PHI-Free AI · Denial Prediction

The AI that predicts denials before you submit the claim

ClearClaim scores every claim against payer policies, NCD/LCD, billing rules, and your own denial history — naming the CARC, the dollars at risk, and the fix — in seconds, before it ever reaches the clearinghouse. PHI-free by design.

+1 (630) 686-8684 · Mon–Fri 9:00–18:00
0%
Denial prediction accuracy
>0%
Denial rate reduction in months
0K+
Claims processed
5–0%
Revenue lift
ClearClaim · Pre-Billing AuditLIVE
RULES LIBRARIES: PAYER · NCD/LCD · GOV · CUSTOMAVG <10s/CLAIM
About AiClaim

Built on one conviction: denials are predictable — so they're preventable

AiClaim pairs 25 years of revenue cycle operations with a denial prediction engine validated on real payer outcomes. 150+ healthcare institutions use us to move denial management from the back end of the revenue cycle to the front — before submission, where it's cheapest to win.

  • Denial prediction at the core — every other capability feeds it or learns from it
  • PHI-free, API-first, compatible with any EMR/PM or clearinghouse setup
  • Biller-in-the-loop design: your team's judgment trains your model
  • In-house software with 24/7 catered support services
More About Us →
★ Nationally recognized outcomes
Denial prediction accuracy99%
Denial rate reduction>50%
Revenue lift delivered5–10%
Avg claim review time<10s
Healthcare institutions150+
ClearClaim — follow one claim through AiClaim
STAGE 01 — INTAKE

The claim arrives. Any way you send it.

Claims flow in through API, an RPA bot watching your PM system, or a simple 837/report export — no IT lift required. HCFA 1500 professional claims and UB-04 institutional claims (DRG, rev codes, value codes and all) are both first-class citizens, reviewed in seconds at enterprise volume.

STAGE 02 — DE-IDENTIFY

PHI comes off. Everything that matters stays.

Other than eligibility, patient identity has no effect on adjudication — so we strip PHI before analysis. No BAA-heavy data exchange, no PHI sitting in another vendor's cloud. We keep the member ID prefix, patient age, and payer details, plus the full clinical and billing picture:

  • Rendering & billing provider, NPI, taxonomy, POS
  • CPT/HCPCS, modifiers, units, ICD-10, DRG, rev codes
  • Payer, plan type, and policy context
STAGE 03 — SCRUB

Audited against every rule that matters.

Four rule libraries, applied transparently: payer medical & reimbursement policies, government guidelines (NCD & LCD), governing billing rules — CCI/PTP edits, MUE units, timely filing, POS validity, ICD-10 specificity — and custom rules built from your own denial history. Every rule shows pass or fail. No black box.

STAGE 04 — SCORE

A denial risk score, with the dollars attached.

Every claim is marked High, Medium, or Low risk with the predicted CARC/RARC codes, the exact dollars at risk, and recommended fixes — before it ever leaves for the clearinghouse. High-risk claims drop into a flagged worklist, prioritized by dollar value, so your team works what matters first.

STAGE 05 — BILLER REVIEW

Your billers stay in control. The AI learns from them.

Billers agree or disagree with every finding and annotate why — contract effective dates, payer quirks, auth on file. Every annotation becomes validation data that trains the model on your book of business, not someone else's. Accuracy compounds with every claim your team touches.

STAGE 06 — FIX & SHIP

Clean claim out the door. First pass.

Approved fixes can be applied by hand — or by the optional fix bot, which logs into your EMR/PM (eClinicalWorks, Athena, and more), makes the biller-approved corrections itself, and logs every action for audit. The claim ships clean, your first-pass yield climbs, and the denial never happens.

CLM-2026-48211HCFA 1500 · PROFESSIONAL
Patient nameJOHNSON, MARCUS T
DOB / SSN04/12/1981 · ***-**-4471
Address2214 PEACHTREE RD NE, ATLANTA GA
Member prefixXQHKEPT
Patient age45KEPT
Payer / planANTHEM BCBS · PPOKEPT
Provider / POSDR. K. RAO, NPI 1457·· · POS 11
CPT + Mod99214-25, 20610-RT
ICD-10M17.11, M25.561
Pre-billing audit · 4 rule libraries11 OF 214 SHOWN
Eligibility — active coverage on DOSPAYER
Prior auth — not required for CPT setPAYER
NCD 150.3 — medical necessity metNCD
LCD L33912 — covered dx pairingLCD
CCI / PTP edits — no bundling conflictsCMS
MUE — units within limitsCMS
POS 11 valid for CPT 99214CMS
Modifier 25 — E/M same-day documentation riskPAYER
Custom rule #47 — Dr. Rao not contracted w/ this planCUSTOM
Timely filing — day 12 of 90PAYER
ICD-10 specificity — laterality presentGOV
Denial risk summarySCORED IN 6.2s
87RISK SCORE
HIGH RISK OF DENIAL

2 of 214 rules failed. Payer history: Anthem denied 78% of modifier-25 claims from this practice in the last 90 days. Routed to flagged worklist.

