Design Smarter. Enroll Faster. Succeed More.
Optimize your next study with real-world feasibility, site selection, and protocol refinement from ConcertAI's Clinical Trial Optimization.
Optimize the Odds. Streamline the Science.
ConcertAI's Clinical Trial Optimization for Hematology (CTO-H) is a software as a service in hematological malignancies for research analytics, clinical trial design, and clinical trial operational optimization. As the industry's most advanced clinical hematology solution, CTO-H is pushing the boundaries of population scale real-world data and generative AI solution for clinical development in hematology, with its unprecedented scale, longitudinality, and biomarker depth.
Key Benefits of ConcertAI's CTO-H
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Accelerate Enrollment
Model the effect of criteria changes and avoid delays due to under-recruitment.
Reduce Site Footprint
Target only the highest-yield sites based on real-time, patient-level RWD.
Improve Diversity & Equity
Design for inclusion from the start by identifying diverse geographies and underserved populations.
Cut Costly amendments
Preempt endpoint failures and eligibility gaps through early feasibility modeling.
Core Capabilities Driving Impact
Protocol Optimization
Refine Criteria for Real-World Fit
- Test inclusion/exclusion criteria against real-world cohorts
- Understand trade-offs across patient segments
- Align assumptions to current standards of care
- Reduce risk of mid-study amendments
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Site Feasibility & Enrollment Forecasting
Model Enrollment with Confidence
- Assess impact of criteria on eligible populations
- Forecast recruitment by site and geography
- Quantify diversity, burden, and endpoint feasibility
- Model survivability and line-of-therapy patterns
Site Selection & Diversity Targeting
Find High-Performing, Diverse Sites
- Evaluate site performance, patient volume, and trial history
- Surface community sites with proven capacity
- Target underserved populations for equity goals
- Integrated investigator and operations data
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AI-Derived Patient Features
Go Beyond Structured Data
- Use clinically validated AI to derive variables like line of therapy
- Enable granular patient characterization at scale
- Add temporal logic across labs, therapies, and biomarkers
- Deliver evidence-backed feasibility in weeks, not months
CTO-H Use Cases
Site selection
is informed by historical performance, capacity, and recruitment rates across sponsor and public datasets
Amendment reduction
is achieved through earlier visibility into endpoint viability, eligibility trade-offs, and SOC alignment
Diversity targeting
allows users to identify high-potential sites with access to underrepresented populations
Trial feasibility
is evaluated through data-driven enrollment forecasting, site readiness, and inclusion impact
Protocol Design
is strengthened by modeling patient availability diversity, and burden across evolving care standards
