Clinical NLP & Medical Coding
Transforming unstructured clinical text into structured intelligencer
Industry Challenge
An estimated 80% of healthcare data exists as unstructured text — clinical notes, discharge summaries, referral letters, pathology reports, and radiology findings. Clinical NLP models extract structured information from this text to power clinical decision support, population analytics, and automated medical coding. These models must be trained on expert-annotated clinical text — a task that requires genuine clinical understanding, not generic text labeling
How SCILabel Serves This Industry
Data Collection
We source de-identified clinical text datasets from hospital electronic health record systems, clinical research databases, and academic medical centres under data sharing agreements. Data types include discharge summaries, progress notes, operative reports, radiology reports, pathology narratives, and referral letters. All text undergoes HIPAA Safe Harbor de-identification before annotation.
Data Annotation & Labeling
Our Track 1 (Medical NLP) workforce — doctors, nurses, pharmacists, and health informatics specialists — annotates clinical text with named entities (diseases, symptoms, medications, procedures, anatomy), relations (medication–indication, disease–treatment), assertions (present/absent/uncertain/historical), and normalised codes (ICD-10-CM, SNOMED CT, LOINC, RxNorm). We support annotation in BRAT, Label Studio, Prodigy, and custom platforms.
Data & Model Evaluation
NLP evaluators benchmark model precision, recall, and F1 on entity recognition, relation extraction, and ICD-10 coding accuracy against expert-coded gold standards. We test for performance variation across clinical specialty and documentation style.
Annotation Types & Formats
- Named entity recognition: diseases, symptoms, medications, procedures, anatomy
- Relation extraction: medication–dose–route–frequency, symptom–diagnosis
- Assertion classification: present, absent, possible, historical, family history
- ICD-10-CM, SNOMED CT, LOINC, and RxNorm code assignment
- Sentence-level and document-level clinical classification
- De-identification review and PHI validation
Specialist Workforce Tracks
Track 1 (Medical NLP): Medical Doctors, Nurses, Pharmacists, Health Informatics Specialists, Clinical Coders.