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@article{183526,
        author = {Lakshmi Priya Darshini Pulavarthi},
        title = {Best practices and Challenges for delivering quality data for a lock},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {2354-2361},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183526},
        abstract = {Achieving a clean, accurate, and complete database lock (DBL) is essential for the credibility of clinical trial results and regulatory compliance. As trials grow more decentralized and data sources more diverse, ensuring quality data delivery at lock has become increasingly complex. This review explores best practices and persistent challenges in managing data for lock readiness, with a particular focus on emerging AI-enhanced workflows, risk-based monitoring, and automation. We propose a theoretical lock-readiness framework, present empirical comparisons across multiple data management models, and highlight critical success factors including query management, discrepancy resolution, and governance. Findings indicate that AI-assisted and RBM-driven systems outperform traditional models in time efficiency, data quality, and audit performance. The review concludes with strategic recommendations and future directions for harmonizing people, process, and technology in modern data lock practices.},
        keywords = {Clinical trials, database lock, data quality, EDC systems, AI in healthcare, risk-based monitoring (RBM), data management, query resolution, real-time data validation, regulatory compliance},
        month = {August},
        }