AI in Risk Based (Quality) Monitoring

Regulatory bodies are promoting Risk Based (Quality) Monitoring as a best practice in clinical trials to correct data quality issues as early as possible in the trial lifecycle. Since this involves the identification of specific patterns in large datasets, artificial intelligence can support us in doing this efficiently.

  • How can AI find specific patterns that humans might miss
  • Interactions between AI and humans in RB(Q)M
  • Real life examples in respiratory

Reimagining clinical trials: Boosting efficiency with simplified, low-code builds

  • In clinical trials where approval is always tomorrow and go-live is yesterday, use of innovative technology is critical to saving time and boosting efficiency but doesn’t have to
    introduce complexity
  • A simplified study build process relies on reuse such as pre-made form libraries and duplication of previous studies
  • Time reductions in study build can be unlocked with intuitive tools that impact learning curve and process time
  • Low-code / No-code approach to study build can offer complex study builds without reliance on complex or proprietary programming languages
  • Sharing key documentation can further improve efficiency across clinical staff and subjects such as signature collection, patient engagement and digital incentives that lead to improved adherence rates