Gill BlitzYour coding team isn’t making mistakes because they’re bad at their jobs. They’re missing HCCs because providers document like they’re writing notes for themselves, not for CMS auditors. That gap between clinical documentation and risk adjustment requirements is where revenue disappears.
MEAT criteria is the bridge. Monitor, Evaluate, Assess, Treat. Four words that determine whether an HCC is defensible or not. Your coders need to find at least one of these elements in the provider’s note for every chronic condition they code. No MEAT, no code.
Where Coders Actually Struggle
Ask any experienced risk adjustment coder what frustrates them most, and they’ll describe the same scenario. They’re reviewing a note for a patient with diabetes, heart failure, and CKD. All three conditions are listed in the assessment section. But when they read the actual note, there’s barely any mention. No labs, no exam findings, no treatment changes. Just a list of diagnoses copied from the last visit.
Can they code these HCCs? No. Will they anyway? Sometimes yes, because they know the patient has these conditions and they’re trying to capture legitimate revenue. That’s exactly how audit problems start.
The issue isn’t that coders don’t understand MEAT criteria. It’s that they’re constantly making judgment calls on borderline documentation, and different coders make different calls. That inconsistency creates both compliance risk and lost revenue.
What Good MEAT Actually Looks Like
For diabetes, monitoring might be: “A1C today is 7.9, up from 7.2 three months ago.” Or: “Patient reports blood sugars running 140-160 fasting.” The provider is tracking the condition’s status.
Evaluation shows clinical judgment: “Diabetes control is slipping despite reported medication compliance.” Or: “Patient’s management remains stable on current regimen.” The provider is thinking about the condition.
Assessment appears in the plan: “Will continue metformin 1000mg twice daily.” Or: “Patient’s diabetes is at goal.” There’s some conclusion tied to the condition.
Treatment is usually easiest: “Increased insulin to 20 units at bedtime.” Or: “Counseled patient on carbohydrate counting.” Any therapeutic intervention counts.
The MEAT evidence needs to connect clearly to the specific condition. A note that says “continue all current medications” doesn’t tell you which medications are for which conditions. That’s not defensible.
The Hidden MEAT Problem
Sometimes MEAT criteria is in the note, but it’s buried. A progress note might not list CKD in the assessment at all, but document: “Creatinine is 2.1, stable from last visit, GFR estimated at 38.” That’s monitoring for CKD. An experienced coder will catch it. A rushed coder won’t.
This is why chart review speed and coding accuracy exist in tension. Push your coders to review 30 charts a day, and they’ll miss embedded MEAT evidence. Let them take their time, and your throughput drops.
The solution isn’t choosing between speed and accuracy. It’s giving coders better tools to identify MEAT criteria quickly. When technology surfaces relevant clinical evidence automatically, coders spend less time hunting and more time making informed decisions.
The Query Decision
When MEAT criteria is unclear or missing, coders face an impossible choice. Don’t code it and leave revenue on the table. Query the provider and slow everything down. Or code it anyway and accept the audit risk.
Most coding operations handle this inconsistently. Some coders query liberally, others barely query at all. Without clear guidelines, you get chaos.
Develop specific query triggers. For example: “Query any chronic condition listed in the assessment when the note contains no labs, no symptom documentation, and no treatment related to that condition.” That’s a clear rule your team can follow.
Use queries as teaching opportunities. Instead of asking “Did you address this condition?”, explain what’s missing: “To support coding for CHF, I need evidence you monitored symptoms, evaluated status, or adjusted treatment. Can you clarify whether you addressed the heart failure?”
Building Better Habits
The long-term solution isn’t better coding. It’s better documentation. When providers understand what MEAT criteria requires and why it matters, they document differently.
Share examples from your own charts with your providers. Show them a note that beautifully captures MEAT criteria, then show them one that doesn’t. The contrast is usually obvious once you point it out. Most providers want to document well. They just need to know what that looks like.
MEAT criteria coding gets easier when documentation improves. Until then, your coders are making tough judgment calls on every chart. Give them clear guidelines, consistent training, and the time they need to get it right.
