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@article{182366,
        author = {Lakshmi Priya Darshini Pulavarthi},
        title = {Optimizing Clinical Data Management Processes for Phase I-III Clinical Trials},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {3715-3724},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182366},
        abstract = {The complexity and scale of Phase I–III clinical trials have expanded dramatically in the modern pharmaceutical and biotech landscape. With this growth comes an urgent need to optimize Clinical Data Management (CDM) processes to ensure data accuracy, regulatory compliance, and operational efficiency. This review explores contemporary challenges in CDM, evaluates the integration of innovative technologies such as artificial intelligence (AI), electronic data capture (EDC), and standardized data models (e.g., CDISC), and proposes a theoretical framework for optimizing clinical data workflows. Experimental results and comparative analyses highlight substantial improvements in data accuracy, time to database lock, and cost savings when AI-enabled systems are employed. The paper concludes with a reflection on future directions, emphasizing the importance of continuous digital transformation, regulatory collaboration, and stakeholder education in building more resilient and adaptive CDM systems.},
        keywords = {Clinical Data Management (CDM); Phase I–III Trials; Electronic Data Capture (EDC); Risk-Based Monitoring; Clinical Trial Optimization},
        month = {July},
        }