John Snow Labs Data Curation and Enrichment Service – Structured, Compliant, and AI-Ready Clinical Data

By eliminating hundreds of hours of manual chart review, this solution empowers teams to accelerate clinical research, registry population, cohort building, and predictive modeling. The combination of automated deep learning pipelines and domain-specific rule sets provides both precision and explainability, producing clean datasets that are ready for downstream analytics and AI.

Designed for environments where data is already de-identified or processed through John Snow Labs’ Custom De-identification Service. It integrates seamlessly with Amazon SageMaker, AWS Glue, and Amazon EC2, and can be customized for any data pipeline or EHR system.

Key Capabilities

1. Automated Extraction: Identify and structure clinically relevant information such as conditions, drugs, labs, and procedures from unstructured text.
2. Normalization and Standardization: Map extracted entities to SNOMED, ICD-10, CPT, RxNorm, and LOINC for uniform representation across systems.
3. Data Enrichment for Research: Generate AI- and analytics-ready datasets for predictive modeling, clinical decision support, and population health.
4. Scalable, Secure Deployment: Built to run on AWS with encrypted, compliant workflows that meet healthcare-grade security standards.
5. Expert Implementation: Delivered with Professional Services for customized integration, optimization, and validation within each customer’s environment.

Example Outcomes

1. Accelerate Research: Automatically curate structured datasets from EHRs for faster insights and discovery.
2. Power Predictive Models: Use standardized data to improve model accuracy and reduce bias in AI applications.
3. Enhance Interoperability: Create consistent datasets that can be shared securely across systems and research teams.