Bring Innovative Medicines
to Patients Faster
Data is the basis of every drug development program. But organizations are plagued by “dumb data” – missing, incorrect or poorly used data. While new technology has addressed some of the challenges, the complexity posed by disparate and siloed data means the end user still struggles to conveniently tap into insight-rich data sources.
At NextTrial AI, we are building a collaborative, artificial intelligence-powered smart data platform for informed decision making at every stage of the clinical trial lifecycle – from data-driven translational research to patient centered cohort selection, efficient study conduct, streamlined regulatory submission and beyond.
NextTrial AI is reimagining the power of data combined with user-driven AI to deliver actionable insights that matter most for trial success.
The NextTrial AI platform is a data-rich environment that comes pre-configured with vast amounts of data from the public domain, including trial registries, genes, biomarkers, publications, claims, safety, regulatory and other real world data sources. The platform also provides inbuilt metadata and versioning supported tools to easily ingest data in real-time from in-house sources, such as EDC, CTM, eTMF, eCOA, mHealth, labs and many others.
In so doing, the platform takes the pain out of time-consuming data integration projects that induce delays. The study team then collaboratively leverages the data-rich environment to create meaningful data slices and clusters, embedded with user-specified analytics, AI and machine learning models to deliver fit-for-purpose actionable insights, smart recommendations and time-sensitive notifications at the program, protocol, country and site level.
Drive innovation, improve operational efficiency and gain complete oversight with data-driven patient-centric approach.
Improve site performance, accelerate patient recruitment, minimize dropouts and achieve total compliance.
Foster collaboration, improve patient engagement and build competitive advantage by empowering geographically dispersed study teams.
Minimize protocol deviations, proactively detect and report SAEs and discover disease areas, ongoing trials and population of interests.
Identity targets, predict interactions and validate novel structure with large-scale multi-modal datasets.
Identify new market segments, predict demand and develop targeted strategy for better product positioning and adoption.