CO-97 BUNDLED E/M CO-242 NON-CONTRACTED · RARC N570 $486.20 AT RISK
FIX 1Attach same-day E/M documentation or remove modifier 25 from 99214.
FIX 2Reassign rendering provider to a contracted NPI for Anthem PPO.
Biller review · annotationREVIEWED BY J. MARTINEZ
Finding 1 — Modifier 25 documentation risk✓ AGREE
Note: Separate E/M not clearly documented. Will query provider and attach addendum before release.
Finding 2 — Non-contracted provider✎ DISAGREE
Note: Dr. Rao's Anthem PPO contract went effective 06/01 — credentialing portal not yet updated. Keep provider as billed.
ANNOTATIONS CAPTURED → MODEL VALIDATION DATA · CUSTOM RULE #47 QUEUED FOR EFFECTIVE-DATE UPDATE

Claim corrected & cleared

Documentation attached; provider kept per biller note. Fix bot applied the update in the PM system — every action logged. Re-scored: risk 4 — LOW.

→ RELEASED TO CLEARINGHOUSE · FIRST-PASS CLEAN
The closed loop

It doesn't just predict denials. It learns from every single one.

After the payer adjudicates, the 835 remittance flows back into AiClaim — and the system grades its own homework.

835 IN

Validate every prediction

Each 835 remittance is matched back to the original prediction. Paid, denied, adjusted — the AI verifies what it got right and what it missed.

LEARN

Missed denial? New rule.

Valid denials the model missed are analyzed by CARC and remark code, and a custom rule is written automatically — so that denial never slips through again.

DEFEND
§

Wrong payer denial? Auto-appeal.

When the AI validates that a payer denied incorrectly, it generates the appeal automatically — and protects your rule set from learning the payer's mistake.

YOUR RULES
Aa

Rules in plain English

Your team writes rules in layman's terms — "Dr. Patel isn't contracted with Aetna" — and the AI translates them into the pre-billing audit instantly.

EVERY ADJUDICATION MAKES THE NEXT PREDICTION SHARPER
You type it like you'd say it
AiClaim turns it into an audit rule
Added to pre-billing audit
The flagship — ClearClaim denial prediction

Stop working denials. Start predicting them.

Every denial you appeal is revenue you already earned, paid for twice. ClearClaim moves the entire fight to before submission — predicting the denial, naming the CARC, pricing the risk, and fixing the claim while it can still ship clean.

0%
AI prediction accuracy, validated against real 835 outcomes
>0%
Denial rate reduction within the first few months
0K+
Claims processed through the prediction engine
<10s
Per claim — at enterprise volume, day one
Predict

Pre-submission risk scoring

Every claim — HCFA 1500 or UB-04 — is scored High, Medium, or Low against 4 rule libraries: payer policies, NCD/LCD, governing billing rules (CCI, MUE, timely filing), and your own custom rules.

Quantify

Predicted CARCs & dollars at risk

Not just "risky" — you see the exact CARC/RARC codes the payer will use and the dollar amount on the line, so worklists prioritize by revenue impact, not guesswork.

Prevent

Flagged worklist + fix bot

High-risk claims drop into a pre-bill worklist with recommended fixes. The optional fix bot applies biller-approved corrections directly in your EMR/PM — every action logged.

Protect

PHI-free architecture

Patient identity is stripped before analysis — no PHI leaves your environment, no heavy BAA data exchange. Adjudication-relevant data stays; risk doesn't.

Learn

835 closed-loop validation

Every remittance grades the model. Missed denials become new custom rules automatically; incorrect payer denials trigger evidence-backed auto-appeals instead.

Launch

6-month lookback onboarding

We analyze six months of your submitted claims and payment data before go-live — so your custom rule set and payer behavior profile exist on day one, not month six.

Industries served

Built for both sides of the revenue cycle

"AiClaim is not here to create friction — we are here to bring transparency and efficiency to both sides of the revenue cycle. Better data = better outcomes."

KPIs & analytics

Every KPI your revenue cycle runs on, in one dashboard

Customized to each client — not a canned report. ClearClaim dashboards give billers their worklists and give leadership the numbers that matter: how much revenue is coming, where denials are coming from, and exactly how well the model is performing on your book of business.

