Drive Innovation, Reduce Study Cycle Time and
Achieve Total Visibility from Inception to Commercialization
Bring data from any and all sources for comprehensive research, in-depth review and powerful analysis. Create, curate and link data slices for various functional needs across the clinical trial lifecycle.
Arm data with sophisticated insights using a simplified set of artificial intelligence, natural language processing and machine learning tools. Empower study teams with smart agents providing critical recommendations.
Monitor, plan and enact course correction at program, protocol, country and site levels using integrated workflow and collaboration tools. Continuously mitigate risks and accurately measure progress.
An all-in-one smart data platform supported by integrated AI, analytics and collaboration tools for actionable insights, predictions and early warnings and managing them for improved decision making at every stage of clinical research.
Combine data from EDC, CTM, eCOA, Safety, Regulatory, mHealth and many more for feature rich data clusters
Use pre-built or deploy your own AI, NLP and ML routines for user driven, AI-assisted decision making
Orchestrate processes across geographically dispersed study teams – from researchers to investigators
Focus on what matters most with complete visibility into study conduct, progress and regulatory compliance
Clinical Trials
Biomedical Publications
Research Sites
Clinical Investigators
Rare Disease Trials
Derive clinically meaningful endpoints with direct relation to the outcome of interest with pooled multi-modal datasets
Extract key subject characteristics from unstructured inclusion/exclusion criteria and EHR data and drive patient recruitment
Leverage in-built and user defined NLP and machine learning routines to extract semantically related terms and classify cause-effect relation
Identify sites with high probability of success by combining up to date data from Registries, Investigator DBs, EHR, Claims and other sources
Perform in-depth review of drug targets, protein interaction and toxicity analysis leveraging compounds, genes, biomarkers, pathways and other datasets
Stay up to date on regulatory information with real time dissemination of information from regulatory feeds, guidance and other unstructured documents
NextTrial AI empowers Sponsors, CROs, Research sites and Physicians with data, intelligence and collaboration tools to achieve favorable clinical outcome faster while being regulatory compliant in a business competitive environment
Leverage n-dimensional multi-modal data in highly scalable machine learning environment for target validation, drug-protein interaction and chemical structure prediction
Combine data from EDC, CTM, eCOA, mHealth, RWE, Safety, Regulatory and many more for feature rich data slices
Combine data from EDC, CTM, eCOA, mHealth, RWE, Safety, Regulatory and many more for feature rich data slices
Combine data from EDC, CTM, eCOA, mHealth, RWE, Safety, Regulatory and many more for feature rich data slices
Combine data from EDC, CTM, eCOA, mHealth, RWE, Safety, Regulatory and many more for feature rich data slices
Combine data from EDC, CTM, eCOA, mHealth, RWE, Safety, Regulatory and many more for feature rich data slices
NextTrial AI empowers study teams with modern set of data, intelligence and collaboration tools for complex scientific research, informed decision making, real time monitoring and complete trial oversight
Leverage n-dimensional multi-modal data in highly scalable machine learning environment for target validation, drug-protein interaction and chemical structure prediction.
Unified data repository, smart agents and collaboration tools help study teams receive intelligent alerts, prioritize actions and continuously monitor site performance over the study course.
Select optimal endpoints, build favorable subject profiles and construct effective treatment plans. Design better protocols and reduce amendments by utilizing past trials, publications, RWD and other data.
From protocol deviations to enrollments and dropouts, IP discrepancies, SAEs reporting and arresting data anomalies, use standard or define your own study specific alerts and KRIs for continuous risk assessment.
Assess study feasiblity by running models taking into considerations local regulatory timelines, site-specific risk factors, investigator profiles and demographic characteristics.
Improve patient connectivity with intelligent insights around medication adherence, patient behavior, informed consent complexity and patient-study bottlenecks.
Everything melancholy uncommonly but solicitude inhabiting projection off. Connection stimulated estimating excellence an to impression.
View AllTo bring a new drug to the market takes approximately 10-15 years and costs between 1.5-2 billion US dollars. As such, a failed trial can result in significant costs in […]
Read MorePlatform empowers study teams with data, intelligence and collaboration tools for improved decision making Bridgewater NJ, March 2, 2022 — NextTrial AI has launched a collaborative, artificial intelligence-powered smart data […]
Read More
