Claims Scrubbing and Submission: What to Automate vs. What Stays Human-Reviewed

What can AI automate in claims scrubbing and submission?

A claims scrub is a check run on a finished claim before it is sent, to catch errors a payer would otherwise reject. Pre-submission validation is the same check named for its timing. Software can own the part of the scrub where a rule can settle the answer.

AI can automate three kinds of check: format validation, completeness checks and payer rule edits. An agent can run all three on every claim, show which rule fired, and sort the claim into a queue.

• Format validation asks whether a value is shaped the way the receiving system expects.
• Completeness checks ask whether a required field is filled.
• Payer rule edits ask whether the claim meets a requirement your team has recorded for that payer or client.

The vendor documentation we reviewed shows both halves of this split. One EHR vendor says its AI Billing solution "provides claim-scrubber-style feedback on the bill before submission" ^[1]. A clearinghouse documents the other half: of a claim that fails its edits, it says Stedi "never sends it to the payer", and of one that passes, "the claim passed Stedi's edits, and Stedi sent it to the payer" ^[2]. Those are one vendor's statements about its own product, not a description of every scrubber.

Submission is the narrower step. Software can assemble the claim batch, hold flagged claims back, and queue clean ones. We recommend that a person releases the batch, which keeps the release decision inside your own review process.

The automatable vs. not-automatable matrix

Use the matrix below to sort every edit in your current scrub step. The test in the last column is the same for each row: if two careful reviewers could reach different answers, a billing specialist decides. Copy the table into your own document and add rows for your clients.

Two rows deserve a second look. Duplicate checks look mechanical, but the call on a flagged pair is a judgment, so that row carries a review step. And "payer rules your team has written down" is a boundary, not a loophole: a rule enters the table because a person recorded and approved it, never because the agent inferred it from past claims.

What stays human in claims scrubbing?

Coding and clinical judgment stay with the billing specialist. Coding judgment means choosing which code or modifier describes a service, or deciding that the record supports it. A flag can point at a field, but it never edits one. We treat this as the organization's own operating choice and recommend writing it down.

• Choosing or changing a code or modifier.
• Deciding whether documentation supports what was billed.
• Deciding whether to override a payer edit, and releasing the claim.

The vendor documentation we reviewed takes a similar line for its advisory flags: "A flag never changes codes and never prevents you from saving the bill, signing the visit note, or submitting the claim" ^[1]. That describes one product's advisories. Your own rule is stricter or looser by choice.

How do payer-specific rules get kept current?

Keep them in a rule table with a named owner, a dated change log and a source note for each rule, and let the table, not the agent, change when a payer changes. This is a recommendation about method. Nothing in a flag tells you a payer's rule has changed; you learn that from the payer or from rejections.

Rejections are the feedback loop. A clearinghouse's documentation describes its 277CA response as indicating whether a claim was "accepted for processing or rejected due to correctable errors, such as invalid data, missing information, or failure to comply with payer-specific rules" ^[3]. We recommend that every rejection that cites a rule your table lacks produces a proposed new row for the owner to review.

1. Assign an owner for each client's rule table and a backup.
2. Record each rule with its payer, wording, source document and the date it was added.
3. Review rejections weekly and propose a row for any that name a rule the table lacks.
4. Retire a rule only when the owner confirms it no longer applies, and keep the old row in the log.

Who is accountable if a claim is submitted incorrectly?

We recommend that the named billing specialist who releases a batch owns that decision inside the organization's review process. The agent is a tool in that process, and software cannot own an outcome. Put this in the written procedure. Where responsibility goes beyond that process is a question for your compliance team or counsel.

Accountability needs a record. For each released claim, keep a short log. That log lets a team trace a rejection to a missing rule, a miss in review or a source-data error, and fix the right one.

• The flags the agent raised on the claim.
• What the specialist did about each flag, and why.
• Who released the batch, and when.

The pattern is the same as the approval step in AI agent approval gates for finance, which transfers to a claims release because the approver is named and the decision is logged.

Does this replace an existing claim scrubber tool?

Not necessarily. A claim scrubber or clearinghouse edit set is a different layer from an agent that works across your clients' queues, and the two can run in sequence. Vendor documentation describes each product's own checks, so read yours before deciding what an agent should add.

The clearinghouse documentation we reviewed shows why the layers matter. Its endpoint "returns an HTTP `201` whether Stedi's edits pass or fail" ^[2], so a successful response alone does not tell the sender the claim went to the payer. Someone, or something, has to read the result and act on it.

• Keep the scrubber you have where it does its job.
• Use an agent for the gap: per-client checklists, flag sorting across accounts, and the log of what was done about each flag.
• Replace a tool only after a side-by-side test on closed claims shows the replacement catches what the old one did.

When this doesn't apply

• If one claim type for one client already clears with few flags, a checklist and a second pair of eyes may be enough.
• If your scrubber or practice-management system already runs the edits you need, add software only for the gap that remains.
• Rules that live only in a specialist's memory cannot be automated until someone writes them down.
• Where two reviewers genuinely disagree about an edit, route it to a person. Do not write a rule just to remove the question.

Where to start: a first scoped workflow

Do not scrub every client's claims with an agent on day one. Pick one client and one claim type, and run the agent in review-only mode alongside your current process.

1. 01
   Inventory the edits

   List every check in your current scrub step for the chosen client and claim type, including the ones that live in a specialist's head.

2. 02
   Sort with the matrix

   Mark each edit automate, automate with review, or stays human, using the one-question test from the matrix.

3. 03
   Write the rule table

   Record each automatable edit with its payer, wording, source and owner. If you cannot cite where a rule came from, leave it out.

4. 04
   Run in review-only mode

   Let the agent flag claims while specialists work as they do today. Log each flag as correct, incorrect or unclear.

5. 05
   Review the disagreements

   For every flag the specialist rejected and every problem the agent missed, decide whether to fix the rule or keep the check human.

6. 06
   Widen by one step

   Add one claim type or client at a time, and keep the release of each batch with a named specialist throughout.

Scoping, building and handing over a workflow like this is what our AI agent implementation service covers. The document review with human approval page describes how a review step with a named approver is structured.

Frequently asked questions

Related reading

• For how a review step with a named approver is structured, see document review with human approval.
• For how a workflow like this is scoped, tested and handed over, see AI agent implementation.
• For the approval pattern that transfers to claims release, see AI agent approval gates for finance.
• For the principle of keeping a person on every output, see human-in-the-loop AI governance.

Sources

1. Elation Health, "AI Billing Guide - Scrubbing claims" (accessed 2026-10-29)
2. Stedi, "Submit professional claims - API" (accessed 2026-10-29)
3. Stedi, "Acknowledgments and ERAs overview" (accessed 2026-10-29)