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80+ CARC patterns · 34% first-pass drop · sub-200ms P95

Pre-submit denial scoring.

Every claim line scored before submission. The ensemble runs a gradient-boosted classifier, an 80-pattern CARC matcher, and a payer-specific historical lookup in parallel. Auto-correct on deterministic patterns, route to specialist on judgment patterns, score-and-log on the rest. 34 percent drop in first-pass denials on the active book, with a root-cause loop back to coding and eligibility so the same denial does not happen twice.

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Ensemble: ML classifier + rule matcher + payer history Quarterly retraining Root-cause loop to coding and eligibility

The patterns

Top CARC patterns. Prevention rate.

The 80 patterns we track account for the bulk of denied-dollar volume on our active book. The chart on the right shows the eight that drive the most denied dollars and the prevention rate the engine achieves on each. Prevention is the percentage of pre-submission flags where auto-correct or specialist routing successfully prevented the denial.

CO-16 missing or invalid information is the biggest single-line item and the easiest to auto-correct because the missing data is almost always structurally locatable. CO-50 not medically necessary is the hardest, because the fix often requires clinical documentation the engine cannot generate; that one routes to specialist with a documentation request.

Top CARC patterns by denied-dollar volume · prevention rate

CO-16

Missing or invalid informationModifier, NDC, referring NPI, POS

94prevent

CO-97

Included in another procedureBundling, NCCI edits, modifier 25/59

87prevent

CO-18

Duplicate claim or serviceSame-DOS resubmit, sibling claim

96prevent

CO-109

Not covered by this payerWrong payer routing, COB miss

81prevent

CO-204

Not covered under benefitPlan-specific exclusion, riders

72prevent

CO-50

Not medically necessaryLCD/NCD miss, documentation gap

58prevent

PR-1

Deductible amountPre-collection at registration

88surface

CO-29

Timely filing limitAged claim auto-escalation

91prevent

Prevention = auto-corrected or routed before submission · 80 patterns total in library

What the engine returns

Risk score. Predicted reason. Source data.

Every scored line returns four things: the headline risk score, the predicted CARC reason if denied, the rule that fired with its supporting source data, and the recommended action. Auto-correct, route to specialist, or submit and log. Sub-200ms P95 so the score does not bottleneck claim throughput.

Denial Prediction · Claim Line 2026-06-19-04 · CPT 73721 · Anthem BCBS

Action · Route to specialist

Predicted CARC

CO-50 Not medically necessary · Anthem LCD L34777 requires conservative-treatment documentation prior to MRI knee

Rule fired

PATTERN-CO50-MRIKNEE-V3 Pattern matcher · payer Anthem · CPT 73721 · LCD lookup miss

Source data

Documentation parse No conservative-treatment notes (PT, NSAID, injection trial) found in last 60 days · prior auth on file but LCD evidence missing

Recommended action

Specialist + clinician nudge Documentation request to ordering provider before submission · auto-correct not safe

The capabilities

Six capabilities. Pre-submit gate.

Each capability runs at the pre-submission gate in the same request as claim finalization. The ensemble combines three signal sources; the action layer decides what happens next; the loop ensures fixes compound over quarters rather than plateau.

01 · Ensemble model

Three signals, one score.

Gradient-boosted classifier for headline denial probability, deterministic 80-pattern CARC matcher (the fastest path with rule-level traceability), and payer-specific historical lookup against active-book denial history. Scores combine into the headline risk. Each signal carries its own confidence so the action layer can decide.

02 · CARC pattern recognition

80 patterns, payer-specific.

The patterns that actually move the number, not every code in the X12 CARC list. Each pattern carries payer-specific overturn probability, recommended action, and the documentation snippet showing why it fired. Library refreshes quarterly with model retraining and surfacing of new patterns from missed denials.

03 · Auto-correct

Safe patterns only, audited.

Auto-correct fires only on deterministic patterns where the fix is unambiguous: missing modifier 25 the documentation supports, missing NDC on a J-code, referring NPI lookup for Medicare, POS code mismatch with rendering location. Every auto-correct writes an audit-trail entry. Judgment patterns never auto-correct; they route.

