For most of the last two decades, claim scrubbing in healthcare RCM has meant one thing: rules-based engines that check submitted claims against tens of thousands of payer-specific edits before the claim leaves your practice-management system. Big clearinghouses sell those rule libraries as a core feature, and they work — for the kinds of errors that are repeatable, well-documented, and consistent across payers.
In 2026, AI is being added to two distinct parts of the workflow: (1) extracting structured data from inbound claim and EOB documents, and (2) augmenting scrubbing with pattern-based predictions on top of the rule library. These are different problems with different answers.
This post walks through where AI actually beats rules-based scrubbing — and where rules still win.
What rules-based scrubbing is good at
Rules engines win on:
- Coding compatibility checks. CPT codes that can't appear together; CPT/ICD combinations that trigger payer rejections; modifier requirements for specific procedures.
- Payer-specific format edits. Required fields per payer, billing-frequency limits, place-of-service combinations.
- NCCI bundling and MUE limits. Standardized national rule libraries that catch unbundling and units-of-service issues.
- Eligibility and authorization checks. When wired to clearinghouse eligibility APIs.
These are well-defined, deterministic problems. A correctly-configured rule library catches 80–90% of the high-frequency denial reasons. There's no reason to use AI for things rules already do well.
Where rules-based scrubbing breaks down
Two places.
1. Documents the rules engine never sees
Rules-based scrubbing runs on submitted claim data. It assumes the claim is already in your practice-management system, structured and ready to go out the door. It doesn't help you with:
- Faxed EOBs from secondary payers
- Paper CMS-1500s from referring providers
- Downloaded payer remittances that didn't come through ERA
- UB-04s with handwritten edits from facilities
For all of those, the data still has to land in your PM system somehow. That's where AI extraction earns its place — it converts the document into structured data that your existing rules engine can then process.
The mental model: AI extraction feeds the rules engine. They're not competing approaches.
2. Pattern-based denial prediction
The other place rules struggle is denial prediction — knowing in advance that this particular claim, for this particular payer, in this particular month, is likely to deny for a non-deterministic reason. Examples:
- Payers tightening medical-necessity criteria silently
- Regional Medicare Administrative Contractors (MACs) interpreting LCD policies differently
- Pre-authorization requirements that change mid-quarter
- Documentation requirements that vary by reviewer
Rules engines don't catch these because they're not in the rule library yet. The denial happens, the team figures out the new payer behavior, and the rule library gets updated — usually 30–60 days after the denials started piling up.
AI augmentation can flag claims that "look like" recently-denied patterns even before the rule is added. The right framing: AI gives you weeks of early warning on emerging payer behavior. Rules still catch the deterministic problems.
Where AI extraction wins in the document workflow
For the inbound document side of RCM — EOBs, paper claims, secondary remittances — AI extraction wins for three reasons:
1. Denial code extraction
CARC (Claim Adjustment Reason Codes) and RARC (Remittance Advice Remark Codes) are the structured fields that tell you why a claim was denied or adjusted. On an ERA, they come through cleanly. On a faxed or PDF EOB, they require manual review.
AI extraction pulls CARC/RARC codes as typed structured fields from every EOB document. Your team filters "show me all CO-45 denials in the last 30 days" and works them as a batch instead of reading every EOB individually.
2. Catching missed denials
Manual EOB review consistently misses 5–10% of appealable denials. Not because the reviewer is incompetent — because EOBs are long, dense, and easy to skim past. AI extraction surfaces every denial code as a typed field, so nothing gets missed in a fast read.
For most billing services, the dollars recovered on previously-missed denials exceed the cost of the tool by a wide margin.
3. Payer-format variance
BCBS, UHC, Aetna, CMS, Humana, and the long tail of regional payers all use slightly different EOB layouts. Rules-based scrubbing handles outbound payer formats; it doesn't help with inbound EOB variance.
AI vision models read the layout semantically rather than via templates. A new payer EOB format works on day one without configuration. Compare that to template-based extraction tools that require per-payer setup before they'll process the document.
Where AI still needs a human in the loop
Three places where automation should defer to a billing or clinical reviewer:
- Coding decisions. AI extracts what's on the document. It doesn't recommend the right CPT code for a procedure or the right ICD code for a diagnosis. That stays with qualified billing staff.
- Appeal strategy. AI surfaces denials by code; humans decide which to appeal, when, and how.
- PHI handling and patient-specific judgment. AI is a data accelerator. It is not, and should not be, a substitute for clinical or compliance judgment.
PaperAI extracts data only. We never recommend clinical or coding decisions. All extracted codes, amounts, and denial reasons should be reviewed by qualified billing or clinical staff before submission or appeal.
HIPAA and BAA
PaperAI is not HIPAA-compliant and does not sign Business Associate Agreements. Today the product is appropriate only for de-identified workflows — training data preparation, CARC/RARC code-library research, denial-pattern analysis on scrubbed sample EOBs, and coder onboarding. Do not upload PHI. For PHI handling, choose a vendor that holds a signed BAA today. Current status is published at /trust.
The right combination in 2026
For most RCM teams, the right answer in 2026 is:
- Rules-based scrubbing for outbound claim quality (keep what works)
- AI extraction for inbound documents — EOBs, paper claims, payer PDFs (close the gap)
- AI augmentation for pattern-based denial prediction (early warning on emerging payer behavior)
Treat them as complementary, not competitive. The teams that get the most out of 2026 RCM tooling are the ones who pair their existing scrubbing library with AI extraction on the inbound side.
For a side-by-side look at clearinghouses, EHR billing modules, manual entry, and AI extraction, see the medical claims processing tools comparison.
Try it on your own documents
PaperAI extracts claim data from CMS-1500, UB-04, and EOB documents in under 30 seconds with structured CARC/RARC denial codes. Drop your first claim or EOB and see the output.