NHS Data Integration Pipeline
Data EngineeringMulti-source healthcare data integration system with ETL pipeline architecture, star schema modeling, and NHS data standards compliance (620k records, 207MB).
Overview
💡 Challenge
NHS Trusts have patient data scattered across multiple isolated systems (PAS, EHR, LIMS, Appointments) that do not communicate. Clinicians lack complete patient journey visibility, and analysts cannot perform system-wide analysis for service improvement.
⚡ Solution
Designed and built comprehensive ETL pipeline integrating 4 NHS source systems into unified star schema data warehouse. System handles multi-format data (CSV, JSON), validates NHS-specific standards (Modulus 11 check digits, ICD-10 codes), and implements GDPR-compliant architecture with data quality framework.
🎯 Impact
Demonstrates capabilities directly applicable to analyzing Scotland's Unscheduled Care Data Mart (UCD) for patient pathway optimization. Architecture supports 620,320 records with <2hr processing time, 99.5% data quality target, and complete audit trail for healthcare analytics and research.
Technical Details
🛠️ Tech Stack
✨ Key Features
- ✓Multi-source data extraction from 4 NHS clinical systems
- ✓Star schema dimensional warehouse with fact and dimension tables
- ✓Valid NHS number generation using Modulus 11 algorithm
- ✓ICD-10 diagnosis coding and clinical terminology standards
- ✓Data quality validation framework with healthcare-specific rules
- ✓GDPR-compliant design with pseudonymization and audit trails
- ✓Synthetic data generation: 50k patients, 100k encounters, 350k lab results, 120k appointments
- ✓Realistic healthcare data patterns with intentional quality issues
- ✓Five-layer architecture: Source → Staging → Processing → Warehouse → Presentation
- ✓Handles multiple data formats (CSV for structured, JSON for clinical notes)
- ✓Scottish healthcare demographics and geographic data
- ✓Performance targets: <2hr processing, 10M+ records daily capacity
- ✓Complete documentation of data dictionary and quality rules
- ✓ETL pipeline design ready for Apache Airflow orchestration
Key Learnings
- →Healthcare data integration requires deep understanding of clinical workflows and terminology standards
- →Star schema dimensional modeling dramatically simplifies complex patient pathway analytics queries
- →NHS number validation with Modulus 11 algorithm essential for data quality in UK healthcare systems
- →Synthetic data generation must preserve realistic correlations between clinical variables
- →GDPR compliance requires pseudonymization strategy, not just encryption
- →Data quality framework must balance completeness checks with realistic missing data patterns
- →Multi-format data handling (CSV, JSON) required for different NHS system characteristics
- →Five-layer architecture provides clear separation of concerns for maintainability and testing
- →Documentation of data lineage and transformation logic critical for healthcare audit requirements
📊 Data Notes
This project uses synthetic/open data to demonstrate capabilities while maintaining privacy and confidentiality. All methods and approaches are applicable to real-world scenarios.