MEAT Evidence Extraction Software

What is MEAT Evidence Extraction?

MEAT stands for Monitoring, Evaluation, Assessment, and Treatment—the four categories of clinical documentation that CMS uses to determine whether an HCC code is supported by the medical record. For a diagnosis to count toward risk adjustment, the patient’s chart must contain evidence that the condition was actively managed during the encounter, not simply listed in the problem list.

CMS requires at least one MEAT element to be documented for each HCC code submitted. During RADV audits, reviewers examine the medical record line by line to confirm that supporting evidence exists. If MEAT criteria are not met, the HCC is disallowed and the associated RAF payment is subject to recovery.

Manual MEAT extraction is one of the most time-consuming steps in chart review. Coders typically spend 3–4 hours per chart reading through clinical notes, lab results, and encounter summaries to locate and document each MEAT element. At scale, this creates significant bottlenecks in RADV preparation and ongoing risk adjustment validation.

RafCite™ automates this process. It reads medical records on-premise, identifies MEAT evidence for each HCC code, and produces structured output with exact source citations—reducing extraction time from hours to minutes while maintaining the documentation rigor that auditors expect.

How RafCite Extracts MEAT Evidence

RafCite evaluates medical records against each of the four MEAT criteria, extracting relevant clinical documentation with page and section references:

Monitoring Identifies documentation of ongoing condition monitoring—lab orders, test results, vital sign tracking, imaging follow-ups, and scheduled surveillance that demonstrate active management of the diagnosed condition.

Evaluation Locates clinical evaluations including history of present illness, review of systems, physical examination findings, and diagnostic workups that confirm the provider assessed the condition during the encounter.

Assessment Extracts provider assessments such as clinical impressions, differential diagnoses, severity staging, and condition status updates that reflect the provider’s judgment on the current state of the condition.

Treatment Captures treatment-related documentation including medication management, therapeutic interventions, referrals, care plan modifications, and patient education that show the condition is being actively treated.

From Raw Records to Structured Evidence

RafCite follows a structured extraction pipeline that transforms unstructured medical records into organized, citation-backed MEAT evidence:

Record ingestion — Medical records are uploaded in standard formats. RafCite parses clinical notes, lab results, encounter documentation, and specialist reports.
HCC code identification — Each submitted diagnosis code is mapped to its corresponding HCC category with the applicable CMS model version (V28 or V24).
MEAT evidence scanning — The system examines the full record for documentation that satisfies Monitoring, Evaluation, Assessment, or Treatment criteria for each HCC.
Citation extraction — Every identified MEAT element is linked to its exact location in the source record—page number, section, and relevant text passage.
Evidence packet assembly — Results are compiled into a structured output organized by HCC code, MEAT category, and supporting citation—ready for reviewer validation.

Reviewer-ready output. RafCite does not make coding decisions. It surfaces the evidence and organizes it so your compliance team can focus on clinical judgment rather than manual record searching.

Why Automated MEAT Extraction Matters

The difference between manual and automated MEAT evidence extraction is measurable across four dimensions:

Speed — RafCite processes a chart in under 5 minutes, compared to 3–4 hours of manual review. For organizations reviewing thousands of charts annually, this translates to weeks of recovered capacity.
Consistency — Manual extraction quality varies by reviewer experience, fatigue, and interpretation. Automated extraction applies the same criteria to every record, every time.
Citation accuracy — Every MEAT finding includes exact source references. Reviewers can verify evidence directly against the record without re-reading the entire chart.
Audit defensibility — Structured, citation-backed evidence packets are easier for auditors to evaluate and more defensible than free-form reviewer notes.

These improvements compound at scale. Organizations handling RADV audit populations of hundreds or thousands of charts can shift from reactive scrambling to systematic, audit-ready preparation.

MEAT Evidence in RADV Context

MEAT extraction is the foundation of RADV HCC validation. When CMS selects a Medicare Advantage organization for a Risk Adjustment Data Validation audit, the plan must demonstrate that every sampled HCC code is supported by clinical documentation meeting MEAT criteria.

Without structured MEAT evidence, organizations face two risks: codes that are genuinely supported may be disallowed because the evidence was not presented clearly, and codes that lack support may not be identified until the audit response is due.

RafCite integrates MEAT extraction into the broader validation workflow:

Pre-submission validation — Extract MEAT evidence before HCC codes are submitted, identifying gaps early
Retrospective chart review — Process existing charts through the chart review pipeline to build evidence packets for your current HCC population
RADV response preparation — When a RADV audit notice arrives, generate evidence packets for the sampled population in days rather than weeks
Ongoing compliance monitoring — Run periodic extraction against submitted codes to maintain continuous audit readiness

All processing runs on-premise. MEAT extraction involves Protected Health Information. RafCite operates entirely within your infrastructure with zero PHI egress—no data leaves your network during processing.

Frequently Asked Questions

What are MEAT criteria in HCC coding?

MEAT stands for Monitoring, Evaluation, Assessment, and Treatment. These are the four categories of clinical documentation that CMS requires to support an HCC code submission. At least one MEAT element must be present in the medical record for a given condition to be considered a valid, supported diagnosis for risk adjustment purposes.

How accurate is automated MEAT evidence extraction?

RafCite produces structured, citation-backed evidence that points to exact locations in the medical record. It is designed as a pre-screening tool that surfaces relevant evidence for human reviewers to confirm. Every extracted finding includes page and section references so your coding staff can verify the evidence against the source documentation.

What medical record formats does RafCite accept?

RafCite processes standard clinical document formats including PDF medical records, scanned chart images with OCR, and structured clinical notes. The system parses encounter documentation, lab results, progress notes, and specialist reports to identify MEAT evidence across the full patient record.

How does automated extraction change the reviewer workflow?

Instead of reading through an entire medical record manually, reviewers receive a structured evidence packet with each MEAT element identified, categorized, and linked to its source location. This shifts the reviewer role from searching to validating—they confirm or reject pre-identified evidence rather than hunting for it from scratch.

Automate MEAT Evidence Extraction

See how RafCite extracts and organizes MEAT evidence from medical records—with exact source citations and zero PHI leaving your network.

Request a Demo → Learn More About RafCite