• Hanming Tu, Zhongping Lin, and Kevin Lee.
• Frontage Laboratories, Inc., Exton, PA, USA. Electronic address: htu@frontagelab.com.
• Clin Ther. 2019 Nov 1; 41 (11): 2436-2444.

AbstractThe industry has adopted Clinical Data Interchange Standards Consortium standards for clinical trial data and the Food and Drug Administration electronic common technical document standard for documents for many years but still faces many challenges. The solutions based on these standards enable integration among solo systems, but the integration needs to be based on business requirements and provides the end-to-end intelligence for the business. The more standards are adopted, the more meaningful and timely metadata are needed to manage the change of the standards and need to be applied in the process. Automation that uses artificial intelligence and machine learning will be the next game changer in the industry to provide data with higher quality and more efficiency. This article discusses the challenges in managing standards adoption, potential approaches for automation through using robotic processes, artificial intelligence, and the maturity model for metadata-driven automation in clinical research.Copyright © 2019 Elsevier Inc. All rights reserved.

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