10/08/2026 | Press release | Distributed by Public on 10/08/2026 04:29
A plan can close more care gaps, improve its underlying performance, and still watch its Medicare Advantage Star Rating decline. The reason may be hiding in plain sight: cut point shifts.
That distinction can fundamentally change how health plans should think about Stars strategy. ">In Medicare Advantage, improving performance is not always enough. Plans need to improve performance relative to where the cut points are moving.
The traditional approach to Stars improvement is relatively straightforward. It follows a familiar five-step loop:
The challenge is that the Stars environment does not operate in isolation.
| Year 1 | Year 2 | Year 3 | |
| Plan performance | 78% | 81% | +3 pts |
| 4-Star threshold | 80% | 83% | +3 pts |
| Gap to 4 Stars | 2 pts short | 2 pts short | Unchanged |
| Result | Below 4 Stars | Still below 4 Stars | No Stars movement |
But from a Stars perspective, it didn't move.
Medicare Advantage organizations often build Stars strategies around historical performance.
| A static strategy | A dynamic strategy | |
| The target | "We need to reach 82%." | "We need to understand where the measure is trending, where the relevant cut point is moving, how much headroom we need, and which interventions can create the greatest incremental improvement." |
| Manages toward | A historical benchmark | Stars resilience |
That difference is critical. The first approach manages toward a historical benchmark. The second manages toward Stars resilience.
There are several reasons cut point shifts can become particularly challenging.
Consider two scenarios.
| Scenario A: Volume-driven | Scenario B: Stars-driven | |
| Where effort goes | The largest pool of open gaps | Members, providers, measures, and interventions most likely to cross the threshold |
| Additional gaps closed | 1,000 | 1,000 |
| Designed around | Closure volume | The incremental performance required to change the Stars outcome |
Both strategies close 1,000 gaps. But the second strategy is explicitly designed around the incremental performance required to change the Stars outcome.
One of the most important implications of cut point movement is that the value of a gap can change depending on where a plan sits relative to the threshold.
The organization doesn't necessarily need to close every available gap. It needs to identify the highest-probability opportunities to generate the incremental three percentage points.
This distinction becomes increasingly important as plans approach the upper end of performance.
The next evolution in Stars management is moving from gap identification to gap intelligence.
The intervention layer could include:
Another blind spot is assuming that every care gap should be addressed through the same intervention.
| What the gap looks like | What may work best |
| A member more responsive to a pharmacy intervention than a provider outreach campaign | Pharmacy intervention |
| A member more likely to complete a screening through an in-home test | In-home testing |
| A provider who needs targeted outreach supported by actionable patient-level information | Targeted provider outreach |
| An apparent gap that doesn't represent missing care, but missing documentation or data | Chart retrieval, review, or supplemental data |
This means the most effective gap-closure strategy is increasingly multipoint.
The model becomes:
This creates a more intelligent feedback loop.
There is another challenge hiding underneath cut point management: fragmented data.
When these sources are not unified, the plan may not have a complete view of the opportunity.
| What happens when data is fragmented | The result |
| A member appears to have an open gap in one system while evidence of completion exists somewhere else | Outreach to a member who has already completed care |
| A provider has a high concentration of opportunities but remains invisible because the data is not connected | The highest-leverage provider is never prioritized |
| A chart contains evidence that changes the outcome, but the organization does not retrieve it in time | A closable gap stays open |
The result is wasted effort-and, potentially, missed Stars performance.
To manage in an environment where cut points can move, health plans need to evolve from retrospective reporting to continuous Stars intelligence. A more effective operating model has three stages.
The goal of Predict is to identify measures at risk before the final performance period. Prioritize allows plans to focus limited resources where they can create the greatest measurable impact.
| Stage | Core question | Output |
| 01 Predict | Where is performance heading? | Measures at risk identified before the final performance period |
| 02 Prioritize | Where will effort create the most impact? | Limited resources focused on the greatest measurable impact |
| 03 Act | What should we do, and for whom? | The right intervention recommended for each opportunity |
Act: turning intelligence into action
| Member | Opportunity | Recommended intervention |
| Member A | Medication adherence opportunity | Pharmacy intervention |
| Member B | Screening gap | At-home testing |
| Member C | Provider documentation opportunity | Targeted provider outreach |
| Member D | Suspected risk adjustment opportunity | Chart retrieval and review |
There is an important strategic distinction here.
That changes how health plans allocate resources. It encourages organizations to prioritize sustainable improvements rather than last-minute gap-closing campaigns.
As organizations prepare for future Stars cycles, leadership teams should ask five questions:
The Medicare Advantage Stars environment is becoming increasingly complex. Health plans are managing more measures, more data sources, more intervention channels, and greater operational pressure-all while trying to improve outcomes for members.
That requires a connected intelligence layer across the Stars ecosystem.
The next generation of Stars performance will not be defined simply by how many gaps a health plan can identify or close. It will be defined by how intelligently the organization can translate data into measurable performance improvement.
Because when the target moves, simply running faster isn't enough.