Reduce the risk of patient nonadherence & dropout in your next trial

Through a unique evaluation of patient characteristics and personality, Compl-AI predicts the risk of non-compliance and dropout at screening and during your clinical trial, helping you strengthen patient engagement strategies

Personalize your patient engagement strategy

Nonadherence in clinical trials and high rates of dropout make efficacy and safety more difficult to determine. Encouraging adherence during the trial and offering other strategies to manage adherence based on special patient traits will help personalize your patient's trial journey and improve the effectiveness of your research.

Dropout Risk

Treatment &


Monitor your non-compliance and drop-out risk before the start and over the course of your clinical trial.

Compl-AI scores and personalizes your patient engagement throughout your trial by combining cutting-edge machine learning-based algorithms with individual patient personality traits and clinical site interactions. This patient-specific approach is proven to significantly predict the risk of nonadherence and dropout. Empowering the study team with such insight enables patient-centered strategies to improve engagement, which in turn improves patient safety and reduces the risk of trial failure.


Patients predicted to be at "high risk" for study dropout, due to non-adherence or consent withdrawal, have a higher risk of stopping the study at the beginning of the study. They also have a higher risk of dropping out of the study compared with patients predicted to be at "low risk". Over the course of the study, the risk of dropout and nonadherence trends down for both groups.
In this study, Compl-AI High patients have 4 times more chances to drop-out from the study as compared to Compl-AI Low patients

How Compl-AI Works

Through an intuitive, easy-to-use interface, study sponsors are empowered to help patients better manage medications and procedures, reducing the risk of nonadherence and dropout. Here's how the setup process works.

Step 1

Assess patient psychology
through a validated questionnaire completed by the subject at screening visit. A predicted score of subject behavior is then computed with a pre-defined machine learning-based model.

Step 2

Sponsor, CRO, and Investigator sites receive patient non-adherence and/or drop out risk score before the next study visit. With this score, the team may optimize patient support and participation in the study.

The next frontier in patient engagement

Minimizing the risk of nonadherence and dropouts is key to your clinical trial's success. Reduce both with Compl-AI, a unique tool that helps sponsors optimize patient recruitment and engagement strategies.

The next frontier in clinical research & patient management

We’re proud to be leading the charge into the next era of drug development.
Cognivia helps clinical trials reduce data variability, empower decision-making, and accelerate the launch of new therapies.
Tell us about your clinical trial below and we’ll be in touch.

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