Vectors shaping the contemporary landscape of health technology assessment: fundamental approaches and the potential of digital solutions (literature review)

Introduction. The role of classical methods in assessing the growing number of health technologies remains significant. At the same time, these methods are increasingly implemented by means of specialized, high‑performance hardware‑software platforms.

Purpose. To identify the fundamental approaches to conducting health technology assessment (HTA) that either retain or gain particular importance in the era of digitalization.

This article was prepared in accordance with the SANRA guidelines for narrative reviews. An information search using relevant keywords was conducted in the PubMed/Medline databases and within the Google ecosystem. Priority was given to the most recent and relevant reports from the past 2–3 years.

Cost‑effectiveness and cost‑utility analyses remain widely applied methods of comprehensive assessment. The platforms Trialstreamer and RobotReviewer, designed for analytical extraction of clinical trial data, are reviewed. Core modeling tools are identified: (1) highly‑specific — TreeAge Pro; (2) generic — MS Excel and other spreadsheet applications; (3) statistical — R, Stata, SAS, WinBUGS. Web‑based applications for interactive modeling, such as R Shiny‑based systems and the ICER Interactive Modeler — provide access to a range of health economics models. These tools enable users to modify input variables and visualize the impact of such changes on outcomes in real time. Packages such as R Markdown can facilitate the automation of final report generation and updating.

Additionally, there are provided examples of artificial intelligence (AI) integration into the routine practice of HTA agencies. It is emphasized that, due to the insufficient exploration of AI’s potential, risks, and limitations, there is an urgent need to develop effective mechanisms for oversight and governance of its use.

The limitations of current HTA models are largely associated with the scarcity of refined and context‑appropriate input variables. Human oversight and the involvement of subject‑matter experts remain critically important for ensuring the quality of HTA when implementing AI‑based systems in sensitive areas such as healthcare.

Funding. This article was prepared by the author as part of the research project “Development of methodological approaches to value-based healthcare (VBHC) in the city of Moscow” (USISR No.: 123032100062-6).

Conflict of interest. The author declares the absence of obvious and potential conflicts of interest in connection with the publication of this article.

Received: March 21, 2025 / Accepted: June 24, 2025 / Published: October 31, 202

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