How to impact model an RVU shift and compensation impacts in a medical practice

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2/12/2026

When CMS tweaks RVUs, it creates questions: How will this affect productivity? Will compensation drop? Do contracts need renegotiation?

If your compensation model relies heavily on work RVUs (wRVUs), even modest changes can create physician dissatisfaction, retention risks, and budget surprises.

Impact modeling answers one core question: If your clinicians work the same way, what will change on paper — in credited wRVUs, productivity thresholds, and compensation — under new RVU rules?

Done well, impact modeling provides defensible forecasts, identifies exposed specialties, and outlines options — from “hold harmless” shadow RVUs to targeted dollar-per-wRVU adjustments or hybrid models.

It doesn’t require a large analytics team, but it does require the right data, the right crosswalk, and a repeatable workflow to align finance, operations, and physician leadership.

What impact modeling means

Most groups run two related models:

Productivity impact: How many fewer (or different) wRVUs will be credited under 2026 values for the same CPT volume and mix?
Compensation impact: If comp is tied to wRVUs (thresholds, tiers, dollar-per-wRVU), how does that change expected earnings?

For the -2.5% “efficiency adjustment” finalized by CMS for 2026, MGMA Government Affairs' resource recommends analyzing this reduction to work RVUs for non–time-based codes.

The basic workflow

Step 1. Pull code volume and mix

You need a dataset — from your PM system, billing clearinghouse reports, or revenue cycle platform — that includes, at minimum:

CPT/HCPCS code
Units
Date of service
Rendering provider (physician/APP)
Often: place of service, payer (optional but useful)

Many groups use the past 12 months (or last 6 months annualized) to capture seasonality and true mix.

Step 2. Map each code to old vs. new wRVUs

Use or create a lookup table that includes:

CPT® code
Current-year wRVU
2026 wRVU (or the adjusted wRVU for codes subject to the policy)

At the most basic level (when you can’t get a clean code-level crosswalk), groups estimate by service line (e.g., “What percent of our ortho wRVUs come from non–time-based codes?”).

Step 3. Recalculate wRVUs under 2026 assumptions

For each provider (and specialty):

Old wRVUs = Σ (units × old wRVU)
New wRVUs = Σ (units × new wRVU)
Delta = New – Old and % change

That gives you a clean picture of “same work, different credited wRVUs.”

Step 4. Translate the wRVU delta into compensation

Apply your compensation plan logic. For example:

Base + incentive above threshold
Tiered rates
Quality-withhold
Pool distribution
Straight dollar-per-wRVU

This is the step you will care about most as a practice leader, because it shows who falls below thresholds, whose total comp is at risk, and how much it would cost to “hold harmless” via dollar-per-wRVU adjustments or other plan changes.

Step 5. Summarize results and make decisions

Typically, the outputs are:

Impact by specialty, provider, and site
A “most exposed” list (in 2026, this will be radiology and procedure-heavy lines)
Scenarios:
Hold harmless (shadow RVUs)
Adjust dollar-per-wRVU
Adjust thresholds
Add hybrid components (quality/value/access)

Who should do this work?

In smaller practices without an analyst, you typically have one or two people — a practice administrator or executive director, billing manager or outsourced vendor partner, and/or CPA/finance advisor — who carry the load on modeling in a simplified version: High-level service line exposure and a few high-volume codes.

In multi-site private groups or small system-owned groups, it becomes more structured and easier to do code-level work, but governance and time can be constraining. A director of finance or controller, revenue cycle manager, and/or compensation/HR leader (if you have one) will be involved; some groups will have a business intelligence (BI) analyst. A medical director or chair would often review results.

Larger organizations will typically have the most robust formal governance and distributed ownership of the work across finance (physician enterprise finance), a physician compensation team, an analytics/BI team (SQL + dashboards), revenue cycle analytics, legal/compliance and/or FMV reviewer (for employed physicians), and specialty operational leadership for interpretation and communication.

What tools should you use?

Most midsize groups can get by with this “good enough” tool stack of: Excel (still the #1 tool for first-pass modeling); PM/billing system exports (CSV files); and an RVU crosswalk table (internal or vendor-provided). Optionally, tools such as Power BI/Tableau are useful for repeatable dashboards.

More automated tool stacks are common in larger systems, including: A data warehouse (SQL) pulling from PM + EHR + RCM; an analytics layer (e.g., Power BI/Tableau); a “physician comp system” module or internal comp calculator; and contract management and governance workflows.

External support such as compensation consultants and/or FMV vendors are common to validate assumptions if the organization has many employed physicians.

How often is sufficient?

It depends on organizational maturity and how tightly your comp is tied to wRVUs:

At minimum: Once during budget/comp planning, and again after the final PFS files are released (to confirm actual values).
In more mature organizations: Quarterly monitoring (because mix changes and recruitment/attrition change baseline exposure).
Most sophisticated: Monthly “shadow versus actual” tracking so leaders can explain variance and avoid surprises.

What additional resources are available?

These links won’t run the model for you, but they can reduce your lift in understanding code identification, contracting response, and benchmarking context: