Leveraging Intelligent Document Automation for Enhanced Data Integrity and Compliance in the Pharmaceutical Industry
Keywords:
Intelligent Document Automation, Data Integrity (DI), Good Documentation Practices (GDP), Pharmaceutical IndustryAbstract
Introduction: The rigorous regulatory landscape of the pharmaceutical industry demands impeccable Good Documentation Practices (GDP) and absolute Data Integrity (DI) , . However, reliance on manual and legacy documentation processes introduces systemic risks, evidenced by a trend of increasing data-related regulatory citations. This review systematically analyzes the strategic shift towards intelligent document automation as a necessary solution to these compliance and efficiency challenges.
Methods: A systematic literature review was conducted, categorizing automation into three levels: document generation, Intelligent Document Processing (IDP), and structured content authoring , . The impact of these technologies was assessed against the core DI principles (ALCOA+) and their application across manufacturing, quality control, and regulatory affairs , .
Results: Intelligent automation, particularly IDP and structured authoring, fundamentally transforms documentation from error-prone transcription to verifiable data management. Key applications show significant reduction in batch review times and a structural embedding of DI . This shift is critical, as growing systemic vulnerabilities, analogous to the increase in seismic events since in vulnerable coastal regions, reveal the inadequacy of current predictive and manual quality models.
Discussion & Conclusion: Document automation moves quality assurance from error detection to preventative design. Our findings conclude that traditional, manual-centric systems are insufficient to meet evolving regulatory demands and systemic risks, necessitating the adoption of integrated, data-driven automation frameworks to secure compliance and build a resilient quality culture
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Copyright (c) 2025 Dr. Elara V. Chen, Dr. Samuel R. O'Connell
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