65% of denied claims are never reworked. AI denial resolution reads the CARC/RARC codes, identifies the error, builds the corrected claim, and resubmits. Here is the step-by-step workflow.
A claim gets denied. The remittance advice arrives with a CARC code and a RARC code. Your biller looks at it, tries to figure out what went wrong, maybe fixes it, maybe puts it in a pile for later. According to MGMA, 65% of denied claims are never reworked. That's not because billers don't care. It's because the volume is overwhelming and each denial requires detective work.
AI denial resolution changes this math. It reads the denial reason codes automatically, identifies exactly what went wrong, builds the corrected claim, and resubmits. The biller reviews and approves rather than doing the detective work from scratch.
The anatomy of a claim denial
Every denied claim comes back with two codes that explain why. Understanding these codes is the first step to resolving denials efficiently.
CARC (Claim Adjustment Reason Code): Tells you the general reason for the denial. Examples: CARC 16 (missing information), CARC 50 (not medically necessary), CARC 197 (authorization required), CARC 29 (timely filing expired). There are over 300 CARC codes.
RARC (Remittance Advice Remark Code): Provides additional detail about the CARC. For example, CARC 16 with RARC N382 means "missing or incomplete service facility information." The RARC narrows down the specific field or data element that caused the denial.
A human biller reads these codes, interprets them, looks up the original claim, identifies the error, corrects it, and resubmits. This process takes 15-30 minutes per denial. For a practice with 50 denials per month, that's 12-25 hours of biller time.
How AI reads and resolves denials
An AI denial resolution system processes denials in four steps:
Step 1: Ingest the remittance. The system reads the ERA (Electronic Remittance Advice) and extracts the CARC code, RARC code, denied amount, and original claim reference number. This happens automatically when the ERA arrives from the clearinghouse.
Step 2: Diagnose the error. Based on the CARC/RARC combination, the AI identifies the specific issue. CARC 4 with RARC M76? Missing modifier 25 on the E/M code when a procedure was billed on the same date. CARC 50 with RARC N386? The diagnosis code doesn't support medical necessity for the procedure. The AI knows the fix for each combination.
Step 3: Build the corrected claim. The AI pulls the original claim data, applies the correction (adds the missing modifier, swaps to a more specific ICD-10 code, attaches the authorization number), and generates the corrected claim. The biller sees a side-by-side comparison: original claim vs. corrected claim with the change highlighted.
Step 4: Resubmit. After biller approval, the corrected claim is resubmitted via EDI. The system tracks the resubmission and alerts the biller if the appeal is denied again, escalating to manual review.
Common denial patterns the AI catches
Missing modifier 25: When a practice bills an E/M code (99213, 99214) and a procedure (20610, 11102) on the same date, modifier 25 must be on the E/M code. This is the single most common denial that AI catches. Revenue impact: - per occurrence.
Diagnosis specificity: Using M54.5 (low back pain, unspecified) when documentation says "left-sided lumbar radiculopathy" (M54.41). Payers increasingly deny unspecified codes when the note contains enough detail for a specific code.
Eligibility lapses: CARC 27 (patient not eligible on date of service). The AI flags these for front desk follow-up: update the patient's insurance on file and resubmit with the correct payer information.
Timely filing: CARC 29 denials are unrecoverable. But the AI prevents them by flagging claims approaching their filing deadline. Medicare: 365 days. Most commercial payers: 90-180 days.
The revenue impact of automated denial resolution
Practices that implement AI denial resolution see consistent results:
Denial rate reduction: 35% to 45% fewer denials through pre-submission claim scrubbing. Claims that would have been denied are caught and corrected before they're submitted.
Recovery rate on remaining denials: 78% of denied claims overturned on first appeal (vs. 35% industry average for manual appeals). The AI identifies the correct fix on the first attempt rather than guessing.
Time savings: Biller time on denial management drops from 12-25 hours/month to 3-5 hours/month. The biller reviews AI recommendations rather than doing the research from scratch.
Revenue recovered: The average practice recovers ,000 per quarter from claims that would have otherwise remained unpaid.
Frequently asked questions
Can AI resolve all types of claim denials?
AI handles the 80% of denials that follow predictable patterns: coding errors, missing modifiers, eligibility issues, authorization gaps, and bundling edits. The remaining 20% (complex medical necessity appeals, unusual payer-specific rules) are flagged for manual review by the billing team.
How fast does AI process a denied claim?
The AI diagnoses the denial reason and generates the corrected claim within seconds of receiving the ERA. The biller reviews and approves the correction. Total turnaround from denial receipt to resubmission: typically under 24 hours vs. 7-14 days for manual rework.
Does the biller still need to review AI corrections?
Yes. The AI recommends corrections and builds the corrected claim, but a human biller reviews and approves every resubmission. The biller retains full authority over what gets sent to the payer.
What percentage of denied claims can be recovered?
With AI-assisted denial resolution, practices recover 78% of denied claims on first appeal. For the remaining 22%, the system provides the denial history and payer communication trail for manual escalation.
The bottom line
Denial resolution isn't hard. It's tedious. Each denial follows a pattern: read the CARC, identify the error, fix it, resubmit. AI does this in seconds, with higher accuracy than manual rework. The 65% of denied claims that currently go unworked represent real revenue sitting on the table. Automated denial resolution picks it up.
See AI denial resolution work on a real denied claim. Book a demo at /demo.
Related reading
Read more: /blog/top-10-claim-denial-reasons
Read more: /blog/ai-medical-coding-accuracy
See how this works in the product: /product/suggest