Life Sciences Clinical
- The Saama Analytics data engine pulls in data from disparate sources, such as CTMS, EDC, site data systems, and other systems of data records, and normalizes them.
- The dashboard offers you a persona-based, and complete view of all aspects of your clinical studies. You get the ability to zoom in and out in dynamic views and drill down to any level to find out the source of any discrepancy.
- Saama Analytics’ AI-enabled solution makes it possible to run multiple scenarios at the planning stage with various inclusion/exclusion criteria, allowing the most viable study plan to go into trial. Leveraging historical data and Real World Data (RWD), the Machine Learning (ML) algorithm of the solution helps you identify the most suitable patient pools and sets the right inclusion/exclusion criteria.
- The solution takes into account various factors such as patient diagnosis, history of drug therapy, and any clinical procedures, to identify the relevant population groups by cross-referencing it with incidence rates and co-morbidities of the disease and many other target metrics.
Life Science Analytics Cloud
• Patient Registries
Pre & Post Approval
Data Quality &
• Site Performance
• Trial Performance
• Supply Chain
Governance • Curation
Harmonization • Orchestration
Data Models • Clinical KPIs
• Resource Mgmt
• Trial Registry
• Site Registry
• IRT/IVRS RTSM
The AI/ML solution delivers many significant benefits to the clinical study conduct and planning processes.
Global Clinical Portfolio Analysis during Pre- and Post-Approval Phases
Track and monitor the entire portfolio or focus on a single study and manage processes of drug development and commercial evidence generation to reach the market faster and with a more viable drug.
Operation and Financial Risk
Track the KPIs of the study to keep a constant check on real-time vs. planned outcomes to ensure any deviations are attended to and mitigated, and potential operational or financial risks are averted.
Milestone Management on the Basis of
Monitor the status of the study on the basis of business strategy, region, country, or user persona by collating data from all available sources and harmonizing them to offer a single version of the truth.
Site Performance Management
Manage all clinical study data from sites to ensure performance expectations are met, and aspects like patient enrollment and retention are on track.
Data Quality and Compliance
Keep a check on the quality of recorded data, ensuring it has minimal errors so that it can be leveraged to deliver faster and more accurate insights.
With machine learning capabilities you can design the most relevant inclusion/exclusion criteria to recruit eligible patients and minimize dropout rates.
Principal Investigator and Site Selection
Leverage historical data and past performances to select the most suitable PI and sites for a trial.