Smart Data Quality (SDQ) AI-assisted data reviews

AI-Assisted Data Reviews uses advanced AI models built for clinical data to automatically flag the data discrepancies manual review would catch — and draft the query text for you.

Detection that never sleeps

AI-Assisted Data Reviews is the foundation of Smart Data Quality. Advanced AI models, deployed directly inside SDQ, continuously scan incoming EDC and non-EDC data to identify discrepancies that would typically surface only through slow, sample-based manual review — then generate predefined query text ready for a data manager to approve.
As a clinical AI pioneer — SDQ was one of the first AI solutions in the pharmaceutical industry — Saama builds advanced AI and machine-learning models trained for clinical data. Applied to data review, they cut query generation from roughly 30 minutes to about 3 minutes per term, so your team spends its time on judgment, not detection.

Why teams choose AI-Assisted Data Reviews

Catch more, earlier

AI surfaces discrepancies that sample-based manual review routinely misses, and it does so continuously as data arrives instead of in periodic batches.

Cut query time from 30 minutes to 3

Model-generated query text removes the manual drafting step — reviewers simply confirm, edit, or reject.

Redeploy your experts

Automating routine detection frees data managers to focus on high-value, judgment-driven review.

Proven at portfolio scale

Built on cloud-based AWS architecture and validated on large, global trials.

3 min

To review and approve an AI-generated query

Down from roughly 27 minutes per query in a traditional manual review

How AI-Assisted Data Reviews works

AI models run inside SDQ against your study data and hand finished, reviewable queries to your team.

Models scan the data

As EDC and non-EDC data arrive, AI models continuously evaluate it for discrepancies and inconsistencies — no rule needs to anticipate every case.

SDQ drafts the query

For each finding, SDQ generates predefined query text describing the issue in clear, consistent language.

Data managers review and approve

Reviewers confirm, edit, or reject each AI-generated query; approved queries flow into the standard query and discrepancy-management workflow.

And it works with your rules. AI-driven detection runs alongside coded data-quality checks from the Integrated Rule Builder, so model-based and rule-based coverage operate as a single layer.