The Challenge
This multi-state healthcare system operated 50+ hospitals, urgent care centers, and outpatient facilities across 8 states. Patient outcome data was siloed across three different EHR systems (Epic, Cerner, and a legacy MEDITECH installation), making enterprise-wide reporting nearly impossible. Clinicians waited days for quality metric reports. The C-suite had no real-time visibility into bed utilization, readmission rates, or financial performance across the network. Compliance teams spent 200+ hours per quarter manually assembling CMS quality reports. The organization faced potential penalties for late and inaccurate quality measure submissions.
Our Solution
Designed a HIPAA-compliant data lakehouse in Microsoft Fabric that unified data from Epic Caboodle, Cerner HealtheAnalytics, and MEDITECH DR repositories into a single analytical model with Delta Lake format.
Built 45+ Power BI dashboards covering patient outcomes (readmission, mortality, LOS), operational metrics (OR utilization, bed management, ED throughput), financial performance (revenue cycle, payer mix, cost per case), and quality measures (CMS Stars, HEDIS, MIPS).
Implemented row-level security tied to Azure AD groups ensuring providers only see their facility and patient panel data. Sensitivity labels applied to all PHI-containing datasets with DLP policies preventing unauthorized export.
Created automated data pipelines refreshing clinical data every 15 minutes for critical dashboards (ED, ICU) and hourly for operational reporting, with full audit logging for HIPAA compliance evidence.
Deployed mobile-optimized dashboards for rounding physicians and nurse managers, enabling bedside access to patient outcome trends and unit-level KPIs.
Results
Quality metric reports that took days now generate in minutes with automated data pipelines.
Automated validation rules and reconciliation checks ensure data integrity across all three EHR sources.
Critical clinical dashboards refresh every 15 minutes, giving real-time visibility to care teams.
CMS quality reporting automation eliminated manual data assembly and cross-referencing.
Early identification of at-risk patients through predictive analytics reduced 30-day readmissions.
All hospitals and outpatient facilities unified into a single enterprise analytics platform.
Implementation Methodology
Phase 1 (Weeks 1-4): Discovery and security architecture. HIPAA security risk assessment, EHR data source inventory, and data governance framework design.
Phase 2 (Weeks 5-8): Data platform build. Fabric lakehouse deployment, ETL pipeline development, data quality validation, and RLS implementation.
Phase 3 (Weeks 9-12): Dashboard development. Built 45+ dashboards across clinical, operational, financial, and quality domains with user acceptance testing.
Phase 4 (Weeks 13-16): Rollout and training. Phased deployment across facilities, champion training program, and go-live support.
Technology Stack
“For the first time in our history, every facility leader sees the same numbers at the same time. That single improvement transformed how we make decisions.”