overview
Challenges
- Fragmented data sources across multiple medical devices and departments
- Lack of standardized data models hindering interoperability
- Difficulty in managing real-time data from diverse sources
- Data silos limiting insights for clinical researchers
- Complexities in ensuring data quality and consistency
Objectives
Create a unified data platform for diverse healthcare data sources
Enable real-time, high-fidelity data ingestion and processing
Build a Health Lakehouse following OHDSI/OMOP principles
Enhance data accessibility for clinical research and patient care
Ensure data quality, security, and scalability
Solution Approach
- Unified Data Ecosystem: Shifted from a device-centric to a datacentric model, treating data as an asset.
- Health Lakehouse on Cloud: Built using Databricks to replace traditional data lakes and warehouses, streamlining data management.
- Real-Time Data Streaming: Employed Kafka for real-time data ingestion and processing.
- Scalable Analytics Framework: Leveraged Apache Spark and Delta Lake for speed, scalability, and reliability.
- Advanced Analytics Integration: Applied NLP and other techniques to standardize and enrich diverse data formats.
- Medallion Architecture: Implemented a multi-layered approach to ensure Atomicity, Consistency, Isolation, and Durability (ACID) properties.
- Data Quality Dashboard: Developed to maintain high-quality data federation and monitor data integrity.
- Compliance & Security: Established unified controls for data access and auditing.
Results & Impact
Enhanced Interoperability
Eliminated departmental and device silos, fostering data collaboration.
Data Liberation
Empowered providers and researchers with comprehensive, accessible data.
Improved Patient Care
Delivered real-time patient and device data, enabling better clinical decisions
Future-Proof Scalability
Multi-tenant architecture supports easy integration of new applications.
Faster Insights
Standardized data models accelerated app onboarding and data analysis.
Reliable Data Streams
Low-latency, high-quality data streams optimized for research and patient care.