More than half of Saama users say our technology makes it easier to resolve and avoid data queries.*
As data becomes more voluminous and complex, mitigating data quality risk becomes increasingly difficult. Saama makes it easier for Data Managers to keep data clean and up to date, resolve queries faster, coordinate activities from start-up to database lock more efficiently, and make strategic contributions to risk-based monitoring (RBM) initiatives.
- Eliminate data silos and access quality data in real time
- Aggregate data into standardized models for studies, financials, planning, and inventory management
- Eliminate manual work through automatic highlighting of missing records and data discrepancies
- Facilitate the creation of dashboards and reports
- Use historical data patterns to anticipate outcomes in advance
- Use AI to identify and resolve common queries, facilitate medical coding, and overcome data onboarding challenges
82% of Saama users say our technology enhances their data management capabilities.*
Smart Data Quality (SDQ)
Accelerate data cleaning and time to query with a transformative AI engine that predicts clinical data discrepancies with unprecedented accuracy and gets smarter over time. Developed with Pfizer and used to speed the development of the groundbreaking COVID-19 vaccine, SDQ is purpose-built to generate and resolve queries on the fly and present results via easy-to-use dashboards. With humans in the loop, unresolved queries can be addressed quickly, while human feedback trains the model for greater accuracy to improve the speed and efficiency of drug development.
Smart Coding Module (Part of SDQ)
The Smart Coding Module uses natural language processing (NLP) capabilities to auto-code adverse events and medications with accuracy rates of up to 85%. The tool works with SDQ to auto-generate queries for items that can’t be coded properly.
Smart Auto Mapper (SAM)
Automate the ingestion, mapping, and transformations needed to convert raw and disparate source system data into a unified standard, such as SDTM, for analysis and regulatory submissions. This smart application accelerates clinical data analysis and challenges manual and time-consuming methods of creating monolithic SAS programs.
Smart Programming and Analysis Computing Environment (SPACE)
SPACE gives clinical data programmers and biostatisticians the flexibility to use their preferred editing tools for more efficient and effective collaboration in a secure and validated environment. Simplify study setup, process implementation, and job management for both exploratory analysis and regulatory submission.
Work with your clinical operations team to monitor each study across all sites and data systems in real time. Automated alerts make it easy to proactively identify issues that could damage data quality or delay database lock.
With a central repository for safety and subject-level data, data managers and medical review teams can avoid duplicative efforts, collaborate more effectively, and quickly visualize trends and outliers. Graphical patient profiles and dynamic line listings focus attention on changes to data and automated alerts enable a faster response to data quality issues.
Risk Based Quality Management (RBQM)
Saama delivers an end-to-end solution that continuously engages users across the study lifecycle, from design and conduct to evaluation and reporting. With risk assessment plans, anomaly detection, automatic notifications, a collaborative tasking system, and built-in KRIs and QTLs, RBQM sets you up for success in managing and mitigating risk.