Most risk adjustment programs still find documentation problems after the patient has gone home. A coder reviews the note days or weeks later, finds a chronic condition listed without evidence of management, and sends a query to a clinician who no longer remembers the visit. That model was always inefficient. With CMS now planning to audit every eligible Medicare Advantage contract and extrapolate the results, it has become a financial liability. The better approach is to capture the evidence for each diagnosis while the clinician is still in the room.
What MEAT Actually Requires
MEAT is a widely used industry framework, not a CMS regulation, but it reflects what auditors look for: evidence that a reported condition was addressed during a face-to-face encounter. Each diagnosis that drives payment should be supported by at least one of four elements:
- Monitor: signs, symptoms, disease progression or regression. Example: "CKD stage 3b, eGFR stable at 38 compared with six months ago."
- Evaluate: test results, medication effectiveness, response to treatment. Example: "A1c 8.4 percent, up from 7.6 percent; current regimen not achieving goal."
- Assess: discussion, counseling, documented status or severity. Example: "Major depressive disorder, recurrent, moderate. PHQ-9 of 14. Discussed therapy referral."
- Treat: medications, therapies, referrals or other modalities. Example: "Continue furosemide 40 mg daily for chronic systolic heart failure; refer to cardiology."
What does not count is equally important. A diagnosis that appears only in a problem list, a past medical history section, or a copied-forward template is not evidence of management. Neither is a condition named in the assessment without any linked monitoring, evaluation or treatment.
Why After-the-Fact Fixes Fall Short
Retrospective review still has a role, but relying on it as the primary control creates three problems.
It is late. A query sent weeks after the encounter depends on the clinician's memory. Addenda written long after a visit can draw scrutiny if they look like they were created to support a code rather than to correct a record.
It is expensive. Every query consumes coder time, clinician time and follow-up effort. Many go unanswered. The cost per supported HCC rises with each round trip.
It does not change behavior. When documentation is cleaned up downstream, clinicians rarely see the pattern. The same gaps recur in the next visit and the next quarter.
There is also a regulatory reason to move upstream. The OIG has reported that diagnoses appearing only on health risk assessments or chart reviews, without follow-up care, generated billions of dollars in MA payments and warrant scrutiny. Diagnoses that rest on a single, thin note are precisely the ones most likely to fail validation when CMS expands sample sizes to as many as 200 records per contract.
What Point-of-Care Capture Looks Like
Capturing MEAT at the point of care does not mean adding a checklist to every visit. It means designing the encounter so that the evidence is produced naturally as part of good clinical work.
Before the visit
Prepare a concise pre-visit summary of suspected and previously documented conditions, with the clinical evidence that supports each one: recent labs, active medications, specialist notes, prior diagnoses. The goal is to prompt clinical evaluation, not to suggest codes. Each item should be framed as a question the clinician can confirm or rule out.
During the visit
Structured inputs help. When vitals, screening instruments and medication reviews are captured in structured form, they become documented evidence of monitoring and evaluation. A PHQ-9 score supports a depression assessment. A recorded BMI with a documented weight management plan supports morbid obesity. A medication reconciliation supports treatment for each condition the medications address.
Before sign-off
This is the most valuable control point and the one most organizations skip. A documentation review before the clinician signs can flag diagnoses without supporting evidence, specificity gaps (diabetes documented without its complication, heart failure without type or acuity) and conditions mentioned in the history but not addressed. Fixing those issues takes seconds while the clinician still has the patient in mind. Fixing them three weeks later takes a query cycle.
A Practical Framework for Leaders
For a CMO or VP of Risk Adjustment building this capability, the work breaks into five parts:
- Define the standard. Publish a short, clinician-friendly guide to what supports a diagnosis, with examples for your highest-volume HCCs. Keep it to a page.
- Prioritize high-risk conditions. Focus first on categories where audits most often fail: acute conditions coded in outpatient settings, cancers without evidence of active treatment, and chronic conditions documented only as history.
- Build review into the workflow. Place a pre-signature documentation check in the encounter itself rather than in a separate coding queue.
- Measure support rates, not just capture rates. Track the percentage of submitted HCCs that would pass a RADV-style review, alongside recapture and suspect closure.
- Close the loop with clinicians. Share individual documentation patterns regularly and without blame. Clinicians generally want their notes to reflect the care they provided.
The Financial Case
Point-of-care capture protects revenue in two ways. It increases the share of legitimate, actively managed conditions that are documented accurately, and it reduces the share of submitted diagnoses that would be reversed in an audit. Under extrapolation, the second effect can matter as much as the first. A modest improvement in support rate across a contract can outweigh a larger increase in raw capture that carries weak documentation.
There is also a clinical dividend. Notes that clearly show monitoring, evaluation, assessment and treatment are better notes. They support care coordination, quality measurement and safer handoffs.
Where Technology Fits
Curitics builds this into the encounter itself. Its component-based Encounter Builder captures vitals with ICD-10 suggestions and validated screenings, closes quality gaps during the visit, and runs an AI documentation review before e-signature, while concurrent coding review and provider queries handle what still needs clarification. The aim is for the note to be audit-ready the moment it is signed.
The Takeaway
RADV expansion rewards organizations whose documentation is right the first time. Shifting MEAT capture from the back office to the exam room is not a coding project. It is a change in how encounters are designed, and it pays off in revenue integrity, clinician time and clinical quality at once.