Expected reimbursements by code Claim volume & throughput Predicted vs. annotated denials Denials caught pre-bill Top denial reasons (CARC) Highest-risk providers & clinics Denial types & trends Model accuracy · precision · recall Payable vs. non-payable amounts Denial rate over time
Claims processed (90d)
41,208
▲ 12% vs prior period
Expected reimbursement
$3.84M
payable vs non-payable split live
Denials caught pre-bill
1,872
$212K protected this quarter
Denial rate
4.1%
▼ from 9.7% at onboarding
Model accuracy / precision
99.1 / 98.2
validated on 835 outcomes
Model recall
97.6%
▲ improves with every annotation
Predicted vs annotated denials · weekly
Top denial reasons caught
CO-97 BUNDLED
412
CO-16 INFO
308
CO-29 TIMELY
231
CO-242 NETWORK
178
CO-151 FREQ
124
Meet our team

Led by operators who've lived the revenue cycle

Rushabh Tolia

Rushabh Tolia

Chief Executive Officer
Shrikant Pandya

Shrikant Pandya, Ph.D.

Chief Technology Officer
George Hernandez

George Hernandez

SVP of Partnerships
Testimonials

What our clients say

"
★★★★★

Aiclaim does an extremely thorough job with the billing for our clinic. They are very responsive and easy to get in touch with when any billing issues or questions arise. They are a top-notch billing company with great customer service, highly recommend.

Matthew Blanchard, M.D.
CEO · Pinnacle Urgent Care
"
★★★★★

AiClaim has been a wonderful addition to our practice. Their expert handling of billing and credentialing has streamlined our operations and accelerated reimbursements. Thanks to their attention to detail and efficient service, we can focus fully on our clients without worrying about administrative tasks. Highly recommend!

Dr. Pooja Shah
CEO · Empowered Minds Today
Denial Rate Calculator

See what clean claims are worth to you.

Estimate the reimbursements and labor you could recover every month by predicting denials before submission. Enter your own numbers — or start from a preset.

Your numbers

Denial rate, prediction accuracy, and the billing modifier are entered as decimals (e.g. 0.10 = 10%).

Calculation Results
Average Claims Denied / Month
Predicted Claims for Intervention
Total Reimbursements Saved
Total Labor Cost Saved

High-level monthly estimate based on the inputs above. Actual recovery depends on your payer mix, contract rates, and workflow.

Get a tailored ROI report →
Frequently asked questions

Questions about denial prediction

How ClearClaim predicts denials before submission, keeps your data PHI-free, and fits the workflow you already run.

What is AiClaim?+
AiClaim is a healthcare technology company. Its flagship platform, ClearClaim, uses AI to predict and prevent claim denials before a claim is ever submitted — moving denial work from post-adjudication appeals to pre-bill prevention.
How does ClearClaim predict denials?+
Every claim is scored High, Medium, or Low risk against four rule libraries: payer policies, NCD/LCD coverage rules, governing billing rules such as CCI, MUE and timely filing, and your own custom rules. For risky claims it names the predicted CARC/RARC codes and the dollars at risk so worklists prioritize by revenue impact.
What does “PHI-free” mean, and how does it affect HIPAA and BAAs?+
Patient identity is stripped before any analysis, so no PHI leaves your environment. Adjudication-relevant data stays while protected identifiers don't, which minimizes BAA-governed data exchange and reduces your compliance surface.
Does ClearClaim work for both institutional and professional claims?+
Yes. It scores both institutional claims (UB-04 / 837I) and professional claims (HCFA-1500 / 837P), so hospitals, urgent care, and specialty groups are all covered.
How does it integrate with our EMR or practice-management system?+
ClearClaim ingests claims through API, RPA, or file export, so it fits existing workflows without a rip-and-replace. An optional fix bot can apply biller-approved corrections directly in your EMR/PM, with every action logged.
What results can we expect?+
ClearClaim has processed more than 250,000 claims, with prediction accuracy independently validated against real 835 remittance outcomes and denial-rate reductions of roughly half within the first months of use. Claims are scrubbed in seconds, even at enterprise volume.
What happens when a denial still gets through?+
Every 835 remittance grades the model in a closed loop. Missed denials automatically become new custom rules, and incorrect payer denials trigger evidence-backed auto-appeals instead of manual rework.
How do we get started?+
Onboarding begins with a six-month lookback: we analyze your past claims and payment data before go-live, so your custom rule set and payer-behavior profile exist on day one. Book a demo to see your own denial risk modeled.

Your denial rate, cut in half.

Bring six months of claims and remittance data — we'll show you exactly which denials we would have predicted, what they cost you, and what your custom rule set looks like on day one.

✆ +1 (630) 686-8684  ·  demo@aiclaim.com