Recruitment Monitoring

Recruitment Monitoring graphs are a powerful visualization tool designed to help you track the progress of patient enrollment in your clinical trials. These graphs provide insights into how many patients have been enrolled over time, helping you manage recruitment goals and timelines more effectively.

To ensure accuracy in these visuals, it's important to understand how patient enrollment is determined and what factors may influence the data presented—particularly at the early stages of a study.

By default, Recruitment Monitoring graphs identify enrolled patients based on the existence of a Randomization event in your Electronic Data Capture (EDC) system. This is considered the gold standard marker of enrollment across most studies, as randomization typically follows screening and consent, confirming the patient's full participation.

In some cases, your EDC system may not contain a designated Randomized event. Without this event, Rivia cannot automatically determine enrollment status, which can result in underreported or delayed enrollment figures in the Recruitment Monitoring graphs.

To accommodate such scenarios, our team can manually configure the graphs to use the patient's Exposure Date as a proxy for enrollment status. This ensures that your graphs still reflect meaningful recruitment trends, even when the default data point is unavailable.

If your study does not include a Randomized event and this alternative configuration has not yet been applied, you may notice discrepancies—typically a lower-than-expected enrolled patient count—in your Recruitment Monitoring display.

This is a temporary and correctable issue. Once we manually switch the configuration to use Exposure Date instead, the graphs will update accordingly to reflect accurate enrollment figures.

If you suspect that your Recruitment Monitoring graph is not accurately reflecting enrolled patient counts, please:

Our goal is to ensure you have the most accurate and useful recruitment data available throughout your study lifecycle.