The end of chart chasing: A new operating model for Stars performance

Medicare Advantage quality programs are still built around a pricing model that does not make economic sense: paying for medical record retrieval and chart abstraction as if value scales with volume.

For years, that model supported the annual hybrid season crunch, when health plans relied on manual chart retrieval and retrospective abstraction to capture clinical evidence before HEDIS submission deadlines. While imperfect, it aligned with a world of sample-based reporting and seasonal workflows.

As HEDIS transitions toward digital quality measures (dQMs), Electronic Clinical Data Systems (ECDS), and full-population, the volume of clinical evidence that must be evaluated is increasing dramatically. The economic model for this review process has failed to keep pace, creating a growing disparity that can no longer be overlooked.

The industry is still paying for activity, not outcomes

Every HEDIS cycle follows the same sequence: Gap lists are exported. Charts are requested. Vendors retrieve records. In-house clinical teams and third-party abstraction vendors manually review documentation, measure by measure. Whether managed in-house or outsourced, costs continue to escalate. Crucially, these expenses are tied to activity–charts retrieved, records processed, and hours worked–rather than actual outcomes. Plans are paying for the work of searching for evidence, not for finding it.

In many cases, health plans pay to retrieve and review charts that yield no usable evidence. This is a workflow inefficiency and a fundamental flaw in the pricing model. And the math compounds quickly: picture four members with open gaps and twenty clinical documents pulled across them–only one gap actually closes. Paying per chart means paying for all twenty documents to find the single page that mattered, pushing the effective cost per gap closure into the hundreds of dollars. The industry has effectively built a system in which costs are guaranteed, but value is not. As measurement expands to the entire population, this imbalance will only grow more pronounced.

Chart chasing was never designed to scale

The traditional chart retrieval model was designed around limited, sample-based HEDIS reporting. This approach was built on several assumptions: that only a sample of members required review, that reporting occurred within a defined cycle, and that manual abstraction served as a final safety net for missing data. Within these parameters, the practice of "chart chasing" was manageable because it was contained. However, those boundaries are now dissolving.

While quality measurement is shifting toward continuous, population-level reporting powered by digital data, clinical documentation often remains fragmented and unstructured. This disconnect has led to a reliance on manual abstraction to bridge the gap left by systems that cannot reliably interpret clinical data at scale. The industry didn't adopt this "chart chasing" method because it was ideal, but because no other solution was available. Now, that limitation is beginning to disappear.

The math breaks before the process does

As measurement shifts to full-population models, the number of records requiring evaluation increases exponentially–more members, more encounters, more documentation, more potential evidence.

In traditional chart-based pricing, every increase in volume translates directly into higher costs. There is no offsetting efficiency curve or mechanism that reduces spend as the system scales. Without the right incentives to improve targeting precision before retrieving charts, the system's economics are destroyed by scale rather than improved by it.

To gain visibility into quality, health plans are forced to increase manual effort and vendor spending.

Manual abstraction fails in three directions at once

As that system shifts toward continuous, population-level measurement, the pressure on abstraction increases across every dimension of the operating model–not just cost, but execution and clinical workflow.

The breakdown is no longer theoretical. It is happening in three distinct ways:

Financial failure

Every year, health plans invest millions in chart retrieval, abstraction vendors, and supplemental staffing. However, this substantial spending is often disconnected from any real impact on quality. Instead of paying for better outcomes, they're simply paying for throughput.

Operational failure

Manual workflows are constrained by seasonal demand, overtime, and vendor bandwidth, which leads to predictable bottlenecks:

• Delays in capturing evidence
• Shortened review periods
• A limited window for intervention during the measurement year

By the time the evidence is available, the opportunity to influence the outcome has often passed.

Provider failure

As the volume of record requests grows, so does the administrative burden on provider organizations. This leads to slower response times and increased friction across the healthcare system. The relentless pursuit of charts strains health plans and puts pressure on the entire delivery ecosystem without improving clinical outcomes.

The market is moving beyond seasonal abstraction

Some health plans are already shifting from the traditional "hybrid season" model toward a year-round, full-population approach. The traditional approach was built for a different era of quality measurement. The key takeaway is clear: leading organizations are not taking the same old approach–they're rethinking the technology, people, and process paradigm entirely. The model is evolving, shifting away from rewarding chart volume and toward recognizing what truly matters, such as:

• Confirmed gap closures
• Accurate exclusions
• Measurable improvements in quality performance

The next operating model for quality

The future of quality operations won't be measured by how efficiently organizations retrieve and review medical records. Instead, it will be defined by something fundamentally different: how health plans can identify relevant clinical evidence earlier, lessen their reliance on manual abstraction, and operationalize the vast, largely inaccessible data that already exists.

As HEDIS moves toward year-round, full-population measurement, a fundamental weakness in today's operating model is exposed: manual abstraction cannot scale. This challenge forces us to ask a more fundamental question: How much of it persists simply because we haven't operationalized a scalable alternative?

The organizations that confront this question first will not only improve their quality performance while reducing their operational burden or costs; they will also redefine how quality performance is managed. The "chart-chase economy" is not just becoming less efficient; it is becoming economically incompatible with the future of quality measurement itself.

To learn more about how health plans are rethinking quality operations for a digital, full-population future, visit our Cohere Capture page.