04 · Specialist routing

Dollar-value triage, by pattern.

Below auto-correct threshold or judgment-required patterns route to a specialist queue keyed by pattern, payer, and dollar value. Specialists see the proposed correction, the rule that fired, and the documentation snippet. Dollar-prioritized so high-value claims get attention first; same audit trail per intervention.

05 · Quarterly retraining

Model freshness, monitored.

Gradient-boosted classifier retrains quarterly on fresh denied and paid claims by payer, by service category, by specialty. 80-pattern library updates against new patterns from missed denials. Payer-specific historical lookup refreshes monthly. Cadence balances freshness against distribution shift between training and production.

06 · Root-cause loop

Fix upstream, compound the gain.

When a denial posts after submission, the engine records the actual CARC against the original risk score. If the failure traces upstream to coding error, eligibility miss, or credentialing gap, the root cause feeds back to that engine. The loop is what makes the 34 percent number compound over quarters rather than plateau at a one-time gain.

How a line runs

Four steps. Sub-200ms.

From coded line to submitted claim. The engine runs at the submit gate; the action layer decides; the loop closes after the 835 posts. Below is the workflow per claim line.

Step 01

Score the line.

Coded line arrives at the submit gate with full context (CPT, dx, modifiers, payer, provider, place of service, dollar value). Ensemble fires: gradient-boosted classifier, pattern matcher, payer historical lookup. Headline risk score returns under 200ms at P95.

Step 02

Decide the action.

Risk score and predicted CARC feed the action layer. Auto-correct if the pattern is deterministic and the fix is unambiguous. Route to specialist if pattern flagged but judgment required. Submit-and-log if risk under threshold. Every decision logged.

Step 03

Apply the fix.

Auto-correct rewrites the line with the recommended fix and an audit-trail entry showing original, corrected, and rule. Specialist routing surfaces the line with the proposed correction and source documentation. Submit-and-log proceeds without intervention but logs risk for post-mortem.

Step 04

Close the loop.

When the 835 posts, engine records actual outcome against original risk score. Missed denial surfaces for library refresh. Upstream root cause (coding, eligibility, credentialing) feeds back to that engine. Quarterly retraining absorbs the new signal.

What clients see

Measured outcomes from denial prediction.

Across our active book. Anonymized; individual results depend on specialty mix, payer mix, and pre-engagement baseline. Numbers below are honest measurement.

34%

First-pass denial drop

Drop in first-pass denial rate against pre-engagement baseline within the first two quarters. Sources: 40 percent of the drop from auto-correct of known patterns, 35 percent from specialist routing on judgment patterns, 25 percent from the root-cause loop feeding fixes back into coding and eligibility.

94%

CO-16 prevention rate

Pre-submission prevention rate on CO-16 missing or invalid information, the biggest single-line CARC by denied-dollar volume. Auto-correct handles most because the missing data (modifier, NDC, referring NPI, POS code) is structurally locatable. Same engine, applied deterministically.

<200ms

P95 scoring latency

95th-percentile latency to score a line through the ensemble. Required because the engine runs at the pre-submission gate in the same request as claim finalization. Slow scoring would bottleneck submission throughput. Fast scoring lets every line get scored without breaking the pipeline.

Common questions

Frequently asked: denial prediction.

What does the denial-prediction engine actually do?

It scores every claim line before submission for the probability of denial, the most likely CARC reason if denied, and the recommended action. Three actions are possible: auto-correct (the engine knows the fix and applies it), route to specialist (the engine knows the pattern but the fix needs human judgment), or submit as-is with the risk score logged. The 80-plus CARC patterns we track are the patterns that actually move the number, not every code in the X12 CARC list.

Is this a machine-learning model or a rule engine?

