Automated Metadata Management
at your fingertips

ApoGI is an integrated, automation platform that leverages AI (Artificial Intelligence) and ML (Machine Learning) and puts focus on standards and metadata management to streamline processes and the generation of artifacts typically associated with the clinical study lifecycle.

Ensuring process/artifact consistency, streamlining and automating repetitive tasks and artifact generation to allow subject matter experts to focus on science and deriving additional value/insights from clinical trial data.

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  • Next generation enterprise platform
  • Ability to integrate and leverage data from multiple sources
  • Ability to anonymize the data for sharing it internally or externally in real time
  • Leveraging new technologies to support artifact generation from protocol to CSR.
  • Automated process workflows to facilitate Governance, Artifact Generation, Review / Approval / Sign-off
  • Availability of Structured Governance Framework to track metadata at multiple hierarchical levels 
  • Impact Analysis/Traceability Tool (across all levels)  

Key Benefits

  • Navigational Panel:
    • Study, Doc, Repository, Strategy Application,
      and tracking of strategies applied
    • Parameter-driven Risk Analysis and Data
      Utility per EMA guidance. Determine by data/
      variable or determine for entire study/project.
  • Strategy Application:
    • Repository and “Pre-De-ID Analysis” tools
      provide methods/strategies to the user to
      leverage with confidence speeding setup and
  • View Panels:
    • See datasets before/after and strategies
      applied immediately to confirm which provide
      best results for specific project. “Sync scroll”,
      summary statistics and histograms available
      to make it easier for reviewers/analysts to
      evaluate the effectiveness of applied de-ID
    • See docs before/after with “Redaction
      Proposal” – like display. De-ID’d values are
      highlighted and easily found for review by
      either the navi panel or via drop-down menu
      directly over the doc. Annotations show how
      the value will appear (in accordance with EMA
      Policy 0070) in the final de-ID’d doc.
    • See results before/after with interactive
      visualizations to identify data risks over/under
      threshold, and alternate display to view risk
      separately by quasi-identifiers/variable
  • Strategy Types:
    • From basic “search-n-replace/redact” to ML
      & regular expressions, to leveraging of de-
      ID’d datasets, to queries and patient-specific/
      narrative strategies