The Challenge
The health system faced multiple challenges:
- Fragmented data - Patient data scattered across 5 different EHR instances
- Reactive care - No systematic identification of high-risk patients
- Resource constraints - Limited data science capability in-house
- Integration complexity - Need to integrate with Epic EHR workflows
- Compliance - HIPAA and patient privacy requirements
Our Approach
Phase 1: Data Foundation (Weeks 1-4)
Built a unified patient data warehouse:
- Integrated 5 Epic instances via HL7 and FHIR APIs
- Standardized clinical terminologies (SNOMED, LOINC)
- Implemented HIPAA-compliant data lake on AWS
- Created data quality monitoring and remediation workflows
Phase 2: Model Development (Weeks 5-10)
Developed and validated readmission risk models:
- Analyzed 3 years of historical data (250K+ admissions)
- Built gradient boosting models with 150+ features
- Validated across demographic subgroups for fairness
- Achieved 89% AUC-ROC on held-out test set
Key Features:
- Prior hospitalizations and ED visits
- Chronic conditions and comorbidity indices
- Social determinants of health (SDOH)
- Medication adherence patterns
- Discharge disposition and support
Phase 3: Clinical Integration (Weeks 11-16)
Integrated predictions into clinical workflows:
- Real-time risk scoring at discharge
- Care coordinator dashboard with patient prioritization
- Epic integration via FHIR for seamless access
- Automated alerts for high-risk patients
- Mobile app for care teams
Phase 4: Deployment & Monitoring (Weeks 17-20)
Launched with comprehensive monitoring:
- Deployed on HIPAA-compliant Kubernetes cluster
- Implemented model monitoring for drift detection
- Built operational dashboards for IT and clinical teams
- Established model retraining pipeline
- SOC 2 Type II audit preparation
Technical Architecture
Epic EHR (5 instances)
↓
HL7/FHIR Integration Layer
↓
Data Lake (AWS S3)
↓
Feature Engineering Pipeline
↓
ML Model (SageMaker)
↓
Prediction API (EKS)
↓
Care Coordinator Dashboard
Epic Integration (SMART on FHIR)
Stack:
- Data Pipeline: Python, Apache Airflow, AWS Glue
- ML Platform: SageMaker, MLflow, XGBoost
- API: FastAPI, PostgreSQL, Redis
- Frontend: React, TypeScript
- Infrastructure: Terraform, Kubernetes, AWS
Results
Clinical Impact
- 18% reduction in all-cause 30-day readmissions
- 24% reduction in heart failure readmissions
- 15% improvement in care transition completion rates
Operational Impact
- 500+ high-risk patients identified monthly
- 200+ care coordinator interventions weekly
- 92% care team adoption rate
- <200ms API latency for real-time scoring
Financial Impact
- $4.2M annual savings from avoided CMS penalties
- $1.8M reduction in readmission-related costs
- ROI of 380% in first year
Key Success Factors
- Clinical engagement from day one - Care coordinators involved in model design
- Seamless integration - Risk scores in existing EHR workflows
- Continuous monitoring - Model performance tracked and retrained monthly
- Focus on actionability - Predictions paired with recommended interventions
- Privacy by design - HIPAA compliance built in, not bolted on
Long-Term Partnership
Following initial success, we expanded the platform:
- Sepsis prediction - Early warning system for sepsis onset
- ED diversion - Identifying patients suitable for observation unit
- Care gap closure - Prioritizing preventive care outreach
- Population health - Risk stratification for value-based contracts