Machine learning trained on payer rules and denial patterns, with an AAPC-certified coding specialist reviewing every high-stakes decision.
Every claim runs the same path. AI does the repetitive checks; an AAPC-certified coding specialist makes the judgment calls.
Real-time eligibility & benefits checks stop front-end rejections before the visit.
AI validates ICD-10 and CPT codes, modifiers and payer rules on every claim.
Machine learning flags high-risk claims and auto-corrects them before submission.
Clean claims submit same-day; AI chases AR by recovery probability first.
A single claim, from the moment it's created to the moment it's paid.
Most billing companies say "AI-powered" and stop there. Here's what's actually running, in plain language.
A maintained database of payer-specific edits: every CPT/ICD pairing, modifier requirement, frequency limit and medical-necessity rule, per payer. This is not AI. It's a lookup, and it catches the majority of errors.
deterministic · no guessingA classifier trained on historical claim outcomes. It scores each claim's likelihood of denial based on features the rules engine can't see: payer behaviour patterns, claim shape, documentation signals, prior denials for similar claims.
machine learning · probabilisticUsed for one job: reading clinical documentation and flagging whether it supports the level of service billed. An LLM is good at "does this note justify a 99214?" It is not allowed to choose the code.
assistive only · never finalEvery open claim is scored on value × recovery probability. Your staff hours go to the claims most likely to actually pay, instead of working the oldest first.
expected-value rankingAnything ambiguous, high-dollar, or flagged-but-unresolved goes to an AAPC-certified coding specialist. Every appeal is written by a person. No claim leaves Sterling without a human accountable for it.
the final gateEvery denial that does get through is root-caused and fed back in. The rules table gets updated; the model gets retrained. The system gets measurably better at your payers over time.
compounds monthlyIt does not diagnose. It does not decide medical necessity. It does not send an appeal without a person reading it. And it does not touch your PHI outside an encrypted, access-logged environment covered by our BAA. Anyone promising more than that is overselling.
Every claim checked against coding rules, modifiers and payer-specific requirements before submission.
Models trained on payer behavior flag high-risk claims before they are ever sent.
Common errors (modifiers, eligibility mismatches, coding conflicts) fixed automatically.
Accounts receivable worked by recovery probability, not just by age.
Collections, denials, AR aging and payer mix, visible any time, not just monthly.
Works inside 20+ EHR, EMR and PMS systems, plus every major clearinghouse.
We're specific about this, because "AI-powered" means nothing if nobody tells you what the AI is actually allowed to do.
No claim leaves Sterling without a human being accountable for it.
Using AI on healthcare data raises fair questions. Here's our answer: PHI is encrypted, access is role-restricted and logged, and our team is HIPAA-certified.
We are also transparent about where PHI is processed. See our HIPAA Compliance page for the full picture.
Eligibility verification (EV), benefits verification (BV), prior authorizations, and denial management at no cost for your first 30 days.
No setup fee. No long-term contract.
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Eligibility verification (EV), benefits verification (BV), prior authorizations, and denial management at no cost for your first 30 days.
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