Both, by design. The ensemble runs three components in parallel: a gradient-boosted classifier for the headline denial probability, a pattern matcher against the 80-plus CARC pattern library (deterministic rules, the fastest path), and a payer-specific historical lookup against our active-book denial history. The three scores combine into the headline risk score. The pattern matcher carries the rule that fired so the recommendation is traceable, which is what the coding and AR teams need.

What is the 34 percent first-pass denial drop?

Across our active book, first-pass claim denial rate dropped 34 percent against the pre-engagement baseline within the first two quarters of operation. Sources: roughly 40 percent of the drop comes from auto-correct of known patterns (modifier sequencing, NDC mismatches, missing referring NPI on Medicare claims, etc), 35 percent from specialist routing on patterns the engine flags but cannot auto-fix, and 25 percent from the root-cause loop feeding fixes back into the coding and eligibility engines.

What CARC patterns get tracked?

Roughly 80 patterns that account for the bulk of denied-dollar volume. Top patterns include CO-16 missing or invalid information, CO-97 service included in another procedure, CO-18 duplicate claim or service, CO-50 not medically necessary, CO-109 not covered, CO-204 not covered under patient benefit, and PR-1 deductible amount. Each pattern carries a payer-specific overturn probability, a recommended action, and a documentation snippet showing why it fired. The library refreshes quarterly with model retraining.

What is sub-200ms P95 scoring?

The 95th-percentile latency to score a single claim line through the ensemble (gradient-boosted classifier, pattern matcher, payer historical lookup) is under 200 milliseconds. That matters because the engine runs at the pre-submission gate in the same request as claim finalization, not in a nightly batch. Slow scoring would push back claim throughput. Fast scoring lets every line get scored without bottlenecking submission.

How does auto-correct work safely?

Auto-correct only fires on deterministic patterns where the fix is unambiguous: a missing modifier 25 that the documentation supports, a missing NDC on a J-code, a referring NPI lookup for Medicare claims, a place-of-service code mismatch with the rendering location. Each auto-correct writes an audit-trail entry showing what was changed, the rule that fired, the original value, and the corrected value. Patterns requiring judgment never auto-correct; they route to specialist.

What is the root-cause loop?

When a denial actually posts after submission, the engine records the CARC, the original risk score, and the actual outcome. If the model under-predicted (we did not catch a pattern we should have), the pattern gets surfaced for inclusion in the next library refresh. If the failure traces upstream (coding error, eligibility miss, credentialing gap), the root cause feeds back to that engine so the same denial does not happen again. The loop is what makes the 34 percent number compound over quarters rather than plateau.

How often does the model retrain?

Quarterly retraining on the active-book claim history. The gradient-boosted classifier sees fresh denied and paid claims by payer, by service category, and by specialty. The 80-pattern CARC library updates against new patterns surfaced from missed denials. Payer-specific historical lookup tables refresh monthly. The quarterly cadence balances model freshness against the cost of distribution shift between training and production.

Pairs with

Where the prediction engine compounds.

The engine reaches further when paired with the rest of the stack. The root-cause loop is what makes the gains stack.

Coding engine

Upstream fix on coding-rooted denials.

When a denial traces to a coding error, the root cause feeds back to the coding rule library so the same denial does not recur. Three-way match and HCC V28 capture flow from the same audit-trail design.

Upstream fix on eligibility-rooted denials.

When a denial traces to an eligibility miss, mid-cycle change detection picks up the pattern for future visits. Same payer rule library, same sweep cadence. Drives the biggest single source of preventable denials on FQHC books.

Upstream fix on credentialing-rooted denials.

When a denial traces to a provider-payer enrollment gap, CredPro v6 surfaces the gap and the recredentialing timeline. RBAC and audit trail flow from the same design as the prediction engine.

Send 30 days of denials. We send back the prevention map.

A free 30-day denial audit. Drop your last 30 days of 835 remits and denied claims. We return a four-page audit covering CARC distribution by payer, denied-dollar volume per pattern, prevention rate the engine would have achieved, root-cause attribution to coding, eligibility, and credentialing, and a 90-day fix plan. An ASP-RCM senior partner on the call.

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