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34% denial drop · 0.91 AUC · payer-rule library refreshed continuously

Denial prediction. From CARC patterns to dollars.

Most denial management is denial recovery. The claim went out, the denial came back, the rework queue grew, and somebody booked the recovery as a win. The dollar that never got denied is the dollar nobody high-fives. Denial prediction inverts the workflow. The model scores every claim before submission against millions of historical denials by payer, by CPT, by region. The high-risk ones route to a scrubber specialist before the clearinghouse touches them. The rest flow through. Denials drop. Recovery becomes the smaller queue.

Get a free denial audit → See top 20 CARC patterns
Trained on 4M+ scrubbed claim lines Payer-rule library refreshed continuously Top 10 percent flagged at 73% precision
Top 20 CARC patterns

The denials the model is strongest on.

CARC (Claim Adjustment Reason Code) patterns where the model has high prevention rates. Prevention rate is the percentage of would-be denials in this CARC bucket that the model flagged in time for the scrubber to fix the claim before submission. Measured across the production book on a rolling 90-day window. The list is ordered by dollar-weighted volume across our client base, not by ease of prevention.

Code
CARC reason
Prevention rate
Pct
CARC 16
Claim lacks information. Modifier-missing subset is the largest sub-pattern.
81%
CARC 197
Precertification missing or invalid. Caught from prior-auth rule library.
76%
CARC 109
Claim not covered by this payer. Caught from COB priority on eligibility log.
72%
CARC 11
Diagnosis inconsistent with procedure. Pattern by CPT-to-ICD pairing.
68%
CARC 96
Non-covered charge. Caught from payer benefit category lookup.
65%
CARC 18
Duplicate claim or service. Pattern by claim history within 90 days.
63%
CARC 151
Payment denied because supporting documentation is missing. Caught from required-attachment library.
61%
CARC 4
Procedure code inconsistent with modifier. Pattern by CPT-modifier-payer triple.
59%
CARC 22
Care may be covered by another payer per COB. Caught from eligibility-log COB rank.
58%
CARC 29
Time limit for filing has expired. Caught from per-payer timely-filing window.
55%
CARC 27
Expenses incurred after coverage terminated. Caught from eligibility-log date span.
54%
CARC 119
Benefit max for the period reached. Caught from utilization log per patient.
50%
CARC 31
Patient cannot be identified as our insured. Caught from member-ID format check.
48%
CARC 234
Procedure not paid separately. Caught from bundling rule library.
46%
CARC 50
Non-covered services because medically unnecessary. Pattern by LCD-NCD lookup.
42%
CARC 24
Charges covered under a capitation agreement. Caught from payer cap-arrangement flag.
39%
CARC 252
An attachment is required to adjudicate. Caught from attachment requirement library.
37%
CARC 45
Charges exceed fee schedule. Caught from fee-schedule lookup at scrub.
34%
CARC 204
Service is not covered under the current benefit plan. Caught from benefit-plan lookup.
31%
CARC 6
Procedure or revenue code inconsistent with the age or sex. Pattern by patient demographics.
28%
Pattern AI vs clearinghouse edits

The edits do not catch what the patterns were never told.

Clearinghouse edits are the right tool for syntactic checks (the date format is wrong, the modifier is invalid, the NPI is malformed). They are the wrong tool for pattern denials (this payer started rejecting this CPT with this modifier on patients of this age three weeks ago). The two should run together, not as substitutes.

Clearinghouse edits

The rule library

Deterministic rules expressed against syntactic claim properties. When a rule is added, claims that match the rule are flagged. When a rule is not added, matching claims pass through and get denied.

  • Deterministic. The rule says yes or no, no probability.
  • Vendor-released. New rules ship on vendor quarterly schedule.
  • Syntactic. Catches malformed fields, missing required values, invalid code values.
  • Reactive. The vendor releases a new rule after a denial pattern is widespread enough.
  • Limited context. Cannot reason about payer-CPT-modifier combinations that are valid in isolation.
Pattern AI

The model

Statistical model trained on millions of historical denials. Continuously updated as new denial patterns appear in the 835 stream. Scores claims for denial probability with payer-aware context.

  • Statistical. Probability scored, threshold tuned per workflow.
  • Continuous. New patterns detected from the 835 stream within days.
  • Contextual. Reasons about payer-CPT-modifier-demographics combinations together.
  • Proactive. Catches denials the rule library has not been told about yet.
  • Auditable. Every flag carries the pattern, the payer rule (when known), and the suggested fix.
Where prediction shows up

Six places denial prediction shows up in the workflow.

The model is not a tab on a dashboard. It is wired into six operational moments in the revenue cycle. Each appearance has a measurable downstream metric that the SLA is written on. None of the six are demo-only.

Appearance 01

Pre-submit scrubber routing

Every claim leaving the scrubber gets a denial risk score. Top decile routes to a specialist for review before submission. Suggested fix is presented with the payer rule that triggered the flag. Specialist throughput is roughly 4 minutes per flagged claim.

−34% first-pass denial rate · 73% precision at top decile
Appearance 02

Coder feedback at the keystroke

When the coder enters a CPT-modifier combination known to denial-cluster on the patient's payer, the coder sees the warning before saving. The warning includes the historical denial rate, the suggested alternative, and the source rule.

