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Case study
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RSM and health system turn data complexity into a scalable, AI-enabled advantage

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Optimizing analytics by integrating Epic Fabric and Azure
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March 06, 2026
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A leading U.S. health system had already made significant investments in cloud modernization and analytics when a major industry shift required them to rethink their approach.

As Epic introduced its Fabric roadmap, changing how electronic health record (EHR) data could be accessed, updated and analyzed, the organization faced a critical inflection point. They needed to integrate Epic’s new capabilities with an existing data platform, preserve control over enterprise data and avoid escalating costs tied to duplicated tools and infrastructure.

By collaborating with RSM US LLP, the health system developed a pragmatic, future-ready data strategy that integrated Epic Fabric with Microsoft Azure and modern business intelligence (BI) tools. The result was a cost-optimized, scalable architecture that delivers near-real-time data access, enables AI and advanced analytics, and reduces annual analytics and platform spend by more than $3 million, without disrupting prior investments.

The challenge: Fragmentation, cost pressures and a changing Epic landscape

Over several years, the health care organization had accumulated a highly complex analytics ecosystem. Multiple EHR systems from past mergers fed into an expanding collection of tools across cloud platforms, data movement technologies and BI environments.

Key challenges included:

Tool sprawl and redundancy: The organization relied on numerous analytics and integration tools, including multiple BI platforms and data replication technologies, many performing overlapping functions.
High and growing costs: Annual spend across analytics platforms and cloud infrastructure reached several million dollars, driven in part by duplicative data movement and licensing.
Delayed access to clinical data: Traditional Epic data pipelines relied on overnight batch processing, meaning many dashboards and reports reflected data that was up to a day old.
Evolving Epic requirements: Epic’s announcement that its future analytics architecture would be delivered through Microsoft Fabric forced the organization to reassess long-term cloud and platform decisions.
Decision fatigue: With infrastructure vendors and software providers all advocating different paths forward, leadership needed objective proof, not opinions, before committing to change.

The organization’s goal was clear: Modernize around Epic Fabric while maintaining independence, controlling costs and maximizing the value of prior investments.

The solution: An independent, Fabric-first data strategy

RSM worked closely with the health system to design a data architecture that treated Epic Fabric as a critical foundation, but not as the sole owner of enterprise data.

The core strategy included:

Critically, this was not a “rip and replace” effort. The approach preserved existing data models where possible, avoided throwaway work and created a flexible foundation that could evolve as Epic and Microsoft capabilities matured.

Implementation roadmap: Prove value before committing

Rather than making sweeping changes up front, RSM helped the organization execute a series of funded proofs of concept (POCs) in collaboration with Microsoft. These POCs tested real-world scenarios using production-scale data, including:

The POCs gave leadership confidence to move forward based on evidence, not theoretical savings or vendor claims.

Outcomes: Tangible savings and better data access

The transformation delivered clear, measurable results:

Beyond cost savings, the organization gained clarity along with a data strategy they could defend, explain and build on with confidence.

Why this matters for health systems using Epic

Epic Fabric represents a major shift in how health care data is accessed and used. Organizations that treat it as a stand-alone solution risk higher costs and loss of control. This engagement demonstrates that it’s possible, and advantageous, to integrate Epic Fabric within a broader, independent data strategy that emphasizes stewardship, flexibility and value.

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