Presentation (CTI)

OBJECTIVES: To review the research progress on value frameworks and assessment methodologies of Early Health Technology Assessment (Early HTA), systematically summarize the advances in its value frameworks and assessment methodologies, and provide references for the institutional construction and practical application of Early HTA.
METHODS: Relevant studies published from January 1, 2021 to March 31, 2026 were systematically retrieved from PubMed, Web of Science, CNKI, WanFang Data and VIP databases. References were traced for supplementary retrieval, and studies with framework-based contributions were included. Descriptive induction and thematic integration were adopted for data analysis.
RESULTS: A total of 11 studies were included. The value framework of Early HTA has broken through the traditional dimensions of clinical efficacy and cost-effectiveness, forming eight core dimensions: unmet medical needs, clinical benefit, economic value and payment, organizational implementation, stakeholder preferences, uncertainty, equity and accessibility, and diffusion and scaling-up. Significant differences exist in the assessment focuses across technology scenarios: diagnostic and high-risk medical devices concentrate on clinical pathway reengineering and performance thresholds; digital health emphasizes value translation and payment models; drug repurposing focuses on commercial feasibility and regulatory collaboration; cancer genomics highlights equity and large-scale implementation. Currently, the field is confronted with several challenges, including inconsistent terminology definition, insufficient adaptability across technology development stages, opaque methodological reporting, inadequate empirical evidence, and weak integration of ethical and equity dimensions.
CONCLUSIONS: Early HTA has evolved from a single cost-effectiveness orientation to a multi-dimensional comprehensive value assessment system. Its methodological features are manifested as advanced assessment nodes, explicit uncertainty characterization, dynamic model updating, and evidence-generation orientation.