ARTICLES, APP NOTES, CASE STUDIES, & WHITE PAPERS

• FDA Guidance Has Implications For EDC And EHR eSource Capabilities

  Gain insight from an eSource study observing digitally received data as far back as 2007 to just before initial guidance was released by the U.S. FDA in 2013.

• How RFI/RFP Optimization Changes The Game

  Selecting the right vendors can determine whether a clinical trial advances smoothly or stalls under budget overruns and delays.

• RWD: More Than Just Another Acronym

  Uncover how real-world data can not only be used for any type of clinical trial, but study sponsors can also use RWD to inform all stages of a study.

• Using eConsent Data To Populate Site And Sponsor Data Systems

  A top-10 pharmaceutical company was looking to reduce the amount of administrative burden on their clinical trial sites and themselves. See how we accomplished it, using eConsent.

• What Is Electronic Data Capture And How Does It Simplify Data Management?

  As EDC systems continue to evolve, they streamline workflows, enhance data security, and improve patient outcomes.

• eCOA Solution Design Recommendations: Applied Insights From Trial Sites, CRAs

  Enhance hybrid trials with digitalization through remote data capture using smartphones, tablets, and computers, improving participant experience and data integrity.

• Why eSource Ensures Data Integrity In Medical Device Clinical Trials

  Learn how eSource data capture offers a more efficient and cost-effective approach to maintaining data integrity.

• A Guide To Electronic Data Capture (EDC)

  EDC systems are designed to support a wide range of clinical research while ensuring compliance with global regulatory standards, revolutionizing data management in clinical trials.

• Finding The Optimal IRT/RTSM Design For Your Clinical Trial

  Determine the best IRT/RTSM design for your trial through consideration of the user, data collection, and future-proofing.

• Realizing The Potential Of AI And Machine Learning In Clinical Research

  The life sciences industry has not yet scaled or realized the full potential of AI/ML. Here are four real-world use cases that can encourage researchers to unlock new efficiencies and insights in their studies.