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.

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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
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article

A community-codesigned LLM-powered chatbot for primary care: a randomized controlled trial

Frances S Mair, Wangyue Chen, Yaneng Li, Sairan Li et al.
2 citations
Nature Health
Digital Mental Health Interventions
80.21
article

A community-codesigned LLM-powered chatbot for primary care: a randomized controlled trial

Frances S Mair, Wangyue Chen, 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
article en
2 citations

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; <i>P</i> < 0.001). Codesign offers a scalable solution for deploying LLMs in resource-limited settings. Chinese Clinical Trial Registry identifier: ChiCTR2500098101.

Nature HealthVol. 1(2)
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)
Openalex Percentile: Top 0%
Digital Mental Health Interventions
80.21
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