This article presents a comprehensive analysis of integrated data validation strategies in clinical systems, aimed at enhancing their quality and regulatory compliance. The study employs an interdisciplinary approach that combines risk-based quality management, data model standardization, and multi-level assessment procedures, including DQA and SSDQA, with a focus on their reproducibility and scalability. Particular attention is given to a comparative analysis of the operational efficiency of direct data entry and automated transfer from medical information systems, with a detailed evaluation of their impact on data preparation speed, metric reproducibility, reduction of transcription errors, and monitoring workload. Key factors determining validation effectiveness have been identified, including trial portfolio size, maturity of digital infrastructure, personnel readiness, and regional implementation specifics. Quantitative indicators of RBQM adoption and related tools, as well as data fitness-for-use metrics obtained from multicenter projects with varying levels of quality control maturity, are presented. The optimal validation architecture is defined as incorporating unified standards, continuous control during the execution stage, and adaptation of tools to local conditions to minimize risks. The article will be useful for clinical research professionals, data quality management system developers, regulatory experts, healthcare IT architects, and researchers in the field of digital transformation of medical technologies.

integrated data validation, clinical systems, quality management, risk-based monitoring

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