Medicare Advantage Chart Review Software

Why Medicare Advantage Chart Review Needs Automation

Medicare Advantage plans rely on accurate HCC coding to ensure appropriate risk-adjusted payments from CMS. Every submitted HCC code must be supported by clinical documentation that meets MEAT criteria—Monitoring, Evaluation, Assessment, and Treatment. CMS audits, including RADV, can recover payments for any code that lacks sufficient evidence.

The volume challenge is significant. A mid-size MA plan may need to review tens of thousands of charts annually for risk adjustment accuracy. Manual chart review typically takes 3–4 hours per record, creating a bottleneck that forces organizations to choose between thoroughness and timeliness.

Inconsistency compounds the problem. Different reviewers apply MEAT criteria differently, producing variable evidence quality that weakens audit defensibility. When CMS requests documentation, inconsistent formats and missing citations slow response times and increase exposure.

RafCite™ by CodaFend is on-premise chart review software built specifically for Medicare Advantage risk adjustment workflows. It automates the evidence extraction process so your coding team can focus on clinical judgment instead of manual record searching.

How RafCite Automates Chart Review

RafCite processes medical records through a structured pipeline designed for MA plan chart review at scale:

Built for Medicare Advantage Compliance

• Under 5 minutes per chart — compared to 3–4 hours of manual review, enabling your team to cover significantly more volume within the same review cycle
• MEAT criteria alignment — structured evidence extraction mapped directly to CMS documentation expectations for risk adjustment
• V28 and V24 model support — dual model version support with automatic hierarchy exclusion and RAF weight calculations for blended payment years
• Citation-backed output — every MEAT finding includes the exact page, section, and text reference from the medical record—no ambiguous summaries
• On-premise deployment — zero PHI egress architecture with containerized components running entirely on your infrastructure
• Annual platform license — no per-chart fees, no per-seat charges, and no cloud processing costs regardless of review volume

Chart Review Workflow with RafCite

RafCite fits into your existing risk adjustment chart review process as a pre-screening layer that handles the labor-intensive evidence extraction step:

1. Record upload — Medical records are loaded into RafCite in standard clinical document formats. Records remain on your local infrastructure throughout processing.
2. Automated analysis — RafCite processes each record, mapping submitted HCC codes to the applicable CMS model and extracting MEAT evidence with source citations. Processing typically completes in under 5 minutes per chart.
3. Evidence review — Your coding team receives structured output for each chart, with every HCC code paired against the supporting documentation found (or gaps identified). Reviewers focus on clinical validation rather than manual searching.
4. Final determination — Coders make the final accept, modify, or flag decision for each HCC. The evidence packet, including the reviewer's determination, forms the audit-ready documentation for that chart.

This workflow preserves the clinical judgment that CMS requires while eliminating the hours spent manually locating and organizing evidence across lengthy medical records.

Who Benefits from Automated Chart Review

RafCite is built for organizations that manage Medicare Advantage risk adjustment programs at scale:

• MA health plans — processing annual and prospective chart reviews across large member populations
• Coding teams — seeking to reduce per-chart review time while maintaining documentation quality for RADV audit readiness
• Risk adjustment vendors — supporting multiple MA plan clients with consistent, high-throughput chart review services
• Compliance officers — requiring standardized evidence formats and audit trails for regulatory response
• Healthcare organizations — that need on-premise AI processing to meet internal PHI security policies

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