Supported & Recognized By
Clairdoc helps billing teams find underpayments, prevent denials before submission, and recover denied claims — faster, with less manual work.
Instead of waiting for denials, Clairdoc reviews claims early, flags preventable billing problems, and surfaces only the cases that need human judgment.
Clairdoc is built around the actual places claims break before submission and reimbursement.
Flags coding combinations that can trigger rework or denials.
Validates claims against payer logic before they leave the queue.
Surfaces missing or inconsistent support for the billed claim.
Highlights claims that may need follow-up before submission.
Detects cases where the note and billed claim may not align.
Routes uncertain or high-risk claims to reviewers with context.
Clairdoc combines AI agents, structured billing logic, and escalation workflows so teams can automate routine review without losing control.
Claims and encounter data enter Clairdoc without a heavy implementation project.
Structured billing rules, payer edits, and AI reasoning keep decisions grounded and explainable.
Clairdoc flags denial risk, coding mismatches, and prior auth issues, then routes edge cases to humans.
Corrections, denials, and approvals strengthen the system over time.
Clairdoc surfaces throughput, claim readiness, review pressure, and preventable revenue risk at the queue level.
Queue-level visibility into throughput, review pressure, and denial drivers.
Clairdoc is shaped by live workflow inputs from billing operators, physicians, and specialty-practice teams.
Built with live billing feedback loops, real claims, and human accountability.
Billing teams contribute edge cases and rejection patterns that sharpen the system over time.
Clairdoc is tested against real claims and payer-policy friction.
Routine throughput stays automated while high-risk decisions stay reviewable.
Claims are denied when clinical context and billing context do not align. Clairdoc evaluates both, not billing rules alone.
Rising denial rates, increasing payer complexity, and growing staffing shortages are making manual claims review increasingly unsustainable.
Claims are not denied because coding is hard. They are denied because clinical context and billing context do not always align.
Flags claims where the documentation may not support what is being billed.
Uses patient complexity alongside payer rules, coding guidelines, documentation, and claim outcomes to identify claims at risk.
Identifies high-risk claims before submission so billing teams spend less time appealing and correcting claims.
Clairdoc is built by a team with peer-reviewed machine learning research on patient complexity, accepted at ICML 2026. That research helps inform Clairdoc's approach to evaluating patient complexity as part of claims review, alongside payer policies, coding guidelines, and documentation analysis.
Read publication →Clairdoc is offering pilots for healthcare revenue cycle teams ready to find underpayments, prevent denials, and recover denied claims with less manual work.
