Traditional data quality checks have served clinical research well for decades, catching obvious issues like missing fields and chronological errors. But as studies grow more complex, these rule-based systems are hitting their limits. They excel at straightforward violations but miss nuanced patterns; like when a patient’s dose adjustment doesn’t align with their medical history, or when lab values show concerning trends that don’t break any single rule but create an inconsistent picture overall.
This is where the pharmaceutical industry faces significant costs from undetected data quality problems. We’ve relied on proven but limited tools that catch the clear-cut issues while subtler, complex relationships slip through undetected. The question is: what if there was a smarter way to identify those “something doesn’t add up” moments that experienced data managers instinctively recognize?
Query Anomaly Detection: Smarter, Context-Aware AI
Enter Query Anomaly Detection (QAD): rather than just adding more rules to existing systems, it uses Artificial Intelligence (AI) to identify patterns that traditional programming approaches might miss.
The system learns from thousands of clinical trials, understanding clinical data the way an experienced data manager would.
Here’s how it works:
QAD uses large language models (LLMs), including Saama’s own LLMs, which are specifically trained on clinical trial data. These models understand the relationships between patient demographics, adverse events, lab results, and treatment histories in ways that traditional programming approaches haven’t been able to achieve.
When the system reviews data, it’s not just checking boxes. It’s asking questions like:
- “Does this patient’s treatment timeline make sense given their medical history?”
- “Are these lab values consistent with what we’d expect from this treatment protocol?”
- “Do the adverse events align with the documented exposures?”
The beauty lies in the nuanced analysis. QAD can identify when a dose adjustment reason doesn’t align with previous medical events, or when laboratory trends suggest data entry inconsistencies that no single rule would detect.
Saama’s QAD: Practical AI for Clinical Teams
Many companies are exploring AI applications, but Saama has developed a particularly practical approach with our QAD platform. We’ve focused on creating a tool that integrates seamlessly into existing clinical workflows. Key features include:
- Pre-Built Scenarios: Over 48 ready-to-use checks covering adverse events, lab results, and treatment adherence.
- Clinical Validation: Every flagged anomaly is reviewed by physicians and data quality experts, ensuring that AI findings are actionable and trustworthy.
More Focus, Less Noise: How QAD Supports Teams
QAD is designed to augment, not replace, human expertise. Automation identifies complex patterns, while experts validate them. The platform provides detailed explanations for each anomaly, helping teams understand why attention is required.
Benefits include:
- Focused Workflows: Teams spend less time on low-value queries.
- Cross-Domain Insight: Relationships across demographics, exposure, labs, and adverse events are considered holistically.
- Efficiency Gains: Automation reduces manual effort, letting staff focus on strategic tasks.
The Broader Impact
QAD doesn’t just detect errors; it transforms how teams work with data. By handling routine pattern recognition, AI frees data managers to focus on strategic analysis, protocol optimization, and decision-making.
For sponsors, this translates directly to business value: accelerated database locks that compress study timelines, enhanced regulatory submission quality that reduces review cycles and approval delays, and ultimately faster patient access to life-saving treatments. For CROs, it improves operational efficiency and client confidence.
Most importantly, stronger data quality leads to better science. When inconsistencies are caught early and comprehensively, studies can generate more reliable, actionable results.
Looking Forward
Clinical data quality is evolving rapidly, and we’re seeing meaningful advances in how technology can support data integrity efforts. The traditional approach of adding more rules and manual reviews has served us well, but there’s clear potential for improvement.
As a key feature within Saama’s Smart Data Quality (SDQ) platform, QAD makes these insights actionable, turning AI-driven anomaly detection into everyday clinical practice.
Discover how it can elevate your data quality- schedule a demo at [email protected].
