Artificial intelligence claims processing means software that reads a claim, checks it against codes and rules, and on the payer side helps decide whether to pay, hold or deny it. The reading and checking save keying time. The deciding still rests on payer rules, and every step can be wrong, so people review what the software is unsure of.
Artificial intelligence claims processing tasks: reading, checking, deciding
Artificial intelligence claims processing splits into three jobs. Reading turns a paper or PDF claim into fields. Checking compares those fields with code sets, payer rules and the patient's record. Deciding, which happens at the payer, turns the checked claim into a payment, a hold or a denial. Each job has its own inputs, outputs and failure modes.
Reading is the job most billing teams meet first. A claim form arrives as a scan, a fax or a PDF attachment, and someone has to get the patient, the provider, the diagnosis codes and the service lines into a system. On a professional claim those service lines sit in item 24 of the CMS-1500. The CMS Medicare Claims Processing Manual, chapter 26 describes six service lines in that section, each with a date of service, a place of service code, a HCPCS code with up to four modifiers, a diagnosis pointer, a charge and a count of days or units. Document AI reads those boxes and returns each one as a named field.
Checking is pattern work on the extracted data. Software confirms that a diagnosis pointer on a line refers to a diagnosis that is actually listed, that a date range has a "to" date, that the line charges add up to the total in item 28, and that a code pair is allowed together. The same chapter tells Medicare contractors to return a claim as unprocessable when a date of service spans more than one day and no valid "to" date is present, which is exactly the kind of rule a checking step can apply before a claim goes out.
Deciding belongs to the health plan. An adjudication system takes the claim, the member's coverage and the plan's rules and produces a result for each line. AI can help a payer route or prioritize claims, but the outcome a provider sees is a paid amount, an adjustment and a reason code.
AI tasks on a claim, side by side
The table below lists the common tasks. The example inputs are fictional.
| Task | Example input | Output | Typical failure | Who reviews |
|---|---|---|---|---|
| Classify the document | A faxed page that could be a CMS-1500, a UB-04 or an EOB | A document type label | A claim attachment labelled as the claim itself | Intake staff |
| Extract header fields | Patient "Ana Ruiz", member ID "XJT448812" (example values) | Named fields with confidence scores | A 0 read as an O in the member ID | Billing specialist |
| Extract service lines | Six lines in item 24 | One row per line with date, code, units, charge | A shaded supplemental row read as a seventh service line | Billing specialist |
| Validate codes | HCPCS 99213 with modifier 25 (example) | Pass, or a flag naming the problem | A valid but rare code flagged as invalid | Coder |
| Check totals | Six line charges and an item 28 total of $1,240.00 (example) | Match, or the difference | A handwritten total read with a missing digit | Billing specialist |
| Predict a denial | The clean claim and past remittances | A risk flag with a likely reason | A confident flag on a claim type the model rarely saw | Billing lead |
| Adjudicate (payer) | The submitted claim and plan rules | Paid, pended or denied per line | A line denied by an edit the provider can contest | Payer staff, then provider on appeal |
Reading these tasks as separate steps makes the review plan clearer. A wrong document label is caught at intake. A wrong number in a field is caught by a person who can see the page. A wrong prediction is caught only when the remittance comes back.
How does an automated claims adjudication system decide a claim?
An automated claims adjudication system decides a claim by running it through a sequence of edits and rules. It checks that the claim is complete, that the member was covered on the date of service, that the codes are valid and allowed together, and that the units are plausible. Then it prices each line and assigns any patient share.
Medicare publishes part of this logic. The CMS National Correct Coding Initiative page explains that procedure-to-procedure (PTP) edits exist to prevent improper payment when pairs of codes should not be billed together, and that medically unlikely edits (MUEs) catch units of service reported in amounts that are not plausible. The page says Medicare administrative contractors build both kinds of edit into their claim processing systems.
Each line ends in one of three states. A paid line has an allowed amount and a payment. A pended line waits for something: records, an authorization check or a manual look. A denied line pays nothing and carries a reason.
The reasons come back as codes. The X12 Claim Adjustment Reason Codes list describes these codes as explaining why a claim or service line was paid differently than it was billed. Several map directly to automated edits. Code 236 covers a procedure or modifier combination that is not compatible with another one on the same day under the National Correct Coding Initiative. Code 16 covers a claim that lacks information or has a submission or billing error. Code 18 marks an exact duplicate claim or service.
The order matters to a billing team. A claim that fails a completeness edit never reaches pricing, so the remittance shows a missing-information reason rather than a coding problem. Fixing the first failure can expose the next one on resubmission.
