Economic and ethical transformation of clinical practice in the context of digitalization: system analysis and predictive risk models - K. A. Ivashkevich (Vestnik of Samara State University of Economics, 2026)
The article analyzes the ethical and economic transformation of clinical practice amidst largescale digitalization and the implementation of artificial intelligence (AI) systems. The relevance of the study stems from the gap between the high investment attractiveness of medical technologies and the institutional unreadiness of the ethical and legal environment for Big Data. The study aims to conceptualize the physician's new professional role and develop a matrix model for minimizing operational risks. The research methodology is based on the systematization of 300 publications for the period 2020-2025, as well as the CBA (costbenefit analysis) method. The author verified the correction multiplier of ethical and legal risks, expressed through the cost of liability insurance, which is integrated into the regression model of panel data with fixed effects (FE) to ensure the objectivity of the calculated parameters. In the article, the author substantiates the transition from classical medical paternalism to the "procedural mediator" model in the "clinician–data–algorithm" triad. The research identifies the "diffusion of responsibility" phenomenon arising from algorithmic opacity, which acts as a direct destabilizer of the financial sustainability of healthcare organizations. It is established that the lack of AI recommendation verification mechanisms necessitates reserving up to 12% of the budget for professional liability risks, while irrelevant traditional informed consent models lead to substantial regulatory fines. The scientific novelty of the study lies in the development of an analytical matrix linking bioethical dilemmas with specific financial indicators (ROI, NPV), proving the dependence of a target ROI of 20-30% on the quality of expert mediation by a doctor, and proposing dynamic consent protocols as a tool for optimizing NPV by 8–10%.