Open Access
Leveraging Intelligent Document Automation for Enhanced Data Integrity and Compliance in the Pharmaceutical Industry
4
Department of Pharmaceutical Engineering, Manchester University of Science and Technology, Manchester, United Kingdom
4
Faculty of Quality Assurance and Regulatory Science, The Dublin Institute of Technology, Dublin, Ireland
Abstract
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
How to Cite
Dr. Elara V. Chen, & Dr. Samuel R. O'Connell. (2025). Leveraging Intelligent Document Automation for Enhanced Data Integrity and Compliance in the Pharmaceutical Industry. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(09), 23–33. Retrieved from https://irjernet.com/index.php/feaiml/article/view/232
References
Srilatha, S. (2025). Integrating AI into enterprise content management systems: A roadmap for intelligent automation. Journal of Information Systems Engineering and Management, 10(45s), 672–688. https://doi.org/10.52783/jisem.v10i45s.8904
Haleem RM, Salem MY, Fatahallah FA, Abdelfattah LE. Quality in the pharmaceutical industry – A literature review. Saudi Pharm J. 2015 Oct 1;23(5):463–9.
Optimization of Inspection Process for Quality Assurance. Int J Sci Res (IJSR). 2016 Apr 5;5(4):577–9.
Lulla, K., Chandra, R., & Ranjan, K. (2025). Factory-grade diagnostic automation for GeForce and data centre GPUs. International Journal of Engineering, Science and Information Technology, 5(3), 537–544. https://doi.org/10.52088/ijesty.v5i3.1089
SlideShare [Internet]. 2015 [cited 2024 Apr 5]. Good Documentation Practices. Available from: https://www.slideshare.net/shettyuc/good-documentation-practices-48147816
Canada H. Good Manufacturing Practices Guidance Document [Internet]. 2015 [cited 2024 Apr 5]. Available from: https://www.canada.ca/en/health-canada/services/drugs-health-products/natural-nonprescription/legislation-guidelines/guidance-documents/good-manufacturing-practices.html
Rangu, S. (2025). Analyzing the impact of AI-powered call center automation on operational efficiency in healthcare. Journal of Information Systems Engineering and Management, 10(45s), 666–689. https://doi.org/10.55278/jisem.2025.10.45s.666
(15) The Power of Intelligent Document Automation | LinkedIn [Internet]. [cited 2024 Apr 6]. Available from: https://www.linkedin.com/pulse/power-intelligent-document-automation-demenselcan/
Chadha, K. S. (2025). Zero-trust data architecture for multi-hospital research: HIPAA-compliant unification of EHRs, wearable streams, and clinical trial analytics. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3477
SignDesk [Internet]. 2024 [cited 2024 Apr 6]. Accelerate Efficiency With Healthcare Document Automation. Available from: https://signdesk.com/in/documents/healthcare-document-automation
Reddy Dhanagari, M. (2025). Aerospike: The key to high-performance real-time data processing. Journal of Information Systems Engineering and Management, 10(45s), 513–531. https://doi.org/10.55278/jisem.2025.10.45s.513
Gunasekaran S. Computer vision technology for food quality assurance. Trends Food Sci Technol. 1996 Aug 1;7(8):245–56.
Patel K, Chotai N. Documentation and Records: Harmonized GMP Requirements. J Young Pharm (JYP). 2011;3(2):138–50.
Industry 4.0, innovation, and pharmaceutical quality control | McKinsey [Internet]. [cited 2024 Apr 5]. Available from: https://www.mckinsey.com/industries/life-sciences/our-insights/digitizationautomation-and-online-testing-the-future-of-pharma-quality-control
Jobs in pharma: How automation is going to affect jobs in pharma, core, auto, and consumer sectors [Internet]. [cited 2024 Apr 5]. Available from: https://economictimes.indiatimes.com/jobs/howautomation-is-going-to-affect-jobs-in-pharma-coreauto-and-consumersector/articleshow/59115678.cms?from=mdr
Durgam, S. (2025). CICD automation for financial data validation and deployment pipelines. Journal of Information Systems Engineering and Management, 10(45s), 645–664. https://doi.org/10.52783/jisem.v10i45s.8900
Faunce TA. Global Clinical Trials: Effective Implementation and Management. JAMA. 2012;307(19):2105–2105.
Top 10 Use Cases of Intelligent Document Processing in the Pharmaceutical Industry [Internet]. [cited 2024 Apr 6]. Available from: https://www.amygb.ai/blog/top-10-use-cases-of-intelligentdocument-processing-in-the-pharmaceutical-industry
Medical Document Automation: Benefits, Tools, and Use Cases - TATEEDA | GLOBAL [Internet]. 2023 [cited 2024 Apr 6]. Available from: https://tateeda.com/blog/medical-document-automation
Transform pharmaceutical authoring processes with the Fonto Integrated Authoring Platform [Internet]. [cited 2024 Apr 6]. Available from: https://www.rws.com/about/news/2024/fonto-launchesiap/
Taral DD, Kolhe SB, Jadhav YK, Kokane SP, Kondawar DMS. REVIEW OF PHARMACEUTICAL DOCUMENTATION SYSTEM. 2022;10(5).
