6 Insights on the New MA Reality

Senior health system leaders sat down to talk honestly about what Medicare Advantage performance requires today. Here are six things that stood out.

Something is shifting in the way high-performing health systems think about Medicare Advantage. The question is no longer just whether coding and care gaps are being managed, but where and when that work happens, and whether today's care model is still fit for purpose.

In a recent roundtable discussion hosted by Bright Spots in Healthcare, chief medical officers, population health executives, and clinical operations leaders came together to discuss these issues. The primary takeaway: for most organizations, the current operating model isn't keeping up with what MA performance now demands. 

Here are six key insights from that conversation.

1. The clinical encounter is now the primary operating moment

MA performance has shifted from retrospective management to real-time accountability at the point of care. That was the throughline of the discussion.

For years, providers could rely on chart reviews, addenda, and post-visit reconciliation to capture risk and clean up documentation. That window is closing. As CMS tightens expectations around risk adjustment, documentation integrity, and quality reporting, the encounter itself has become the most important moment to get the record right, close care gaps, and capture a patient's true burden of disease.

If that doesn't happen during the visit, the opportunity is often gone for the year. Work that once took place after the visit needs to start happening before and during it. This shift requires rethinking how clinicians are prepared, what information they have in front of them, and how documentation happens in real time rather than hours or days later.

2. High-performing organizations are redesigning where the work happens

The organizations seeing the strongest MA performance have started rethinking who does what, and when.

The leaders described purpose-built care models that create capacity outside the traditional PCP visit: APP-led wellness clinics for comprehensive annual visits, pharmacist-led programs for chronic care and medication management, and population health touchpoints between appointments. Some organizations have gone further, building entirely separate tracks for high-complexity MA patients so the standard primary care schedule doesn’t carry all the weight.

These are deliberate operating model decisions that improve access, increase RAF accuracy, and let physicians focus on complex cases. At scale, MA performance requires team-based capacity, and individual physician performance alone has a ceiling.

3. AI only creates value when it disappears into the workflow

There was strong agreement on this, and it runs counter to how artificial intelligence in healthcare is often sold.

The use cases that actually work are embedded directly in the clinical workflow: pre-visit prep that surfaces relevant history and open care gaps, real-time support during the encounter, and documentation assistance that reduces burden. When these capabilities live inside the EHR without extra steps or logins, clinicians use them. When AI sits outside the workflow or creates noise, it gets ignored, regardless of how good the underlying technology is.

The leaders were precise about what good implementation looks like. AI earns adoption when it enables better decisions in the moment. When it doesn't, clinicians ignore it.

Navina's Clinician Copilot is built around this principle, with native EHR integration, point-of-care clinical insights, and a workflow designed to fit into how physicians already work.

4. The real constraint is operating model maturity, not technology

The ability to improve MA performance varies widely depending on contract structure, funding, and experience with value-based care. Not every organization starts from the same place.

Leaders in mature, risk-bearing models described clear advantages: more flexibility to invest in team-based care, tighter alignment between performance and revenue, and economic structures that support workflow redesign. 

Organizations still predominantly in fee-for-service face the opposite. Every new role or tool must justify its ROI against volume-based incentives, making change harder even when the case for it is clear. Several leaders noted that this requires deliberate contract strategy and honest conversations about where the organization is actually heading.

There’s no universal playbook. What works in a fully capitated model may not translate to a hybrid environment. Organizations need to be honest about what they can fund and sustain before committing to a direction.

5. AI performance requires tighter integration across clinical, population health, and documentation teams

A recurring theme was internal fragmentation. Clinical care, population health, and documentation workflows often run on different timelines, use different data, and optimize for different goals. 

Quality improvement in healthcare is harder when the evidence for a care gap closure exists in the chart but isn't visible to the right person at the right moment. Risk capture suffers when coding reviews happen days after a visit. Population health programs struggle to be proactive when their data is always a step behind.

The organizations making progress are bringing these functions together around the encounter: shared visibility into patient context, tighter connections between clinical decisions and risk capture, and teams that can coordinate in real time. This is more an organizational challenge than a technology one, and it shows up clearly in performance.

6. Some of the biggest performance risks sit outside the clinic

A meaningful share of MA performance risk lives outside the exam room. Post-acute variation, readmissions, social isolation, and lack of caregiver support all drive cost and outcomes, and they don’t fit neatly into clinical workflows.

Even strong health systems described real challenges with inconsistent SNF quality, limited community resources, and fragmented post-discharge pathways that undermine upstream care. A patient can have a well-documented visit, accurate coding, and a closed care gap, and still end up back in the ED two weeks later because the transition home wasn't well supported. That's a performance problem that no amount of point-of-care improvement fully addresses. Improving MA performance at a systems level means extending beyond the clinic into post-acute coordination and social support. 

What this means for health systems today

The old model is running out of runway. Today’s MA environment doesn’t have room for managing performance retrospectively, leaning on individual physicians, or treating coding and quality as back-office work.

The organizations pulling ahead have built operating models where performance happens in real time, at the point of care, with the right teams and tools behind it. They’ve aligned clinical, population health, and documentation functions while grounding their approach in economic reality.

None of this happens overnight. But the conversation made one thing clear: it’s achievable, and the organizations doing it are already seeing results.

If you're thinking through what this shift looks like for your organization, Navina's team works with health systems navigating these challenges. You can also read how peers are approaching the AI question in Achieving the Real ROI of AI.