Healthcare Data Analytics & Population Health Platform
Enterprise-grade healthcare analytics platform providing actionable insights for population health management and clinical quality improvement.
Key Results
Overview
Metropolitan Healthcare Network operates 12 hospitals and 50+ clinics serving 500,000+ patients. They struggled with fragmented data across multiple systems, making it impossible to track patient outcomes, identify high-risk populations, or measure quality metrics effectively. We built a unified analytics platform that aggregates data from all sources and applies AI to deliver actionable insights.
The Challenge
Healthcare network lacked unified view of patient data across 12 facilities, making population health management and quality improvement initiatives ineffective
Problem Statement
Patient data was siloed across different EHR systems, billing platforms, labs, and specialty clinics. Clinical leaders had no way to identify patients at risk for readmission, track chronic disease management effectiveness, or understand population-level health trends. Quality improvement initiatives relied on manual chart reviews that took weeks to complete. The lack of real-time visibility made proactive care management impossible.
The Solution
Built comprehensive data analytics platform integrating EHR, claims, lab, and patient-generated data with AI-powered insights for population health management
Our Approach
Built data integration layer supporting HL7 FHIR, HL7 v2, and custom APIs
Implemented ETL pipelines processing 2M+ patient records daily
Developed AI models for risk stratification and readmission prediction
Created interactive dashboards for clinical and operational insights
Built patient cohort identification and care gap analysis tools
Implemented real-time alerting for high-risk patient identification
Ensured full HIPAA compliance and data governance
Integrated social determinants of health data
Implementation Timeline
Phase 1: Data Integration
4 monthsConnected 15 different data sources including Epic, Cerner, laboratory systems, claims data, and patient portals. Built secure data lake with real-time and batch integration pipelines.
Phase 2: Analytics Foundation
3 monthsDeveloped data warehouse with star schema optimized for healthcare analytics. Created master patient index for unified patient view across all facilities.
Phase 3: AI & Predictive Models
4 monthsTrained machine learning models for readmission risk, chronic disease progression, and care gap identification. Validated models with clinical teams.
Phase 4: User Applications
3 monthsBuilt role-based dashboards for clinicians, care coordinators, and executives. Implemented real-time alerting and automated reporting systems.
Phase 5: Deployment & Optimization
2 monthsRolled out across all facilities with comprehensive training. Continuously optimized models based on outcomes data.
Technologies Used
This platform has revolutionized how we manage population health. For the first time, we can identify high-risk patients in real-time and intervene before problems escalate. The AI predictions have been remarkably accurate.
Dr. Michael Stevens
Chief Medical Information Officer, Metropolitan Healthcare Network