Accelerate Your Data Review Processes - Today
Using the industry’s most advanced AI models, Smart Data Quality (SDQ) gives study teams the power to manage the high volume and variety of today’s clinical trial data.
Experience the power of data review automation
Saama is the industry’s undisputed leader, with over 100+ AI models trained exclusively for life sciences. SDQ automates data review processes, reducing query generation times from 30 minutes to just 3 minutes per query.
Benefits
Automate data
review processes
AI- powered models eliminate manual reviews, reduce errors, and minimize trial delays.
Accelerate time to
database lock
SDQ helps keep data clean as it is collected, speeding the time to database lock.
Reduce time to
issue a query
With SDQ, automatically identify data discrepancies and generate queries.
Scalable across
your portfolio
Built on a cloud-based architecture backed by AWS, SDQ is scalable across your portfolio and is proven on large, global trials.
Traditional
Manual Data
Review Process
Days
Resources
27 min
To manually review and write a query.
2,615
Queries
1,177
Hours
Smart Data Quality
Automated Data
Review Process
Days
Resource
3 min
To review and approve AI generated query.
2,615
Queries
130
Hours
How AI is Applied
AI models are deployed within SDQ to automatically identify data discrepancies.
See how Saama reduces query times from 30 minutes to 3 minutes.
“(SDQ) saved us an entire month. It really has had a significant impact on the first-pass quality of our clinical data and the speed through which we can move things along and make decisions.”
Head of Data Monitoring
Top 3 Global Pharmaceutical Company
Features
Accelerate your trials while maintaining clean, high-quality data.
Advanced AI models automatically identify data discrepancies that would typically only be caught by manual data reviews, and also generate predefined query text.
Review data listings manually and perform advanced data review in a single location. Users can review pre-built listings or create custom listings using generative AI, and can even assign tasks to team members and vendors.
SDQ’s self-service rule builder allows users to code data quality (DQ) checks directly within SDQ and reuse them across studies. DQ checks can be coded once and used across multiple source systems and work in conjunction with AI-driven checks.
Describe desired data quality (DQ) check, and SDQ will write the code and test it, automatically, using generative AI trained on proprietary historical DQ check data.
Track, review, and take action on all queries in one location, regardless of source.
Automatically capture data masking configurations from Data Hub to SDQ, ensuring secure and precise data management.
Track data-cleaning status and missing data at the patient level, and view fully configurable and reusable milestone readiness.
Uncover unknown data patterns and anomalies that may otherwise be missed leveraging Generative AI.
Unified process to create, collaborate on, and approve the data review plan, connecting DQs, IRLs, subcategories, and listings into a structured workflow.
Review and manage both query-based and non-query based protocol deviations collaboratively within SDQ to prevent duplicate PDs.
