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Automation With Intelligence in Drug Research.

Hanming Tu1, Zhongping Lin1, Kevin Lee2

  • 1Frontage Laboratories, Inc., Exton, PA, USA.

Clinical Therapeutics
|October 5, 2019
PubMed
Summary

Clinical Data Interchange Standards Consortium standards and FDA electronic common technical document standards face adoption challenges. Automation using AI and machine learning offers a path to higher quality data and efficiency in clinical research.

Keywords:
artificial intelligence smart bioanalyticsautomation efficiency matrixmaturity model of intelligent automationmetadata-driven automationstandard-based integration

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Area of Science:

  • Clinical research informatics
  • Regulatory science
  • Data management

Background:

  • Industry widely uses Clinical Data Interchange Standards Consortium (CDISC) standards for clinical trial data and Food and Drug Administration (FDA) electronic common technical document (eCTD) standards for regulatory submissions.
  • Despite widespread adoption, significant challenges persist in achieving seamless integration and deriving end-to-end business intelligence from these standards.
  • Effective management of evolving standards necessitates meaningful and timely metadata crucial for process adaptation.

Purpose of the Study:

  • To discuss the challenges associated with adopting industry standards in clinical research.
  • To explore potential automation strategies, including robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML).
  • To introduce a maturity model for metadata-driven automation in clinical research.

Main Methods:

  • Review of current industry practices and challenges in standards adoption.
  • Conceptual framework for automation leveraging AI, ML, and RPA.
  • Development of a maturity model for assessing and guiding metadata-driven automation.

Main Results:

  • Identified key challenges in managing the complexity and evolution of clinical research data standards.
  • Proposed automation approaches to enhance data quality, integration, and efficiency.
  • Outlined a phased maturity model for implementing metadata-driven automation.

Conclusions:

  • Automation through AI, ML, and RPA presents a transformative opportunity for clinical research.
  • Metadata-driven automation is essential for navigating the complexities of standards adoption and maximizing data value.
  • A strategic approach to automation and metadata management is critical for future clinical research success.