In-EHR or coding-tool overlay · zero additional clicks
Appearance 03

Pre-visit eligibility cross-check

Eligibility AI verifies coverage; denial prediction overlays known denial patterns for the patient's payer-plan combination. Front-desk staff are alerted to required precerts, attachment requirements, and benefit-period limits before the visit.

Front-desk alerts on 14% of visits · prevents downstream CARC 197
Appearance 04

Appeals queue prioritization

When a denial does happen, the same model scores appeal probability. High-overturn-probability appeals route to an appeals specialist with the predicted argument and source-of-truth citations. The queue is prioritized by overturn probability times dollar value.

78% median overturn rate on prioritized appeals
Appearance 05

Payer-rule cluster detection

The 835 stream is monitored continuously for new denial clusters. When a cluster appears (same payer, same CARC, same CPT-modifier combination, new this week), the rule library is updated within three days and the prediction threshold for affected claims adjusts.

3-day new-rule-to-routed-action SLA
Appearance 06

CFO denial dashboard by dollar

The denial dashboard reports the dollar impact, not just the percentage. Denials by payer ranked by dollar weight. Top CARC codes ranked by dollar weight. The CFO sees what the denial volume is costing in cash terms, not in claim counts.

Daily refresh · dollar-weighted ranking · payer drill-down
Common questions

Frequently asked questions: denial prediction.

What is denial prediction and how is it different from clearinghouse edits?
Denial prediction is the model scoring every claim for the probability of denial before the claim leaves your scrubber, using patterns trained on millions of historical denials by payer, by CPT, by region, by site of service. Clearinghouse edits are deterministic rules that catch syntactic and obvious-rule violations. The edits catch what the rules say. The model catches what the data says. CARC 16 missing-information denials triggered by a payer who started requiring a specific modifier on a specific CPT three weeks ago do not have a clearinghouse edit. The pattern model catches them.
How accurate is denial prediction?
On the production book, AUC measured on a rolling 90-day window is 0.91 for first-pass denial risk. Precision at the high-risk threshold (top 10 percent of claims by risk score) is 73 percent. The high-risk threshold is the operational floor: claims above it are routed to a scrubber specialist for review before submission. The remaining 90 percent of claims flow through unchanged. The first-pass denial drop across our book has been measured at 34 percent.
Which CARC patterns does the model catch best?
The model is strongest on patterns where the denial reason is correlated with claim-line features the model can see. CARC 16 (missing information): 81 percent prevention on the modifier-missing subset. CARC 197 (precertification missing): 76 percent prevention because the model knows the prior-auth requirement from the payer rule library. CARC 109 (claim not covered by this payer): 72 percent prevention because the model knows COB priority from the eligibility log. The model is weaker on patterns driven by post-submission events (medical-necessity reviews, retrospective audit).
What happens when the model flags a high-risk claim?
The claim routes to a scrubber specialist with the predicted denial reason, the payer rule that triggered the flag, and the suggested fix. The specialist reviews, applies the fix (add the modifier, correct the place of service, attach the precert reference), and releases the claim. Throughput on flagged claims is roughly 4 minutes per claim because the work is presented as Apply this fix, not Find the problem. The specialist is doing the part of the job their training is for.
Why pattern AI beats clearinghouse edits?
Three reasons. First, the edits are deterministic; the patterns are statistical. The patterns catch denials the rule library does not know about yet. Second, the edits are reactive to vendor releases; the patterns are continuous. When a payer changes a rule, the model sees the new denial cluster within days. Third, the edits are syntactic; the patterns are contextual. A claim that passes every edit can still be denied because the combination of payer, CPT, modifier, and place of service is wrong in that payer's current rule set.
What about appeals?
Denial prediction is upstream of appeals; the goal is prevent the denial, not appeal it after. When a denial does happen, the same model scores appeal probability. High-probability appeals route to an appeals specialist with the predicted argument and source-of-truth citations. Median appeal overturn rate on our book is 78 percent because the appeals queue is prioritized by overturn probability and dollar value, not by days outstanding.
How does the model handle a new payer rule?
Three days from new-rule detection to routed scrubber action. Day one: the model sees the denial cluster appear in the 835 stream. Day two: pattern recognition labels the cluster with the suspected new rule and the payer. Day three: the rule is added to the prediction library, the high-risk threshold for the affected CPT-by-payer combination updates, and claims at risk in the production scrubber start routing. The payer-rule library is refreshed continuously, not quarterly.
Can we see the model on our own data before we sign?
Yes. The free 30-day denial audit runs the model on a 90-day claim sample under a same-day BAA. The output is a four-page written audit covering measured first-pass denial rate, top 20 CARC patterns with prevention projections, top 10 payers with denial dollars at risk, and a tailored implementation roadmap with month-by-month projected denial drop. Useful whether or not you hire us.

Run the model on your denials. See the dollars.

Free 30-day denial audit on a 90-day claim sample under a same-day BAA. The four-page written report covers your top 20 CARC patterns, top 10 payers ranked by dollar weight of denials, projected first-pass denial drop, and the implementation roadmap. A senior partner on the call.

Get my free denial audit See the AI suite