Where AI Helps With Claim Denials, and Where It Doesn't

AI helps most with claim denials before a claim goes out and right after a denial comes back. Before submission, it can check eligibility, authorizations, and registration data, which are the top causes of denials. After a denial, it can read the payer's reason codes, sort denials by cause, track appeal deadlines, and draft appeals for review. It helps least where judgment lives: medical necessity arguments, coding decisions, and payer escalations.

How common are claim denials?

Common enough to be a line item. Hospitals surveyed by Premier reported denial rates of nearly 15% on 2023 claims, as high as 49% in some cases, and Premier estimates providers spent $25.7 billion on fighting denied claims that year, up 23%. On HealthCare.gov plans, KFF found insurers denied 19% of in-network claims in 2024. And in a 2024 MGMA poll, 60% of medical group leaders said their denial rates had risen over the previous year.

Most denials are worth fighting. Premier found 68.6% of denied claims were eventually paid, and Optum's 2024 Denials Index found 84% of denials potentially avoidable. The waste is in the rework.

Why do claims get denied?

Mostly for administrative reasons, not clinical ones. In Experian Health's 2025 survey, a claims software vendor's annual study, the top causes were:

KFF's data points the same way: only 5% of HealthCare.gov denials were for lack of medical necessity, while 25% were administrative and 9% were for missing prior authorization or referral. Premier also found that 10.4% of denied claims had been approved through prior authorization first.

Where can AI prevent denials before submission?

At the front of the revenue cycle, where most of the causes live:

• Eligibility. An automated check before every visit catches inactive coverage and wrong plans, the way eligibility verification works when it runs on the schedule.
• Authorizations. AI can flag services that need prior authorization and assemble the request, covered in AI for prior authorization.
• Registration and claim data. Missing subscriber IDs, mismatched names, and blank fields can be caught before the claim leaves.
• Documentation. Letters of medical necessity can be drafted from the chart for a clinician to review. RadiusDocs, a platform Cream Digital built, generates medical necessity letters and prior authorizations from uploaded records, described in the RadiusDocs case study.

Any system that reads claims and charts is handling patient data, so ask the vendor the right questions before it starts.

Where does AI help after a denial?

In reading, sorting, and tracking, which is most of the work:

1. Reading the reason. Payers explain adjustments with Claim Adjustment Reason Codes and Remittance Advice Remark Codes (CARC and RARC), delivered on the electronic remittance advice, the X12 835. AI can turn those codes into a plain cause and a next step.
2. Sorting by cause. Grouping denials by payer and reason shows which fixes would prevent the most.
3. Tracking deadlines. Medicare claims must be filed within a year of service, and a redetermination is due within 120 days of the initial decision, the first of five appeal levels. Commercial plans set their own limits.
4. Drafting the appeal. Pulling the denial, the records, and the payer's policy into a first draft.

Where should a person stay in charge?

Wherever the answer needs judgment or accountability: medical necessity appeals, coding decisions, write-off approvals, and escalations with a payer. Experian found that 90% of denials are already reworked with at least some human review, and that is the right instinct. The AI prepares the case and a person decides, the pattern described in human-in-the-loop handoffs.

What about payers using AI to deny claims?

Regulators are drawing lines. CMS requires Medicare Advantage plans to base medical necessity decisions on the patient's own history, physician recommendations, and clinical notes, and has said an algorithm that decides from a larger data set instead does not comply. California's Health and Safety Code says a health plan's AI tools may not deny, delay, or modify care based on medical necessity, a decision reserved for a licensed physician or other qualified professional. And since 2026, CMS-0057-F requires affected payers to give a specific reason when they deny a prior authorization, which makes the reason easier to act on.

How should a practice measure it?

Five numbers, tracked monthly: first-pass acceptance rate, denial rate by cause, days from denial to resubmission, appeal success rate, and dollars written off. If AI is working, the first number rises and the last one falls. For how Cream Digital approaches this work, see back office operations.