Healthcare Analytics Infrastructure
Challenges
- PHI de-identification required before analytics processing
- Genomic datasets are petabytes — expensive to store and process
- Real-time clinical decision support needs sub-second inference
- Multiple data silos — EHR, claims, labs, pharmacy, wearables
- Consent management and data access governance at patient level
- Interoperability with FHIR R4 APIs across all health systems
CloudFormation Solves
- Lake Formation + Glue for HIPAA-compliant de-identification pipelines
- S3 Intelligent-Tiering for petabyte-scale genomic data cost management
- SageMaker real-time endpoints for clinical decision support inference
- Redshift + dbt for unified analytics across all care data sources
- AWS HealthLake for FHIR-native data storage and querying
- IAM Lake Formation row-level consent-based access control
Analytics Architecture
Health Data Lake
HealthLake (FHIR R4 native) + S3 data lake with Lake Formation governance. Glue crawlers catalogue all data assets. Athena for ad-hoc queries across de-identified datasets.
Genomics Processing
AWS Batch or Azure CycleCloud for WGS/WES pipeline execution. FSx for Lustre as high-throughput scratch storage. Results to S3 + HealthOmics for long-term storage.
Clinical Decision Support
SageMaker models trained on de-identified outcomes data. Real-time endpoints behind API Gateway serve EHR-integrated alerts. FHIR CDS Hooks specification compliant.
Population Health Reporting
Redshift + dbt transform claims + clinical + social determinants data into quality measure dashboards. HEDIS, STARS, and value-based care metrics automated.
Business Outcomes
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