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Accelerating Clinical Trials with Best-in-Class Technology

Traditional methods of setting up trials and managing large volumes of clinical data rely on time-consuming, repetitive manual processes. Today, our industry is increasingly adopting technology solutions to automate end-to-end clinical trials, from protocol to submission. As a result, it’s vital for organizations to be armed with the right knowledge and strategy when selecting solutions and vendors, in order to avoid pitfalls and pave the way for successful implementation.

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Hear thought leaders from Formedix and eClinical Solutions discuss metadata management, study automation, and ultimately how to gain faster insights with Clinical Metadata and Data Repositories.

Should you build a proprietary platform? Go for a stack solution from a single vendor? Or adopt an MDR-CDR multi-vendor approach? Gain insight into the pros and cons of the various options, as well as the key considerations to address. Learn how integrated MDR and CDR solutions can be leveraged together to maximize speed, quality and efficiencies. Speakers will draw on real-world use cases, and demonstrate live how best-in-class software can work in harmony to drive business benefits and long-term return-on-investment. Don’t miss out – register now to learn how to successfully implement technology for automating end-to-end trials.

Key Learning Objectives

  • The pros and cons of different technology approaches.
  • Key considerations when appraising technology solutions, and strategies for successful implementation.
  • The likely challenges and how to navigate them.
  • Business benefits of MDR-CDR integrations demonstrated live.

Target Audience

  • VP of Data Management
  • Director of Data Management
  • Senior Executive Director of Data Management
  • Executive Director of Data Management
  • Associate Director of Data Management
  • Senior Head of Data Management
  • Head of Data Management
  • Senior Data Management Manager

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2024 Industry Outlook: Driving Tomorrow’s Breakthroughs with Clinical Data Transformation