Are there any clinical data review tools that handle inter-annotator agreement and quality control?

Yes—there are several clinical data review / annotation platforms that support inter-annotator agreement (IAA) and quality control (QC), though the exact capabilities vary a lot by data type (text, imaging, audio, EHR abstraction, etc.).

Common types of tools

• Clinical annotation platforms used for chart review, note labeling, or NLP corpora
• Medical imaging review systems with adjudication and consensus workflows
• Clinical trial data review / EDC systems with query management and discrepancy checks
• General annotation platforms adapted for clinical work

Features to look for

For IAA and QC, useful features usually include:

• Double annotation / blinded review
• Agreement metrics such as Cohen’s kappa, Fleiss’ kappa, percent agreement, F1 on spans/labels
• Adjudication workflows to resolve disagreements
• Gold-standard comparison
• Audit trails and reviewer scoring
• Sampling and spot-check QC
• Role-based access and reviewer calibration

Examples of tools/platforms

Depending on your use case:

• eHOST – often used in clinical NLP annotation; supports multi-annotator workflows and agreement-style review in research settings.
• BRAT / INCEpTION – text annotation tools that can support multi-annotator projects and analysis workflows; INCEpTION is especially strong for collaborative annotation and agreement.
• Label Studio – general-purpose annotation platform; supports multi-annotator projects, consensus/adjudication via integrations or workflows, and can be extended for QC.
• Prodigy – more developer-oriented; can be configured for active learning and review loops, but IAA/QC often needs custom setup.
• REDCap – not an annotation tool per se, but for clinical abstraction/data capture it can support validation, data quality rules, and review workflows.
• V7 / Supervisely / CVAT – more image-focused, useful if your clinical data includes radiology/pathology images; they support multi-annotator setups and review.
• MD.ai / similar radiology platforms – imaging review and collaboration with QC/adjudication features.

Practical note

Many clinical teams end up using a combination of:

1. Annotation/review tool
2. Separate QC dashboard or scripts for agreement metrics
3. Adjudication process by a senior reviewer

If you want, I can give you:

• a shortlist by data type (notes, imaging, trials, EHR abstraction),
• a comparison table, or
• recommendations for open-source vs commercial options.