How Microsoft Fabric transformed healthcare data for faster, smarter decisions

30% REDUCTION IN DATA PROCESSING LATENCY

As the client’s business expanded, their legacy technology stack built on Microsoft On-Prem SQL Server (SSIS) experienced increased demand. Users encountered delays in data processing, receiving reports later than expected and resorting to manual methods and phone calls to obtain time-sensitive information.

Inconsistent data modeling and multiple third-party sources (e.g., Optum, Paycom) caused data quality issues, resulting in unreliable reports and a lack of trust in the data. Users were forced to bypass dashboards and revert to source systems to obtain critical information.

These problems were exacerbated by the business’s rapid expansion and limited team capacity, making it difficult to meet increasing demands and leading to persistent break-fix cycles. Expiring licenses and high maintenance costs further motivated the transition to the new data platform.

The client also sought to increase cost savings and efficiency in analytics by moving reporting capabilities in-house.

Kforce led a comprehensive migration to Microsoft Fabric on Azure, an AI-empowered lakehouse platform that improved data processing and resolved data quality issues while streamlining internal reporting.

Our team enabled scalable, cloud-based data management, eliminating the need for costly on-premises maintenance and allowing the flexibility of a pay-per-use model.

By rationalizing and standardizing data models, our team helped simplify development, reduced errors and made reporting more reliable and easier to maintain. Data validation and cleansing improved data quality and built trust in dashboards and operational reports, so business users could rely on automated insights rather than manual checks.

We also improved regulatory reporting for the Department of Health Services ensuring compliance and more efficient processes. Additionally, the project included the migration of gaps in care, specifically HEDIS measures, to support improved patient outcomes and healthcare delivery.

Using best practices, we consolidated 40% of data models, which simplified development and reduced errors. The new architecture enabled seamless addition of new data sources and improved security and trust in data.

The team achieved a 30% reduction in data processing latency, which allowed internal teams to access up-to-date information quickly, supporting better decision-making and operational efficiency.

The new modernized platform allowed the company to bring regulatory report generation in-house, ensuring compliance with state Medicaid requirements and reducing reliance on third-party vendors.

• 40% OF DATA MODELS CONSOLIDATED, WHICH SIMPLIFIED DEVELOPMENT AND REDUCED ERRORS
• 30% REDUCTION IN DATA PROCESSING LATENCY, SUPPORTING OPERATIONAL EFFICIENCY

The foundational improvements to their data modeling, data processing and reporting capabilities resulted in the client extending the partnership. The roadmap includes continued data model and reporting standardization.

Implementing these changes sets up the organization to take full advantage of AI capabilities. Once the company is prepared to integrate AI, they can expect to see further improvement in member engagement and business growth through AI-driven efficiency initiatives, agentic AI and predictive use cases.