The meaning of each reason code and how to post it belongs elsewhere. This section covers how the system reaches a decision, not how to respond to one.
AI insurance claims processing on the provider side versus the payer side
AI insurance claims processing looks different depending on who runs it. A provider uses AI to get claims out clean: reading paper, filling fields, catching errors before submission and predicting denials. A payer uses AI to take claims in: routing them, applying edits, flagging unusual patterns and deciding which ones need a person.
On the provider side, the goal is a claim that passes the payer's edits the first time. The work starts with source documents such as encounter forms, superbills, referral letters and scanned claim forms. Extraction pulls the fields out, and scrubbing checks them against the same kinds of edits the payer will run. The output is an electronic claim. Under HIPAA, the eCFR text of 45 CFR 162.1101 defines the health care claims transaction as a request from a provider to a health plan to obtain payment, with the accompanying information. The standard for that transaction is named in the next section of the same subpart.
On the payer side, the goal is a correct decision at volume. Payers see claims from many providers with many habits, so their models look for patterns across the whole book: unusual billing frequency, code combinations that rarely occur, or a provider whose claims suddenly change. A flag usually sends the claim to a person or asks the provider for records rather than denying it outright.
The two sides meet at the remittance. When a payer's edit denies a line, the provider's own AI may have predicted it, missed it, or flagged it and been overridden. Comparing predictions with actual remittance codes is the only honest way to tell whether provider-side AI is helping.
For how AI fits across eligibility, coding, posting and collections, the overview of AI in revenue cycle management covers the billing-cycle view. This post stays with the claim itself.
Where do artificial intelligence claims tools get things wrong?
Artificial intelligence claims tools get things wrong in predictable places: layouts they have not seen, codes that are rare in their training data, poor scans, and cases where a confident answer hides a wrong one. The last is the most expensive, because a wrong value with a high score skips the checks built to catch it.
Unseen layouts are the first limit. A model that has read thousands of standard claim forms may still stumble on an old form version, a payer-specific attachment or a claim printed slightly out of line with the boxes on the form. Fields shift, and a value lands in the wrong box.
Rare codes are the second. A checking step that flags anything unusual will flag valid but uncommon codes, which wastes a coder's time. A step tuned to flag less will let real errors through. Neither setting is free.
Poor scans are the third. Faxed claims lose detail, and handwritten corrections on a printed form can be read as the original value or the correction. The guide to medical bill OCR shows how amount columns on a billing statement suffer from the same problem, and why totals are worth checking after extraction.
Confident errors are the fourth. A model can assign a high score to a value that is simply wrong, such as a member ID with two digits swapped that still looks valid. A totals check, a cross-check against the eligibility record, or a person looking at the page catches these. A confidence score alone does not.
Supplemental rows on the CMS-1500 are a specific trap. The CMS claims processing manual chapter on the CMS-1500 explains that each of the six lines in item 24 has a shaded top portion for supplemental information, such as drug codes, and that this is not meant to allow 12 service lines. Software that reads the shaded row as a separate line doubles the line count and breaks the total.
Human review in artificial intelligence claims workflows
Human review in artificial intelligence claims workflows is the step where a person confirms or corrects what the software was unsure of. Every AI claims workflow needs one, because each AI task on a claim has a failure that only a person with the source page can resolve. The open question is where to put it.
Three places work well. The first is after extraction, where a reviewer sees any low-confidence field highlighted on the original page and fixes it before the claim is built. The second is after scrubbing, where a coder looks at flagged code combinations. The third is after the remittance, where a billing lead compares denials with what the software predicted.
The first checkpoint does the most good per minute. A wrong field caught before submission costs a few seconds. The same error caught on a denial costs a resubmission and days of waiting.
Schema Care is built around that first checkpoint. It reads CMS-1500 and UB-04 fields, diagnosis codes and procedure codes from a claim, scores each field, and holds any field below the threshold your team sets until a reviewer approves or corrects it on the highlighted page, as the human review steps on the Schema Care homepage describe.
How to set a confidence threshold, and how to size a review queue, are covered in the human review category. For the order in which to automate each claims step, the phased claims automation plan for a billing team lays out a three-phase approach.
Some teams also use software robots to enter checked data into payer portals. That is a different tool with different failure modes, compared in the post on RPA in healthcare claims processing.
A review step also needs a record. Each approval and correction should show who made it and when, so that a later question about a claim can be traced back to the page and the person who confirmed the value.