Healthcare AI is only as reliable as the data behind it. Clinical data is complex, contextual and highly specialised, so accurate annotation requires people who understand medicine, not simply the annotation tool.
Our healthcare annotation practice is led by full-time, in-house MD physicians working alongside clinical NLP specialists and trained medical scribes, supporting collection, validation, annotation and quality review end to end.
EHR data, discharge notes, clinical narratives, medical terminology, diagnoses, procedures, symptoms and clinical events.
ICD-10, SNOMED CT, RxNorm and LOINC mapping and validation.
Medication names, dosage, frequency and adverse events.
Radiology, pathology, H&E images and other clinical imaging datasets.
De-identification of EHRs and clinical documentation for appropriate AI and data workflows.
Our annotation process includes multiple layers of human quality control. MD physicians adjudicate complex or ambiguous cases, while structured quality checks measure annotation performance against held-out gold sets.
Complex or ambiguous cases are escalated to in-house MD physicians rather than resolved by consensus alone.
Tooling-agnostic teams working within major annotation environments including Amazon SageMaker Ground Truth, as well as client-specific labelling platforms.
ISO/IEC 27001 certified, HIPAA-aligned and BAA-ready, built on more than 25 years of handling sensitive US healthcare information.
Clinical language is contextual. Whether a symptom is negated, historical, hypothetical or attributed to a family member changes the label entirely, and a general annotator will not reliably catch it. That is why our practice is physician-led rather than crowd-sourced.
In-house MD physicians adjudicate complex or ambiguous cases. We do not settle clinical ambiguity by majority vote among non-clinicians.
Against held-out gold sets, with structured quality checks throughout the project rather than a single acceptance test at the end. We report performance against the measures agreed at the start.
Yes. Our teams are tooling-agnostic and work within major annotation environments including Amazon SageMaker Ground Truth, as well as client-specific labelling platforms.
Yes. We de-identify EHRs and clinical documentation as part of the workflow where the engagement requires it.
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