The MEAT Criteria Problem That’s Costing You Millions

Every risk adjustment coder learns about MEAT criteria in their first week. Monitor, Evaluate, Assess, Treat. Simple enough. The provider needs to document at least one of these elements for every chronic condition. Easy to understand, hard to execute.

The problem isn’t that coders don’t know the rules. The problem is that providers document like they’re writing notes for other clinicians, not for CMS auditors. And the gap between how doctors write and what risk adjustment requires is where your revenue disappears.

What Actually Happens in Real Charts

Here’s a scenario that plays out thousands of times a day. Your coder is reviewing a progress note for a 72-year-old Medicare Advantage patient. The assessment section lists: Type 2 Diabetes, Congestive Heart Failure, Chronic Kidney Disease Stage 3, Hypertension, and COPD.

Five chronic conditions. Five potential HCCs. Looks good, right?

Then your coder reads the actual note. The chief complaint is knee pain from a fall. The history discusses the fall and current pain level. The exam focuses on the knee. The plan includes X-rays and pain medication. Those five chronic conditions appear nowhere except as a list in the assessment.

Can your coder code those five HCCs? No. Should they anyway because the patient obviously has these conditions? Absolutely not. That’s how audit disasters happen.

Where Coders Make Mistakes

The most common coding error isn’t aggressive overcoding. It’s inconsistent application of MEAT criteria across different coders. Give ten coders the same chart, and you’ll get different coding decisions on borderline documentation. Some will code anything listed in the assessment. Others won’t code without explicit MEAT evidence.

This inconsistency creates two problems. First, you don’t know your true risk position. Are you overcoding and building audit liability? Are you undercoding and leaving money on the table? Second, you can’t educate providers about documentation quality because you don’t have consistent data showing where gaps are.

What Good MEAT Documentation Looks Like

Let’s get specific. For heart failure, weak documentation says: “Continue current CHF medications.” That’s vague. Which medications? What’s the current status?

Strong documentation says: “Patient’s heart failure remains compensated on current regimen of Lasix 40mg daily and carvedilol 12.5mg twice daily. No dyspnea, orthopnea, or lower extremity edema on exam today. Weight stable at 185 pounds.” That’s monitoring (weight, symptoms, exam findings), evaluation (clinical judgment), and treatment (specific medications).

For diabetes, weak documentation says: “Diabetes controlled.” Controlled based on what?

Strong documentation says: “A1C today is 7.2, down from 7.8 three months ago. Patient reports good medication compliance and has been following dietary recommendations. Will continue metformin 1000mg twice daily.” That’s monitoring (A1C trend), evaluation (clinical judgment), and treatment (medication continuation).

The difference isn’t about length. It’s about specificity. Good MEAT documentation ties clinical data to clinical thinking to clinical action.

The Query Problem Nobody Solves

When MEAT criteria is missing or unclear, coders should query the provider. In practice, this rarely happens consistently. Queries are seen as burdensome. They slow down workflow. Providers often don’t respond. So coders make judgment calls instead.

But those judgment calls are exactly what get organizations in trouble during audits. CMS doesn’t care that your coder “knew” the patient had active heart failure based on the medication list. They care whether the note documents MEAT criteria. If it doesn’t, the HCC gets deleted.

Organizations that handle MEAT criteria well have clear query triggers built into their workflow. For example: “Any chronic condition listed in the assessment that has no corresponding documentation in the HPI, exam, or plan requires a provider query before coding.” That’s a rule coders can follow consistently.

And the queries themselves matter. Don’t send vague requests like “Please clarify diagnosis.” Send specific requests: “Your note lists CHF in the assessment but doesn’t document current symptoms, exam findings, or treatment. Can you clarify whether you addressed the heart failure during this visit?”

Building Better Documentation at the Source

The long-term fix for MEAT criteria problems isn’t better coding. It’s better documentation. When providers understand what’s required and why it matters, they write better notes.

Share real examples with your providers. Show them a note that perfectly captures MEAT criteria alongside one that doesn’t. Most physicians are surprised their documentation is inadequate. They’re documenting what seems clinically relevant to them. They don’t realize that “continue current meds” without specificity doesn’t support risk adjustment coding.

Regular feedback helps too. Instead of annual educational sessions, provide monthly reports showing each provider’s MEAT criteria documentation rates. When Dr. Johnson sees that only 60% of her diabetic patient notes contain adequate MEAT criteria compared to 85% for the group average, she pays attention.

MEAT criteria coding gets easier when providers document better. Until then, your coders are making tough calls on every borderline chart. Give them clear standards, enforce consistent application, and don’t skip the queries that protect your defensibility.