Improving tuberculosis-related knowledge in tuberculosis patients: Protocol for the development and validation of an evidence-based Q&A robot powered by large language models.

Abstract

Background: Inadequate health knowledge of tuberculosis patients is one of the causes of poor adherence among tuberculosis patients in China's tuberculosis control. In this study, we will develop and validate the effectiveness of a large language model (LLM) to improve the health knowledge of tuberculosis patients.

Methods: We will design a LLM application tailored to tuberculosis scenarios and evaluate its effectiveness in tuberculosis patient health education through a single-center, factorial-design randomized controlled trial. The study will feature a factorial design with two factors: LLMs-based health education model and a peer-intervention health education model, each with two levels (yes/no). A total of 148 tuberculosis (TB) patients in the intensive treatment phase will be randomly allocated to four groups through simple randomization. The primary outcome will be the patients' level of personal health knowledge about tuberculosis, measured through questionnaires administered at discharge and three months later.

Conclusion: We are the first study in China to apply LLMs to tuberculosis health education. Tailored specifically for TB, our model uses certified guidelines and expert consensus to minimize inaccuracies. Large language models provide access to personalized, private health information, and reducing stigma. Instead of creating a new platform, we use the popular WeChat platform to deliver education via videos, text, and images, enhancing accessibility and engagement. This innovative approach aims to improve patient adherence and contribute to better TB management and disease control outcomes.