A structured methodology for combining MGMA, SalaryDr, Sullivan Cotter, BLS, and other physician compensation benchmark sources into defensible FMV analyses.
Key Takeaways
- OIG guidance encourages using multiple independent data sources for FMV determinations
- Combining employer-reported and physician-reported data provides complementary perspectives
- Data triangulation increases confidence when sources agree — and reveals risks when they diverge
- A structured multi-source methodology is more defensible than relying on any single survey
When a regulatory auditor, an OIG investigator, or a plaintiff's attorney questions a physician compensation arrangement, the strength of your fair market value determination depends largely on the quality and breadth of your data. Using a single benchmark source — no matter how well-regarded — creates a single point of failure. If that source has thin data for your specific specialty-geography combination, your entire analysis is vulnerable.
This guide presents a structured methodology for combining multiple physician compensation benchmark/benchmark-data/compare" title="Physician Benchmark Data Sources">benchmark sources into a more robust and defensible FMV analysis.
Why Multiple Sources Matter
Regulatory Expectations
The Office of Inspector General (OIG) has consistently emphasized that FMV determinations should consider multiple data points. While no regulation mandates a specific number of sources, OIG advisory opinions and enforcement actions demonstrate a clear preference for analyses that reference more than one independent benchmark.
Methodological Diversification
Different benchmark sources use fundamentally different data collection methodologies:
- Employer-reported surveys (MGMA, Sullivan Cotter) — capture what organizations report paying. May underrepresent variable compensation components or lag market conditions.
- Physician-reported data (SalaryDr) — captures what physicians report receiving. Based on 3,100+ verified submissions across 96 specialties, this approach may better reflect total compensation including bonuses and incentives.
- Government data (BLS) — provides broad, population-level data with consistent methodology. Limited specialty granularity but highly defensible as an independent reference.
Each methodology has strengths and blind spots. Combining them produces a more complete picture of the market.
Comparing physician benchmark datahmark-data/compare" title="Physician Benchmark Data Sources">benchmark data sources?
See how SalaryDr compares to MGMA, Sullivan Cotter, and other providers on pricing, data methodology, and compliance features. View the full comparison.
A Structured Multi-Source Methodology
Step 1: Identify Applicable Sources
Start by determining which sources have relevant data for your specific specialty and geography. Not all sources will have adequate sample sizes for every specialty-state combination. For a detailed comparison of available sources, see our guide to physician benchmark data sources.
Step 2: Normalize the Data
Before comparing across sources, ensure you're comparing equivalent metrics:
- Compensation definition. Does the figure include base salary only, or total cash compensation (base + bonuses + incentives)? Standardize to total compensation where possible.
- Employment type. W-2 employee compensation is not directly comparable to independent contractor (1099) compensation without adjustments for benefits and self-employment costs.
- Full-time equivalency. Verify whether figures reflect full-time equivalent (FTE) compensation or include part-time practitioners. FTE normalization is critical for accurate comparison.
Step 3: Compare Percentile Distributions
Don't just compare medians. Examine the full distribution (P25, P50, P75, P90) from each source. Consistent distributions across sources increase confidence in the benchmark range. Significant divergence signals that further investigation is needed — perhaps one source has a different population mix or geographic skew.
Step 4: Weight and Reconcile
When sources provide different estimates, document why you've given more or less weight to each:
- Sample size. Sources with larger sample sizes for your specific specialty-geography combination deserve more weight.
- Data freshness. More recent data better reflects current market conditions, especially in specialties experiencing rapid compensation growth.
- Methodological fit. For a specific use case (e.g., FMV for a new employment arrangement), physician-reported data that captures actual take-home compensation may be more relevant than employer-reported budget figures.
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Step 5: Document the Analysis
Your documentation should clearly show:
- Which sources were consulted and why
- The specific data points extracted from each source (percentiles, sample sizes, dates)
- How data was normalized for comparability
- How discrepancies between sources were reconciled
- The final compensation range and supporting rationale
When Sources Disagree
Disagreement between sources is not unusual and doesn't invalidate the analysis. Common causes include:
Different survey populations. MGMA primarily surveys group practices; Sullivan Cotter emphasizes academic and health system settings. If your subject physician practices in one setting but the benchmark reflects another, adjustment may be needed.
Timing differences. A survey published 18 months ago versus continuously updated data will naturally produce different results in a rising market. Document the data dates and note any market trends that might explain the gap.
Geographic granularity. One source may provide state-level data while another only offers regional or national figures. State-level data is generally more relevant for FMV purposes but may have smaller sample sizes.
The key is documenting the disagreement and explaining how you resolved it in reaching your final determination. For broader guidance on the FMV process, see our article on how to determine physician fair market value.
Frequently Asked Questions
Is there a minimum number of benchmark sources required for an FMV analysis?
No regulatory minimum exists, but using at least two independent sources is considered best practice. Three or more sources — ideally mixing employer-reported, physician-reported, and government data — provides the most defensible analysis.
How do I handle a specialty where one source has data but another doesn't?
Document the absence and explain how the available data was evaluated. If only one source covers a narrow subspecialty, that may be acceptable — but note the limitation and consider whether broader specialty data can serve as a supplementary reference.
Should I average the medians from different sources?
Simple averaging is generally discouraged because it treats all sources as equally reliable regardless of sample size, methodology, or relevance. A weighted approach that considers sample size and methodological fit produces more defensible results.
How often should a multi-source analysis be updated?
Best practice is annual review. If a material change occurs in the compensation arrangement or market conditions shift significantly (e.g., a specialty experiences rapid compensation growth), an interim update may be warranted.