MEAT Criteria: The Documentation Standard That Makes or Breaks Your RADV Audit

If you work in risk adjustment, you’ve heard the term “MEAT criteria” more times than you can count. But understanding what it means and actually documenting it correctly are two different challenges. The difference between the two can cost your organization hundreds of thousands of dollars when CMS auditors arrive.
Let’s break down what MEAT really means, why auditors care so much, and how to fix the most common documentation failures before they become expensive problems.
What MEAT Actually Stands For
MEAT is an acronym: Monitor, Evaluate, Assess, or Treat. For a diagnosis to be valid for risk adjustment, the medical record must show that the provider did at least one of these four things with that condition during the encounter. It’s not enough for a diagnosis to appear somewhere in the chart. There must be active clinical engagement with the condition.
This standard exists because CMS wants to pay for conditions that actually require ongoing clinical management. If a patient has diabetes, the provider should be checking blood sugar levels, adjusting medications, counseling on diet, or doing something that shows active treatment. Passive mentions don’t count.
Why This Matters for RADV Audits
During a RADV audit, CMS reviewers go through your submitted charts line by line. For every HCC code you claimed, they look for supporting documentation. And the first thing they check? MEAT Criteria Coding.
If they can’t find evidence of monitoring, evaluation, assessment, or treatment, they reject the diagnosis. That rejected diagnosis costs you the associated HCC payment. One rejected HCC might represent $3,000 to $15,000 in annual revenue, depending on the code. Multiply that across a sample of 200 charts, and the math gets ugly fast.
The real danger comes from extrapolation. If CMS finds a pattern of missing MEAT evidence across your sample, they can apply that error rate to your entire contract. A 10% error rate across tens of thousands of members translates to multi-million dollar clawbacks.
The Five Most Common MEAT Documentation Failures
Walk through any organization’s charts and you’ll see the same patterns repeatedly.
First: vague documentation. Providers write “diabetes” or “hypertension noted” without any context. These phrases tell you nothing about active management. CMS auditors will reject them every time.
Second: history references. A note says “history of stroke” or “past heart failure.” The word “history” is a red flag. It suggests the condition happened in the past but isn’t currently active. For risk adjustment, you need current conditions with current management.
Third: missing treatment information. The diagnosis appears in the assessment section, but there’s no plan. The provider lists CHF but doesn’t mention diuretic adjustments, fluid restrictions, or monitoring plans. No treatment plan means no MEAT.
Fourth: medication lists without context. The chart shows the patient takes metformin, but there’s no mention of diabetes in the provider’s note. The mere presence of a medication isn’t enough. Also the provider must document the condition and the treatment connection.
Fifth: copy-paste documentation. Providers copy forward the same problem list visit after visit without updating it. Six months later, it still says “COPD stable” with identical wording. Auditors spot this pattern instantly. Repeated identical language suggests the provider isn’t actually evaluating the condition at each visit.
What Good MEAT Documentation Looks Like
Let’s compare two examples for a patient with Type 2 diabetes.
Bad documentation: “DM2 stable. Continue current meds.”
Why it fails: “Stable” is vague. There’s no specific information about monitoring, evaluation, or treatment. An auditor will reject this.
Good documentation: “Type 2 diabetes with neuropathy. A1C today 7.2%, improved from 7.8% three months ago. Patient reports good adherence to metformin 1000mg twice daily. Peripheral sensation intact bilaterally. Continue current medication regimen. Recheck A1C in 3 months.”
Why it works: This note shows active monitoring (A1C testing), evaluation (comparing current results to past), assessment (checking for complications like neuropathy), and treatment (continuing medication with a follow-up plan). Multiple MEAT elements are present.
How to Fix MEAT Documentation Problems
Start with provider education, but make it specific. Don’t just tell providers about MEAT. Show them examples from your own charts. Point out what works and what doesn’t. Most providers want to document correctly. They just need concrete guidance.
Update your EHR templates. Build prompts that encourage complete documentation. When a provider lists a chronic condition, the template should require them to document specific management activities. This doesn’t mean more clicking. It means smarter templates that guide documentation naturally.
Run internal audits regularly. Don’t wait for CMS to find your problems. Sample your own charts quarterly. Look for the common failures we listed above. Track which providers consistently document well and which need additional support.
Create feedback loops. When your internal audits find documentation issues, close the loop with providers quickly. Specific, timely feedback drives behavior change better than annual training sessions.
Implement pre-submission validation. Before you send encounter data to CMS, run it through a validation process. Flag any diagnoses that lack clear MEAT support. Give your team time to query providers and fix issues before they become audit findings.
The Real Cost of Poor MEAT Documentation
Organizations that ignore MEAT criteria pay for it eventually. RADV audits aren’t optional. They’re coming. And when auditors review your charts, they won’t give you credit for conditions you “meant” to document properly. They’ll only count what’s actually written in the clinical record.
Getting MEAT right protects your revenue, reduces audit risk, and improves the defensibility of your risk adjustment program. The alternative is expensive.
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