Netfira [Internet]. 2023 [cited 2024 Apr 6]. The unseen power of human-in-the-loop automation for automated document processing. Available from: https://netfira.com/human-in-the-loop-automationfor-automated-document-processing/
( PDF ) Integrating Production Automation Expert Knowledge Across Engineering Domains [Internet]. [cited 2024 Apr 5]. Available from: https://www.researchgate.net/publication/220439470_Integrating_Production_Automation_Expert_Knowledge_Across_Engineering_Domains
Bonthu, C., Kumar, A., & Goel, G. (2025). Impact of AI and machine learning on master data management. Journal of Information Systems Engineering and Management, 10(32s), 46–62. https://doi.org/10.55278/jisem.2025.10.32s.46
Shanley A. Pharma Manufacturing. 2009 [cited 2024 Apr 5]. Pharmaceutical Process Control: Is the Great Divide Growing? Available from: https://www.pharmamanufacturing.com/home/article/11361268/pharma-process-and-automationpharmaceutical-process-control-is-the-great-divide-growing-pharmaceutical-manufacturing
Hariharan, R. (2025). Zero trust security in multi-tenant cloud environments. Journal of Information Systems Engineering and Management, 10(45s). https://doi.org/10.52783/jisem.v10i45s.8899
Kumar K. Data Integrity in Pharmaceutical Industry. J Anal Pharm Res [Internet]. 2016 Aug 3 [cited 2024 Apr 5];2(6). Available from: https://medcraveonline.com/JAPLR/data-integrity-inpharmaceutical-industry.html
Sailaja AK, Sumakanth M. An Overall Review on Role of Automation in The Pharmaceutical Industry.
SignDesk [Internet]. 2024 [cited 2024 Apr 6]. Accelerate Efficiency With Healthcare Document Automation. Available from: https://signdesk.com/in/documents/healthcare-document-automation
Bonthu, C., & Goel, G. (2025). Autonomous supplier evaluation and data stewardship with AI: Building transparent and resilient supply chains. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3854
CFR - Code of Federal Regulations Title 21 [Internet]. [cited 2024 Apr 5]. Available from: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrpart=210
Document Generation Software (Trusted by 2000+ Clients) [Internet]. [cited 2024 Apr 6]. Available from: https://www.windwardstudios.com/overview
Nakagawa A. LIMS: Implementation and Management. In 1994 [cited 2024 Aug 29]. Available from: https://www.semanticscholar.org/paper/LIMS%3A-Implementation-and-Management-Nakagawa/1c5718ac0cd104f61fe16d6837c4ca4ffbd3f95f
THE ULTIMATE GUIDE - Document Automation [Internet]. Avvoka. [cited 2024 Apr 6]. Available from: https://academy.avvoka.com/document-automation-the-ultimate-guide/
Vulpen M van. Embracing the future of document authoring in pharma: a structured and data-driven approach [Internet]. 2023 [cited 2024 Apr 6]. Available from: https://www.fontoxml.com/blog/embracing-the-future-of-document-authoring-in-pharma-a-structuredand-data-driven-approach/
P T, Somareddy HK, T. S, Gowrav M. Impact of Automation in Pharmaceutical Industry on Roles and Responsibilities of Quality Assurance: A Review. Int J Pharm Qual Assur. 2013 Jan 25;11:74–82.
Raviteja M, Gupta NV. A review of electronic data management in the pharmaceutical industry. Asian J Pharm Clin Res. 2013 Jun 1;6:38–42.
Kumar K. Good Documentation Practices (GDPs) in the Pharmaceutical Industry. J Anal Pharm Res. 2017 Mar 8;4.
The Rules Governing Medicinal Products in the European Union. In: GMP/ISO Quality Audit Manual for Healthcare Manufacturers and Their Suppliers, (Volume 2 - Regulations, Standards, and Guidelines) [Internet]. 0 ed. CRC Press; 2004 [cited 2024 Apr 5]. p. 257–316. Available from: https://www.taylorfrancis.com/books/9780203026656/chapters/10.3109/9780203026656-14
Gaurav Malik. (2025). Business Continuity & Incident Response. Journal of Information Systems Engineering and Management, 10(45s), 451–473. https://doi.org/10.52783/jisem.v10i45s.8891
Yashika Vipulbhai Shankheshwaria, & Dip Bharatbhai Patel. (2025). Explainable AI in Machine Learning: Building Transparent Models for Business Applications. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(08), 08–15. https://doi.org/10.37547/feaiml/Volume02Issue08-02