Codesigning a Nurse-Led, Large Language Model-Empowered Agent to Increase Hepatitis B Screening and Vaccination for Inclusion Health Populations: A Research Protocol

<b>Background/Objectives:</b> We aim to codesign and test a nurse-led, large language model-empowered agent to increase hepatitis B screening and vaccination for inclusion health populations. <b>Methods</b>: This study employs a double diamond model-guided codesign methodology. It includes four phases: (i) Discover: To identify intervention targets, a systematic review was undertaken that synthesized 51 factors influencing hepatitis B screening and vaccination among inclusion health populations. A qualitative study will later be conducted to further elucidate specific cultural barriers in the Chinese context. (ii) Define: To delineate effective intervention designs, two systematic reviews were performed, informing the integration of nurse-led intervention components (e.g., counseling, case management, and care coordination) and adaptation of a large language model to address identified intervention targets. (iii) Develop: To codesign an agent, hepatitis B prevention datasets will be constructed with subsequent model adaptations through fine-tuning and retrieval-augmented generation, as well as collaborations among diverse stakeholders. It will facilitate human-agent interactive consultation, intelligent case management, and care coordination, as well as collaborate with a nurse-led multidisciplinary team to manage hepatitis B screening, vaccination, and care linkage. (iv) Deliver: To evaluate and refine the agent, a mixed-methodology will be adopted, encompassing quantitative evaluation of model response, as well as qualitative evaluation of user experience, technical barriers, and potential benefits. <b>Discussion</b>: This intervention is expected to improve hepatitis B screening and vaccination rates among inclusion health populations, thereby enhancing diagnosis, immunity, and care linkage. It will establish a codesign framework for nursing-specific large language models, broadening the impact of nurses on preventive health equity.