How digital modernization and AI can help CMS address America’s chronic disease crisis
Chronic and mental health conditions account for roughly 90% of the United States’ nearly $5 trillion in annual healthcare spending, placing sustained pressure on Medicare, Medicaid, and the broader healthcare system. Although rates of chronic disease have risen among every demographic, a staggering 90% of Americans over age 65 live with at least one chronic condition. When the youngest of the baby boomers cross that age threshold by 2030, the implications for CMS are daunting: rising utilization, increasing expenditures, and growing pressure to improve outcomes while maintaining affordability.
At this scale, improving chronic disease management is not solely a clinical problem. It’s a system challenge that depends on how effectively CMS and other health agencies can use data, payment models, and digital infrastructure to support prevention, monitoring, and coordinated care across populations. Digital modernization and AI are two powerful tools federal health programs can deploy to address these challenges.
How CMS influences chronic disease outcomes
CMS tracks prevalence, spending, and utilization across more than 60 chronic conditions, from cardiovascular disease to mental health disorders. Together, these conditions drive avoidable hospitalizations, high per-beneficiary costs, and significant reductions in quality of life. CMS can use these data to influence how care is delivered, coordinated, and evaluated over time. Two of the agency’s most valuable data-backed tools are:
Tracking the effectiveness of care with clinical quality measures
CMS’ shift from manual and claims-based reporting to electronic clinical quality measures (eCQMs) has improved the accuracy and timeliness of performance data. For chronic disease management—where early detection and consistent monitoring are critical—more reliable measures enable CMS to better understand how outcomes vary across populations and care settings.
With more accurate and timely data, CMS and other health agencies can identify emerging risks, detect gaps in care earlier, and align incentives more precisely with program goals. Over time, this more coordinated, data-driven approach can create stronger feedback loops between measurement, intervention, and outcomes—an essential capability for managing chronic conditions at scale.
Developing and promoting value-based care models
Value-based care models, such as the Kidney Care Choices Model, shift care delivery away from episodic treatment and toward coordinated, long-term management. By aligning incentives around outcomes rather than volume, these models encourage earlier intervention, better coordination across specialties, and greater investment in prevention.
For patients managing complex or multiple conditions, value-based approaches can support more consistent care over time. For CMS and other health agencies, these models can create opportunities to improve outcomes while reducing avoidable costs associated with late-stage complications.
Digital and AI innovation can advance the federal health mission
In our work with federal health clients, we’ve seen how modern data infrastructure and human-directed AI systems can enhance health agencies’ power to support chronic disease management.
As demands on federal health programs increase, modernized digital infrastructure can be a key enabler of more proactive data-driven chronic disease management. One example is the modernization of CMS’ end-stage renal disease (ESRD) data systems. CMS needed a more efficient way to collect and share information for tens of thousands of ESRD patients nationwide. Through a scaled-agile, user-centered approach, we helped CMS develop a modern system that provides more than 200 transplant centers with timely access to waitlist and preparation data. The updated platform is helping the agency improve transparency, reduce administrative burden, and support more informed clinical decision-making while using taxpayer funds responsibly.
Governed AI applications can analyze large datasets to identify patterns, support monitoring and prediction of disease progression, and automate documentation through ambient AI tools. Federal health programs can benefit from these tools to augment decision-making while maintaining security, privacy, and regulatory compliance. We’re currently working with CMS and CDC on AI-enabled use cases such as:
- integrating advanced analytics into public health surveillance platforms like BioSense.
- addressing privacy and security considerations for large datasets containing personal health information.
- testing optimization strategies that reduce infrastructure costs without compromising performance.
Strategic considerations for federal health leaders
To help reduce the burden of chronic disease nationwide, CMS and other health agencies need technology that’s reliable, scalable, and grounded in clinical and regulatory expertise. As agencies explore digital and AI-enabled approaches to meet that need, leaders should focus on:
- designing digital solutions that support interoperability and long-term program sustainability.
- prioritizing human-directed workflows where technology augments clinical judgement—but doesn’t replace it.
- ensuring AI systems are transparent, explainable, and integrated into existing governance frameworks.
By merging technological innovation with responsible governance, CMS and other health agencies can strengthen chronic disease management and improve patient outcomes while developing efficient, sustainable health programs.
