Health System Cuts $3M With Data Integration
A recent case study shows a health system slashing over $3 million in costs by integrating its Epic electronic health record data using Azure. The project fixed major data fragmentation issues, creating a unified data source that is now 'AI-ready' for developing predictive patient insights.
The case study highlights a collaboration between a leading U.S. health system and RSM US LLP to modernize their data strategy. The health system faced challenges with a complex analytics ecosystem, including multiple EHR systems from previous mergers, redundant tools, and high costs. The introduction of Epic's Fabric roadmap, which changed how EHR data is accessed and analyzed, prompted the need for a new approach. The project involved integrating Epic Fabric with Microsoft Azure to create a more cost-effective and scalable architecture. A key part of the solution was migrating their Snowflake data warehouse from Amazon Web Services (AWS) to Azure, aligning their entire analytics stack with the Microsoft ecosystem. This move helped to reduce cross-cloud data movement and consolidate their platforms. The results of this data integration were significant, leading to over $3 million in annual savings. These savings were achieved by reducing business intelligence licensing costs, retiring redundant data movement tools, and platform consolidation. In addition to the financial benefits, the health system now has access to Epic data that is refreshed hourly instead of daily, enabling more timely insights. This unified data source is now described as "AI-ready," positioning the health system for future innovations in advanced analytics and predictive insights. Healthcare providers are increasingly using AI and predictive analytics to forecast disease outbreaks, personalize treatment plans, and optimize resource allocation. This project provides the foundational data structure to develop these types of capabilities. For marketing analytics, this integrated data allows for a more comprehensive understanding of the patient journey, which can be used to personalize outreach and improve patient engagement. For a marketing analytics professional, a key takeaway is the importance of a unified data strategy. In an interview, you might be asked how you would approach analyzing customer data from disparate sources. You could reference a case study like this to explain the value of data integration and how a single source of truth enables more powerful analytics, from predictive modeling to personalized marketing campaigns. Common tools used in such projects that are valuable to know include SQL for querying databases and data visualization tools like Tableau or Power BI to create insightful reports.
