Improving technology systems behind Medicare healthcare innovation

Summary

The challenge

CMMI runs dozens of payment and delivery models aimed at improving healthcare outcomes and reducing costs for Medicare, Medicaid, and Children’s Health Insurance Program beneficiaries. Each model depends on technology systems to collect data from participants, analyze results, and share findings. The stakes are high. Model participation is often voluntary, so the usability of those systems directly affects whether providers engage — and whether CMMI can draw meaningful conclusions from the data.

Over time, many of CMMI’s systems had evolved independently. Different teams built and maintained their own platforms without shared design standards, consistent data practices, or a common approach to understanding user needs. The result was a fragmented technology landscape: inconsistent interfaces that confused users, duplicated infrastructure that added technical complexity, and growing gaps between what teams needed and what their systems delivered.

The deeper problem was structural. Teams lacked the methods and practices to surface real user needs or translate research findings into system improvements. Without embedded user research, design decisions were driven by assumptions rather than evidence. CMMI had no systematic way to learn what providers and model participants actually wanted from its technology.

The solution

Before fixing any one system, we measured all of them. A baseline assessment of CMMI’s IT platforms evaluated maturity across user-centered design, agile delivery, and DevSecOps practices. The assessments gave CMMI a clear picture of where each platform stood. They identified the highest-leverage gaps in usability and technical practices, and established a prioritized foundation for improvement.

With that diagnostic in hand, we embedded directly into platform teams. Research reached past five layers of administrators to the providers actually using the systems. Working alongside the Salesforce Connect team, we designed and ran a user research plan that included interviews with healthcare providers. The work surfaced insights about provider needs that CMMI had never had the structural incentive or method to discover.

Model participation is largely voluntary, so the usability of our systems is important both for retaining users and establishing CMMI’s reputation as an innovator… The Discovery team pierced through five layers of administration/relays; they revealed insights on what users actually want that CMMI had never considered, and had no structural incentive to find out.

IT Advisor, CMMI

We ran similar targeted research for the Enterprise Data Management Program (EDMP), helping teams understand engagement patterns and data needs across model participants. These findings informed redesigned user interfaces for key systems such as the Centralized Data Exchange (CDX), improving usability and enabling more effective data analysis. The improvements supported programs like the Emergency Triage, Treat, and Transport (ET3) model, where faster access to cleaner data helped teams make more timely decisions.

Throughout the engagement, we focused on building capability — not just delivering artifacts. User research practices outlasted any single redesign — built into how teams work, not handed off as a deliverable. We coached teams on user research methods, ran design workshops, and embedded iterative practices (user interviews, usability testing, and prototype-and-test cycles). The goal was a sustainable shift: from technology decisions driven by assumptions to decisions grounded in evidence from real users.

The results

• Assessed platform maturity across CMMI’s IT systems, identifying gaps in usability, technical practices, and user alignment that informed a prioritized modernization roadmap
• Uncovered user insights CMMI had never surfaced by reaching through multiple administrative layers to interview healthcare providers and end users directly
• Redesigned user interfaces for the Centralized Data Exchange and other key systems, improving usability and enabling more effective data analysis across model programs
• Embedded user-centered design practices across multiple platform teams, establishing repeatable methods for user research, usability testing, and iterative design