muzi Shen
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking University (CN)
- Harbin Institute of Technology (CN)
- Tencent (China) (CN)
- Beijing Normal University (CN)
- Beijing Academy of Artificial Intelligence (CN)
- East China Normal University (CN)
- University of Glasgow (GB)
Publication Details
- Journal
- Nature Health
- Published
- 2026-01-19
- DOI
- https://doi.org/10.1038/s44360-025-00021-w
- Citations
- 2
- Primary Topic
- Digital Mental Health Interventions
- Type
- article
- Field-Weighted Citation Impact
- 80.21