End-to-End Testing of a Healthcare Management System (EHR)
Project Overview
- Client: Large hospital network (50+ locations)
- System: Electronic Health Record (EHR) modernization
- Scope: Migrate 12TB of patient data from legacy systems to cloud
Key Achievements
- 99.998% data accuracy post-migration
- 40% faster batch processing
- Zero HIPAA violations during 12-month post-launch period
Testing Innovations
- Developed hybrid manual/automated validation framework
- Implemented real-time data checksum monitoring
- Created regulatory-compliant audit trail system
Legacy System Landscape
Mainframe System: 25-year-old COBOL/DB2 platform
Data Volume:
- 8 million patient records
- 120 million historical transactions
Pain Points:
- 4-hour daily batch processing window
- Frequent data corruption incidents
Business Objectives
- Reduce medication errors by 30%
- Achieve 99.99% system availability
- Enable real-time analytics
Technical Requirements
| Requirement | Target Metric |
|---|---|
| Data Migration Accuracy | <0.001% error rate |
| API Response Time | <200ms @ 10K TPS |
| Audit Trail Completeness | 100% traceability |
Optimization Statistics
High-Level Architecture
Data Flow Details
Extract Phase:
- VSAM → Parquet conversion
- EBCDIC to UTF-8 transcoding
Transform Phase:
- Clinical code mapping (ICD-9 → ICD-10)
- Temporal data alignment
Load Phase:
- ACID-compliant bulk loading
- Referential integrity validation
Technology Stack
| Component | Technology |
|---|---|
| Database | AWS Aurora PostgreSQL |
| ETL | Apache Spark + Glue |
| API Gateway | Kong |
| Monitoring | Prometheus + Grafana |
Data Integrity Risks
- Problem: Discrepancies in medication dosage records
- Root Cause: Floating-point precision differences
- Solution: Implemented fixed-decimal validation
Performance Bottlenecks
- Issue: 14-second response time for lab results
- Diagnosis: N+1 query problem in FHIR API
- Fix: Added GraphQL-style batching
Compliance Gaps
- Finding: Audit logs missing practitioner IDs
- Resolution: Enhanced log enrichment pipeline
Test Types Matrix
| Test Type | Tools | Coverage Metric |
|---|---|---|
| Unit | JUnit | 85% LOC |
| Integration | TestContainers | 100% APIs |
| Security | OWASP ZAP | All endpoints |
Data Validation Approach
Algorithm for Record Matching:
def validate_record(source, target):
discrepancies = 0
for field in critical_fields:
if field == ‘medication’:
if not validate_dosage(source[field], target[field]):
discrepancies += 1
elif source[field] != target[field]:
discrepancies += 1
return discrepancies / len(critical_fields) < 0.0001
Test Environment Strategy
- Production Clone: AWS replica with sanitized data
- Load Test Cluster: 100-node k8s cluster
- Compliance Sandbox: Air-gapped HIPAA environment
Framework Architecture
classDiagram
class TestOrchestrator{
+executeTestSuite()
+generateReport()
}
class ETLValidator{
+validateSchema()
+compareChecksums()
}
TestOrchestrator –> ETLValidator
CI/CD Pipeline
- Pre-merge: 1,200 unit tests (15 min)
- Nightly: Full regression (4.5 hrs)
- Production: Canary deployment validation
Load Testing Results
| User Load | Response Time | Error Rate |
|---|---|---|
| 5,000 | 187ms | 0% |
| 10,000 | 423ms | 0.2% |
| 15,000 | 1.2s | 1.8% |
Pen Test Findings
| Vulnerability | Severity | Fix |
|---|---|---|
| SQL Injection | Critical | Parameterized queries |
| Broken Auth | High | MFA enforcement |
HIPAA Checklist
- Audit trail integrity
- Encryption at rest
- Role-based access
Phased Rollout
- Pilot: 2 clinics (3 months)
- Regional: 12 locations (6 months)
- Enterprise: Full rollout (3 months)
Incident Management
- Severity 1: Resolved in <4 hours
- Data Corrections: 12 records out of 8M
Quantitative Outcomes
- 37% reduction in medication errors
- $2.8M saved in manual reconciliation
