The AI-powered HCC coding platform for in-house risk adjustment teams

Charta gives in-house coders at health plans and risk-bearing provider organizations the same evidence-first workflow behind its retrospective and RADV programs. AI reads every chart at intake, including scans, faxes, and handwritten notes, then presents coders with the strongest evidence for each potential HCC first. Coders validate incremental HCCs and flag unsupported codes for deletion in one review, against your organization's coding policies, with one-click access to every cited source.

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Your coders spend more time searching than validating.

Most in-house coding workflows send coders page by page through every chart, including charts with no HCC value and redundant documentation for conditions already captured. Most are also built to find adds, so unsupported codes stay in the submission. That model no longer fits the regulatory environment: CMS now audits every Medicare Advantage contract each year, treats each unsupported HCC in a RADV sample as a full overpayment, and has reduced the number of risk-adjustable diagnoses under V28.

Put every coder hour toward a coding decision.

Charta ranks clinical evidence by strength and directs coders to the date of service, page range, and MEAT criteria behind each recommendation. Once a coder validates an HCC, lower-priority documentation supporting the same condition no longer needs review. A separate queue presents HCCs flagged for deletion alongside the strongest available evidence to retain them, so every add and delete carries a citation and decision trail. Managers track charts per hour, review time, and validation rate by coder in real time.

99%

HCC recall rate of Charta's Smart Scan engine, which surfaces the evidence your coders review.

20%

About 20% of MA member charts contain no relevant HCC evidence. Charta removes them from the coding queue before a coder opens them.

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See how your coders can validate adds and deletes against cited evidence in a single workflow, across retrospective review, RADV, and second-pass QA.

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