Overview
Cognivia helps clinical trials manage data variability with predictive algorithms and patient insights. The company offers Cognivia Signal, an early-warning platform for detecting participant dropout risk and placebo response variability, and Placebell, a covariate adjustment approach to reduce placebo bias in clinical trials.
Key people
In the news
- Innovation is not in short supply in clinical research. The challenge is connecting technology, data, processes, and people in a way that creates measurable and sustainable change. Today at the Fortrea Innovation & Technology Summit EU in Basel, Dominique Demolle, CEO of Cognivia, joins leaders from Fortrea and Veeva Systems to explore how our industry can move beyond individual innovations and work collectively toward transforming clinical development at scale. Looking forward to discussing what it will take to turn promising
- Most AI discussions focus on what the technology can do. Yet after every pilot comes a different challenge: Can this actually become part of how clinical development operates? In our recent contribution to a white paper with Fortrea, a recurring theme emerged: Successful adoption appears less dependent on technology alone and more dependent on five organizational conditions: ✓ A clearly defined purpose ✓ Relevant and usable data ✓ Integration into existing workflows ✓ Evidence that builds confidence ✓ Alignment across stakeholders
- Everyone agrees that clinical research needs to transform. The harder questions are: • Why do so many transformation initiatives fail to scale? • How do we balance innovation with trust and governance? • How do we bring people along rather than impose change upon them? • What does responsible adoption actually look like in practice? These are some of the questions that will be explored at the upcoming Fortrea Innovation & Technology Summit in Basel. We're proud that Dominique Demolle, CEO of Cognivia, has been invited to join
- We're pleased to share that our CEO, Dominique Demolle, has been invited to participate in a panel discussion at the Fortrea Innovation & Technology Summit in Basel alongside leaders from Fortrea and Veeva Systems including Clare Campbell-Cooper Hugo Cervantes and Alejandro Martinez Galindo. Clinical research is entering a period of profound transformation. New technologies are creating significant opportunities, but lasting change will depend on much more than technology alone. The future of clinical development will require
- Human behavior influences clinical trial outcomes in more ways than we traditionally measure. At Cognivia, we're exploring how predictive behavioral intelligence can help sponsors better understand, quantify, and anticipate participant-related factors that impact trial success. From accounting for behavioral variability statistically to identifying participants at risk of disengagement, we're excited to discuss how behavioral science and predictive analytics are opening new possibilities for clinical research. Heading to CNS Summit
- What if one of the biggest sources of variability in pain trials isn't treatment-related at all? For years, the industry has tried to understand why outcomes can differ across sites, countries, and patient populations. At IASP 2026, Cognivia in collaboration with Novartis will present scientific poster #190 exploring a factor that often sits in the background of these discussions: patient expectations. The findings raise an important question for trial design and analysis: If expectations influence outcomes, should they still
- What if patient drop-out risk could be identified before it becomes visible in your operational data? Cognivia Signal was built to help teams see risk earlier. Using behavioral intelligence, Cognivia Signal provides: 👉 Early predictive indicators of patient drop-out risk, 👉 Visibility into sites enrolling patient populations with elevated behavioral risk, 👉 Insights into the drivers of disengagement, including health literacy, emotional burden, and site confidence, 👉 An additional intelligence layer that complements RBM,
- In conversations about AI in clinical trials, one topic consistently takes center stage: data quality. But an equally important question may deserve more attention: Are we collecting the information that is most relevant to the decisions we're trying to make? This question emerged repeatedly during the development of our recent thought-leadership paper on AI adoption in clinical development with Fortrea. It's a subtle distinction, yet one that could have significant implications for how our industry evaluates risk, designs
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