Data Labeling & Annotation for AI & RWE
High-quality data labeling and clinical annotation to support AI, Generative AI, and RWE development using healthcare claims, registry, and EHR/EMR data. Enabling accurate model training, validation, and explainability for observational research and advanced analytics.
Powering AI with Clinical ‘Ground Truth’
The Method
- Clinician-led and expert-reviewed labeling
- Standardized definitions aligned with regulatory expectations
- Inter-annotator agreement workflows
The Application
- Training Supervised Learning Models
- Validation of Generative AI
- Annotation of unstructured EHR data
We deliver clinically meaningful labels that power reliable AI models and defensible evidence.
What We Label
Clinical phenotypes and disease states
Health outcomes and safety events
Treatment exposure, switching, and adherence
Lines of therapy
Comorbidities and risk factors
Procedures, diagnostics, laboratory results, and biomarkers
Temporal events and longitudinal patient journeys
Methodological Rigor
Clinician-led and expert-reviewed labeling processes
Standardized definitions aligned with regulatory and HTA expectations
Controlled vocabularies and common data models
Inter-annotator agreement and quality assurance workflows
Full documentation and audit-ready traceability
Use Cases
Supervised and semi-supervised AI / GenAI model training
Outcome validation and phenotyping for RWE studies
Comparative effectiveness and safety research
Signal detection, drug safety, and pharmacovigilance
Discuss Your Project With Us
Ready to get started? Our team of clinicians and researchers is here to help.
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