AI that catches denials before submission.
A denied claim costs an average of $25 to rework, takes 14 days to refile, and forgoes 9 percent of full value at takeback when finally paid. Prevention beats recovery on every axis. Our denial prediction model scores every claim before it leaves, routes high-risk claims to specialists with the predicted reason and source data, and auto-corrects the deterministic ones. The result on our active book is a 34 percent drop in first-pass denials.
The top denial codes, scored at the source.
Most denial dashboards rank by frequency. Our AI ranks by prevention probability. The top patterns to the right are the ones our model catches most reliably before submission. A clearinghouse will tell you a claim has a syntax error. Our model will tell you a CPT, DX, modifier, and payer combination has a 78 percent historical denial rate in your specific payer mix and exactly what to fix.
The catch rate climbs as more clients onboard. The model is trained continuously on pair instances across the entire active book, refreshed every 90 days, and validated against held-out data before any update ships.
The Denial Prevention AI console.
Live pre-submit scoring queue, 80-pattern catalog, CARC/RARC mapper, and payer-by-pattern heatmap. Your billers see the predicted block reason and exact fix before the claim ever leaves the building.
Six capabilities. Each one moves the denial number.
Generic AI medical billing tools score claims with a black-box probability. Our model surfaces the predicted reason, the source data driving it, the historical rate for the pattern, and the exact correction to apply. Each capability is real production code, validated by certified denial prevention specialists.
Risk score on every claim.
Ensemble model trained on three years of claim-denial pairs across our active payer mix. Outputs 0-100 denial risk score in under 200 ms P95. Score, predicted reason, source data, recommended correction. Live at the moment of submission, not after.
Deterministic fixes apply.
When the AI predicts a deterministic fix (missing modifier, wrong DX-CPT pairing, lapsed authorization, missing referring NPI), it auto-corrects and re-scores. Specialist review for judgment calls only. Every auto-correction logged with model version and confidence.
High-risk claims route to humans.
When the AI predicts a judgment call (medical necessity, documentation gap), it routes to a senior coder with the predicted CARC, source chart context, and historical pattern. The specialist sees in 10 seconds what would take 5 minutes to investigate cold.
Quarterly retraining on live data.
The model retrains every 90 days on the full claim-denial corpus across the active book. Validation runs on held-out data before any update ships. CARC patterns that disappear get pruned; new patterns get caught within one retraining cycle.
Patterns by payer, by region.
The same CPT-DX pair has a 12 percent denial rate at one regional Medicaid and 38 percent at another. Our model carries payer-specific patterns and emits payer-specific predictions. Generic models that average across payers lose this granularity entirely.
Prevention feeds CDI.
The patterns the AI surfaces feed clinician education and CDI program priorities. If the model is catching a particular medical necessity documentation gap repeatedly, the answer is upstream coaching, not infinite downstream rework. The loop closes.
From 14.7 percent to 9.7 percent. Measured.
Across rollouts past the eight-week calibration window, first-pass denial rate dropped from a baseline average of 14.7 percent to 9.7 percent on the same claim volume and payer mix. That is the 34 percent relative drop. We measure this against the prior 90-day baseline before AI deployment, not against a national benchmark, so the comparison is apples to apples for the same client.
The drop accrues from three sources roughly equally: prediction-routed denial prevention, eligibility-side catches before submission, and authorization tracking eliminating one specific common denial. The math: a practice billing $20M annually that drops denial rate from 14.7 to 9.7 percent recovers approximately $1M in run-rate cash that previously bled through rework, takebacks, and write-offs.
Four steps. Sub-second decisioning.
From the moment a claim is queued for submission to the moment it ships to the clearinghouse, here is the work the model and our specialists execute.
Score the claim.
Ensemble model emits a 0-100 risk score under 200 ms P95. Predicted CARC, source data points driving the score, historical rate for the pattern, recommended correction. All logged through the LLM Gateway.
Route by risk band.
Below 60: ship. 60 to 80: auto-correct if deterministic, route to specialist if judgment. Above 80: route to senior specialist with the predicted CARC, chart context, and historical pattern in view.
Fix or escalate.
Auto-correct applies and re-scores. Specialist review applies the correction and re-scores. Escalation routes to a coding lead. Every action logged with actor, decision, and outcome.
Learn from the outcome.
Once the 835 comes back, the actual denial status feeds the training corpus. The model retrains every 90 days on the full updated corpus, with held-out validation before deployment. CARC patterns evolve; the model evolves with them.
Measured outcomes from denial prevention AI.
Across our active book past the calibration window. Anonymized; individual results depend on payer mix, prior baseline, and how clean the claim file is at engagement start.
Frequently asked questions: denial prevention AI.
How does pre-submit denial scoring actually work?
What is the AI actually catching that humans miss?
What does the 34 percent drop number measure?
Does this work on day one?
What happens when a claim is flagged high-risk?
How is this different from a clearinghouse edit check?
What about denied claims, not just prevented ones?
Can you predict denials on our existing claim file?
Send a 90-day denial dataset. We send back the prevention map.
A free 30-day denial audit. Drop your CARC and RARC codes, payer, CPT, and line value for the last 90 days. We return a four-page written report covering your denial taxonomy, the top recoverable categories with dollar estimates, predicted prevention rate, and a 90-day fix plan. No sales deck. A senior partner on the call.