Why your panel size number is probably wrong

Primary care panel size is often treated as a basic operational input: set a number, apply a few adjustments, and move on. For years, that approach worked well enough and a widely accepted benchmark of 1,800 to 2,000 patients per physician became the norm, with some variation by patient population.

Many organizations today still rely on static benchmarks and simple age and gender adjustments to determine panel size. This approach ignores two central issues: patients are not interchangeable; and care delivery and payment models have evolved. Patients with similar demographic profiles may have entirely different clinical demands. One may require only occasional visits; the other may have multiple chronic conditions and require frequent follow-up and coordination across care settings.

When panel size is set without accounting for those differences, the consequences show. Panels with large thresholds can create demand challenges and contribute to staff and clinician burnout, especially as patient complexity rises. Conversely, panels with low thresholds can limit patient access and service line growth, reducing appointment availability for new patients. These pressures are clearest in value-based environments in which care intensity—and not just visit volumes—drives both workload and financial performance.

Balance workload, not headcount

A more effective approach begins with a shift in perspective. Panel size should be risk-adjusted to reflect patient complexity.

Risk adjustment offers a pathway with more precision. Risk Adjustment Factor (RAF) scores, derived from Hierarchical Condition Category (HCC) coding, estimate the expected cost and care needs of a patient population by assigning weights based on clinical complexity, rather than treating patients as single units. In this system, a practice managing a higher-risk population would carry a smaller panel than one caring for a healthier population.

Risk adjustment and RAF-based panel size measurement are already established practices in advanced value-based care organizations. In those settings, panel size is one of the first operational levers addressed because of its downstream impact on access, cost, and outcomes. It also aligns more closely with how payment actually works, as reimbursement is tied to patient complexity. In risk-based and capitated arrangements, payers use RAF and HCC methodologies to adjust payments based on expected cost.

Health systems that use a different method than their primary payers to measure panel size create both internal and external misalignment. The result is a system in which clinicians manage panels based on one definition of complexity, while payment is determined by another. This disconnect distorts incentives and makes it harder to manage total cost of care effectively.

It’s not as easy as it looks

That said, moving to a risk-adjusted, complexity-based model is not as simple as adopting a new formula. It requires infrastructure and discipline.

Clinical documentation is foundational. RAF scores are only as accurate as the clinician documentation and data behind them. If diagnoses are not captured consistently, patient complexity will be incorrect, risking that panel size thresholds will be set too high.

Patient attribution (determining which patients are considered part of a clinician’s active panel) also needs to be clearly defined. Some organizations count any patient seen by a clinician without a time restriction, which inflates panels. Instead, consider defining a maximum time period for attribution, often 18 to 24 months since the last patient visit, to ensure panels reflect active relationships.

Benchmarks have a role but must be applied carefully. For instance, academic benchmarks are likely of limited value to community-based practices, and what works for a small, rural practice may not work well for a large, metropolitan organization. Even appropriate benchmarks should be treated as reference points rather than fixed targets.

Panel size is not a static metric, nor an inconsequential one. It determines access, workload, and performance. Get it wrong and the system feels it at all levels. Many organizations are still using methods that ignore patient complexity, an approach that fails in a value-based and access-forward environments.

This is an active, dynamic management lever that ensures patients are wholly represented in their care. Used correctly, care delivery efficiencies and observable bottom-line impact will result.

Getting started: four key considerations

For organizations that currently define panel size mostly using age and gender adjustments or static benchmarks, shifting to a risk-adjusted, complexity-based approach can feel significant. It does not need to happen all at once, but it does require clarity on where to begin. We recommend health systems assess four key domains when developing a risk adjusted model:

1. Assess current methodology. How is panel size defined today, and what assumptions underpin it? If the answer is age and gender adjustment alone, refinement is needed.
2. Evaluate coding capabilities. Accurate RAF scoring depends on consistent capture of diagnosis and treatment. If gaps exist, strengthening clinical documentation should be a priority.
3. Establish clear attribution rules. Define what qualifies a patient for inclusion in a panel, and apply a consistent lookback period to remove inactive patients.
4. Select benchmarks carefully. Use them to inform decisions, not dictate them, and ensure they align to your organization’s care delivery setting.

The goal is not perfection at the outset. It is progress toward a model that reflects the realities of patient care.

Joyjit Choudhury contributed to this article.