@article{Wang2026, 
author = {Yang Wang and Chuan Zou and Mengruo Guo and Liuhua He and Xuhang Lu and Xianzhong Gui and Zhijie Xu and Kai Lin and Hua Jin and Mi Yao and Hui Yang and Dehua Yu},
title = {A five-stage, AI-assisted approach for general practitioners to formulate practice-based research questions},
year = {2026},
journal = {Chinese General Practice Journal},
volume = {3},
number = {2},
keywords = {General practice, Artificial intelligence, Research question, Research methodology, Prompt engineering},
url = {https://www.sciopen.com/article/10.1016/j.cgpj.2026.100111},
doi = {10.1016/j.cgpj.2026.100111},
abstract = {General practitioners (GPs) in primary care—particularly in low- and middle-income countries (LMICs)—frequently encounter meaningful clinical problems but lack the methodological training to formalize them into research questions. This paper reports a five-stage, AI-assisted approach that embeds established frameworks—including the JBI Population–Concept–Context framework, scoping review methodology, and evidence-based questioning paradigms—into nine standardized AI prompts, guiding GPs through: practice observation and value assessment; information extraction and evidence-based transformation; literature search and knowledge summarization; research question prototype construction; and methodology selection and feasibility assessment. Built on human-AI collaboration with human primacy, the approach requires no prior methodological training. It was piloted through the Shanghai General Practice Research Network (SGPRN) and is most applicable in low evidence-density primary care settings.}
}