Smart Data Quality (SDQ)
Clean Patient Tracker (CPT)

The Clean Patient Tracker gives study teams a real-time, subject-level view of data-cleaning status — so you can resolve issues early and shorten time to database lock.

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Smart Data Quality (SDQ) Clean Patient Tracker (CPT)

One subject-level view of clean status

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 the Clean Patient Tracker

One dashboard

SDV, coding, missing data, and queries in a single view
Real-time, subject-level clean status across every site and milestone — replacing EDC, spreadsheets, and CTMS

One dashboard

SDV, coding, missing data, and queries in a single view

Real-time, subject-level clean status across every site and milestone — replacing EDC, spreadsheets, and CTMS

How the Clean Patient Tracker works

CPT tracks cleaning from configuration all the way to a green, clean status.

Configure cleaning criteria

Define the criteria — SDV, coding, missing-data logic — that determine when a subject is clean across all required study workflows before final database lock.

Ingest and track

EDC and non-EDC data flow in, and CPT tracks cleaning status across sites and milestones, detecting missing visits and forms using anchor-date windows.

Review and resolve

Data managers work each line — Missing Data, On Hold, Queried, Escalated, or Closed — until all pending items are resolved according to predefined workflow rules.

Configure cleaning criteria

Define the criteria — SDV, coding, missing-data logic — that determine when a subject is clean.

Ingest and track

EDC and non-EDC data flow in, and CPT tracks cleaning status across sites and milestones, detecting missing visits and forms using anchor-date windows.

Review and resolve

Data managers work each line — Missing Data, On Hold, Queried, Escalated, or Closed — until all pending items are resolved.

Green means clean.

When all pending items are resolved or closed, the subject’s status turns green, signaling the data is clean and ready for the next milestone.

Green means clean. When all pending items are resolved or closed, the subject’s status turns green, signaling the data is clean and ready for the next milestone.

Features

Dashboard overview

A bird’s-eye view of clean status per subject, pending issues, a six-month cleaning-trend graph, and data-quality metrics like time-to-entry and query turnaround.

Milestone and prediction service

User-defined milestones feed a confidence-score algorithm and a Likelihood of Meeting Milestone indicator.

Missing data and cleaning

A single location for missing visits and forms with multi-factor filters, CSV export, and threaded comments.

Detailed view

Per-criterion pending counts plus deep links into SDQ (Discrepancy Management, IRL, To-Do) and EDC.

Flexible configurations

Cleaning-criteria selection, custom subject status, coding and vendor dataset selection, and milestone setup — with the ability to inherit configurations from other studies.