ObjectiveTo investigate the construction strategy of a knowledge base for health technology assessment (HTA) indicators based on a multi-granularity knowledge representation model, in order to meet the users' diverse demands for HTA knowledge services. MethodsFirstly, we constructed a multi-granularity HTA indicator knowledge representation model based on systematically analyzing the content and structure of the HTA indicator system in literature. Secondly, we extracted multi-granularity HTA indicator knowledge from literature and conduct subject indexing in a human-computer collaborative way. Finally, based on the HTA knowledge service requirements, a prototype of the HTA indicator knowledge base-HTA Indicators was designed and developed. ResultsA multi-granularity HTA indicator knowledge representation model was constructed, covering 5 core knowledge units (indicator systems, indicator items, formulas, measurement variables, and subjects), 20 types of attributes, and 12 types of relationships. This model represented the intrinsic characteristics and connections between multi-granularity indicator knowledge units. Knowledge extraction and subject indexing of multi-grain HTA indicators were conducted based on 227 HTA indicator documents, forming instance data. Finally, a prototype of the HTA indicator knowledge base, named HTA Indicators, was developed. HTA Indicators provides services such as multi-granularity HTA indicator knowledge retrieval, navigation, and linking. ConclusionThe construction strategy of the HTA indicator knowledge base based on the multi-granularity knowledge representation model is feasible. The indicator knowledge base can achieve multi-dimensional semantic organization of indicator knowledge, provide multi-level and multi-dimensional indicator knowledge retrieval and discovery services, and meet the users' demand for precise HTA knowledge. In the future, we will explore the use of cutting-edge technologies such as large language models to achieve the automated construction of large-scale HTA knowledge, thereby enhancing the efficiency and intelligence level of knowledge base construction.
With the rapid global development of artificial intelligence (AI) technology, its applications in various industries continue to deepen. Against this backdrop, AI empowering high-quality development in the pharmaceutical and healthcare sector has become a global research hotspot, and AI-enabled health technology assessment (HTA) constitutes a key component of AI applications in the pharmaceutical field. Currently, the World Health Organization (WHO), International Society for Pharmacoeconomics and Outcomes Research (ISPOR), National Institute for Health and Care Excellence (NICE), and Canada’s Drug Agency (CDA-AMC) are all actively exploring the application pathways, technical challenges, developmental limitations, and solutions of AI in HTA. This paper aims to conduct a systematic analysis of AI-related basic concepts and classifications, typical application scenarios of AI in HTA, risk challenges, and position principles, based on the exploratory findings of the aforementioned entities regarding AI applications in HTA and integrating the technical characteristics of AI and HTA themselves. It further seeks to propose the application pathways, directions, and future prospects of AI in China’s HTA field by drawing on international beneficial experiences.