Clinical Data Quality Intelligence
Find clinical data errors before they become expensive problems.
Clinical study data often moves through multiple systems, reports, manual entry steps, and review processes.
TrialVerity is being designed to help clinical teams detect discrepancies earlier, identify the supporting evidence, and focus human review where it matters most.
Designed around evidence, traceability, and human oversight.
Clinical data sources
Prioritized findings
3 potential- Lesion length mismatchHigh
- Visit date out of sequenceMedium
- Unit inconsistencyReview
The Problem
Small data errors can create large downstream workloads.
Clinical information can pass through multiple manual entry, reconciliation, review, and reporting steps.
An inconsistency introduced early may not be discovered until much later, creating additional investigation, correction, and re-review.
A discrepancy’s path
- Source data
- Manual entry
- Study records
- Reports
- Manual review
- Error discovered
- Investigation
- Correction
- Re-review
When a problem surfaces late, the work to resolve it multiplies.
Manual Transcription
Data copied or re-entered across records and systems.
Cross-Document Inconsistency
Related clinical facts may differ between source documents, reports, and structured data.
Repeated Reconciliation
Experts spend time manually comparing multiple sources.
Late Error Discovery
Problems discovered downstream can create more investigation and rework.
These are common potential workflow challenges. Not every organization experiences all of them.
The Approach
An intelligent quality layer — not another clinical system.
TrialVerity is being designed to work around existing clinical workflows rather than forcing organizations to replace the systems they already use.
Existing clinical environment
TrialVerity quality layer
Result
Higher-confidence data
Apply known rules, ranges, dates, calculations, field consistency, and study-specific logic.
Compare related information across records and source documents.
Surface contextual inconsistencies and unusual patterns that traditional rules may miss.
Show why something was flagged and identify relevant supporting evidence.
Automation identifies and prioritizes.
Humans make final decisions.
How It Works
From raw clinical data to evidence-based findings.
- 01
Ingest
Clinical reports, structured exports, spreadsheets, and supporting documents.
- 02
Normalize
Organize information into a consistent internal clinical-data model.
- 03
Validate
Apply deterministic data-quality and study-specific rules.
- 04
Reconcile
Compare related clinical facts across documents and structured records.
- 05
AI-Assisted Review
Identify contextual inconsistencies and anomalies that deterministic checks may not capture.
- 06
Human Decision
Present the finding, evidence, and potential resolution to an authorized reviewer.
Conceptual Product UI
Don't just flag a discrepancy. Explain it.
A conceptual reviewer workbench: every finding is paired with its supporting evidence, a potential resolution, and a clear record of what changed.
Potential Discrepancy
Severity: HighConfidence: High- Subject
- 1045
- Field
- Lesion Length
- Entered Value
- 42 mm
- Procedure Report
- 22 mm
- Supporting Record
- 22 mm
Potential issue
The entered value differs from two supporting source records and may represent a transcription or reconciliation discrepancy.
Supporting evidence
- Procedure Report. Page 14 — Lesion length: 22 mm
- Imaging Record. Lesion measurement: 22 mm
Potential resolution
Review the entered value against the supporting source documentation.
Value lineage
Audit history
- Original preserved
- Finding generated
- Evidence linked
- Reviewer decision pending
This is a conceptual UI. It does not imply that the product currently has these production capabilities.
Rules + AI
Use rules where rules work. Use AI where reasoning helps.
Deterministic Validation
Potential uses
- Required fields
- Missing data
- Invalid ranges
- Date sequencing
- Unit validation
- Calculations
- Duplicate detection
- Protocol-defined rules
- Cross-field consistency
AI-Assisted Analysis
Potential uses
- Narrative inconsistencies
- Cross-document interpretation
- Contextual anomalies
- Likely transcription errors
- Unusual patterns
- Evidence explanation
- Prioritization of complex findings
The objective is not to use AI everywhere. The objective is to use the most reliable method for each type of quality problem.
Human Oversight
AI assists. Humans decide.
Preserve Originals
Source information should never be silently overwritten.
Evidence-Based Findings
Potential corrections should be accompanied by supporting evidence and an explanation.
Human Review
Authorized reviewers remain responsible for final decisions.
Traceability
Original, proposed, and approved values — along with reviewer decisions and history — should remain traceable.
- Original
- Finding
- Evidence
- Proposed resolution
- Human decision
- History
Who It's For
Built for teams responsible for clinical data confidence.
Clinical Data Management
Potential value
Reduce repetitive reconciliation work and focus review on meaningful exceptions.
Clinical Operations
Potential value
Identify questionable records and recurring data-quality patterns earlier.
Quality
Potential value
Create clearer evidence and traceability around findings and resolutions.
Regulatory
Potential value
Improve confidence in the underlying clinical information supporting downstream regulatory workflows.
Long-Term Vision
From error detection to error prevention.
- 01
Detect
Identify likely errors and inconsistencies for human review.
- 02
Explain
Show why the information appears questionable and surface evidence.
- 03
Resolve
Support evidence-backed reviewer decisions.
- 04
Learn
Identify recurring quality patterns across workflows, reports, and studies.
- 05
Prevent
Move validation closer to the point where data is first created.
The long-term opportunity is to move clinical data quality from reactive correction toward proactive prevention.
Design Partner Program
Help shape TrialVerity around real clinical workflows.
We are looking for a small number of medical-device and clinical-research organizations to participate as early design partners.
We are particularly interested in teams that spend meaningful time manually reviewing, reconciling, investigating, or correcting clinical-study information.
The objective is to understand real workflows before defining the production platform.
- 01
Discovery
Understand where errors arise and how they are currently identified and resolved.
- 02
Historical Cases
Review a small number of representative, de-identified historical examples.
- 03
Proof of Value
Evaluate whether deterministic validation and AI-assisted analysis could identify known problems earlier.
- 04
Define V1
Use the findings to determine the appropriate production workflow, controls, architecture, and integration requirements.
Why become a Design Partner?
Work with us to evaluate whether intelligent validation can reduce manual review and reconciliation in your actual workflow. Design partners receive early access to the proof-of-value process and an opportunity to influence how TrialVerity develops around real clinical-data challenges.
Start small. Validate against real problems. Build only what creates measurable value.
Design Partner Intake
Interested in exploring a design partnership?
Tell us a little about your team and the workflow you’d like to improve. We read every submission.
Find the errors earlier.
TrialVerity is being designed to help clinical teams spend less time searching for discrepancies and more time reviewing the findings that matter.
Early-stage platform currently in design-partner discovery.