Case Study
A northeastern US multi-hospital system with fragmented patient records and inconsistent provider data across 500+ locations threatened care coordination, regulatory compliance, and patient safety.
DataCatalyst’s Accelerate methodology delivered enterprise data governance and master data management through lean expert teams focused on business-driven requirements and fast value delivery.
Achieved 70% reduction in duplicate patient records, unified provider directory, streamlined regulatory compliance, and enabled population health analytics for value-based care programs.
This leading integrated health system had grown rapidly through mergers and acquisitions, creating a complex web of disparate systems that threatened both patient safety and operational effectiveness. With over 17 hospitals, 500+ outpatient centers, and thousands of employed physicians, the organization faced critical data challenges that directly impacted patient care.
The fragmented landscape included multiple EMR platforms, siloed provider credentialing systems, and inconsistent patient identifiers that created dangerous care coordination gaps. Duplicate and incomplete patient records posed genuine safety risks, while inaccurate provider information disrupted referrals and scheduling.
Meanwhile, increasing regulatory pressures under HIPAA, CMS reporting requirements, and value-based care programs demanded reliable data lineage and auditability that the current state simply could not support.
Patient experience suffered as inconsistent information about providers, locations, and services appeared across call centers, portals, and scheduling systems. Perhaps most critically, the data fragmentation hindered population health initiatives, quality reporting, and predictive analytics capabilities essential for modern healthcare delivery.
The health system recognized that addressing these challenges required more than technical fixes – it demanded a comprehensive enterprise data strategy that could bring order to chaos while maintaining focus on patient care delivery.
The organization needed a partner who could navigate the complex healthcare landscape while delivering results quickly. They required expertise in both healthcare-specific data challenges and proven methodologies for enterprise data transformation. The solution had to address immediate safety concerns while building a foundation for future analytics and population health initiatives.
Critical success factors included maintaining real-time accuracy for patient-facing systems, ensuring regulatory compliance throughout the transformation, and establishing sustainable governance processes that could evolve with the organization’s continued growth.
DataCatalyst deployed its proprietary Accelerate methodology through a lean team of seasoned data experts. This business-driven approach ensured that clinical needs, not IT requirements, shaped the solution architecture.
The engagement began with comprehensive discovery and profiling across 15+ systems to understand the true scope of patient and provider data challenges. Rather than attempting to solve everything simultaneously, the team applied MVP principles to deliver value incrementally while managing risk.
Key elements included designing a multi-domain data model that linked patient, provider, and location entities, while establishing data governance councils with clinical, operational, and IT representation. The approach prioritized patient safety through careful master patient index design and real-time validation processes.
DataCatalyst’s unique blend of management consulting and technical expertise proved essential in bridging healthcare operations with technology implementation, ensuring that solutions would actually be adopted by clinical staff.
The implemented solution created a single source of truth for critical healthcare data domains while establishing sustainable governance processes to maintain data quality over time.
Core components included:
The solution incorporated real-time validation against regulatory requirements and industry standards, ensuring that data quality improvements supported rather than hindered clinical workflows. Clinical data standards alignment mapped lab results, diagnoses, and procedures to industry vocabularies, enabling seamless interoperability.
The transformation fundamentally changed how the health system managed and leveraged its most critical data assets. What had been a fragmented collection of disconnected systems became an integrated platform supporting coordinated care delivery.
Clinical teams gained confidence in patient data accuracy, enabling safer care transitions and more effective coordination across the care continuum. Provider information became consistently reliable across all patient-facing channels, eliminating confusion and improving the overall care experience.
The governance operating model established clear accountability for data quality while embedding data stewardship into existing clinical and operational workflows. This ensured that improvements would be sustainable rather than requiring constant technical intervention.
Perhaps most significantly, the organization shifted from reactive data management to proactive governance, anticipating and preventing data quality issues before they could impact patient care or regulatory compliance.
The implementation delivered quantifiable improvements that directly supported the health system’s mission of providing excellent patient care while maintaining operational efficiency.
The transformation enabled population health and quality programs to gain reliable data foundations essential for measuring outcomes and supporting value-based care initiatives. Patient satisfaction improved through consistent provider and location information across all touchpoints, while operational costs decreased through the elimination of manual duplicate record remediation processes.
Revenue leakage from misrouted referrals and inaccurate provider credentialing was substantially reduced, while compliance risks were proactively managed through robust governance and audit-ready data controls.
With a solid data foundation in place, the health system is positioned to leverage emerging healthcare technologies and advance its population health initiatives. The unified patient and provider data enables sophisticated analytics for clinical decision support and predictive modeling.
The established governance framework provides the operational structure needed to maintain data quality as the organization continues to grow and evolve. This foundation supports future initiatives in personalized medicine, value-based care optimization, and patient engagement platforms.