Customer Stories
Accelerating Healthcare Analytics with Databricks
Problem
Thousands of providers, researchers, and policymakers rely on timely, high-quality data to inform decisions that impact over 65 million beneficiaries. Historically, they were constrained by the absence of a centralized, scalable analytics solution. The legacy system was slow, costly, and reliant on outdated tools—resulting in long runtimes, manual processes, and limited collaboration. Analysts needed faster, more reliable tools to support real-time decision-making and meet the complex, evolving needs of national healthcare quality programs.
Solution
eSimplicity implemented Databricks as the enterprise analytics platform for CMS’s QDAS program, delivering a secure, production-ready environment in just 90 days. The solution transformed CMS’s analytic capabilities, dramatically reducing runtimes, enhancing usability, and resolving key security vulnerabilities, while supporting over 3,000 users and 2 petabytes of data. It now powers scalable, self-service analytics that drive faster, data-informed decisions across the Medicare quality landscape.
Outcomes
The Databricks implementation revolutionized CMS’s analytics capabilities by cutting runtimes, enhancing user experience, and supporting secure, large-scale data processing. It delivered immediate performance gains, empowered analysts with modern tools, and established a scalable foundation for AI-driven insights and future healthcare data innovation.
MEASURABLE IMPACT
We produce results for our customers
The Quality Data and Analytics Support (QDAS) program at the Centers for Medicare & Medicaid Services (CMS) advances national healthcare quality initiatives by empowering analysts, researchers, and policymakers with modern, scalable tools for data analysis. Serving over 3,000 users and supporting more than 2,400 analytic jobs per day, QDAS helps the Center for Clinical Standards and Quality (CCSQ) unlock actionable insights across nine care settings and over 500 quality measures.
CCSQ lacked a centralized, cloud-scalable solution for healthcare quality data that could support the growing volume, complexity, and urgency of analytics needs. The existing CDR was not optimized for performance or cost, and critical analyses required hours to days to complete. Analysts faced unstable runtimes, manual processes, and limited tooling options. In parallel, legacy tools imposed high overhead and introduced friction into collaborative workflows, limiting agility and responsiveness to policy or operational needs.
Process & Solution
eSimplicity employed a human-centered, fail-fast pilot approach to evaluate Databricks as a modern alternative to legacy tools. The team deployed segmented environments within the existing data ecosystem and conducted real-world testing with federal data scientists, who evaluated Databricks alongside other tools. This process combined quantitative metrics—such as runtime and cost—with qualitative feedback on usability and integration experience.
Within the Databricks pilot, key analyses that previously took days could be completed in ~30 minutes, and users overwhelmingly preferred the Databricks interface. Based on this feedback, CMS adopted Databricks as the go-forward platform for scalable analytics.
Following CMS’s approval, the QDAS team completed the full Databricks MVP in just two weeks and migrated power users to a production-ready environment within 90 days. The new architecture resolved significant security vulnerabilities, maintained CMS ATO compliance, and enabled thousands of analytic jobs daily. The system now supports over 2 PB of data, 192,000 tables, and 3,000+ users with modern, self-service tools for data analysis, publishing, and governance.
Within 90 days, eSimplicity migrated power users to a production-ready environment. The solution immediately delivered value by reducing analytic cycle times, improving user productivity, and resolving previously identified security vulnerabilities. Throughout the implementation, the team maintained Authority to Operate (ATO) and closed outstanding POAMs through close coordination with CMS security teams. Databricks quickly became the standard for analytics development within CCSQ, enabling new workflows, increasing performance, and providing a foundation for future AI adoption.
Outcomes
The QDAS Databricks implementation transformed CMS’s ability to analyze healthcare quality data—delivering faster, more secure, and more cost-effective analytics at scale. It empowered stakeholders with intuitive, modern tooling, drastically improved analytic throughput, and laid the groundwork for a future-ready, AI-capable infrastructure.