AI-powered RCM vs. traditional medical billing: what AI does today and where people still decide
AI-powered revenue cycle management uses software to do the repetitive checking and sorting that billers used to do by hand: verifying eligibility through 270/271 transactions, scrubbing claims against NCCI and payer edits before submission, grouping denials by reason code, ranking accounts receivable by urgency, and posting 835 remittances while flagging underpayments. Traditional billing does the same work with people reading portals, worklists and EOBs one at a time. The difference is speed and coverage on routine checks. The judgment calls (how to code a visit, whether to appeal, when to write off a balance) still belong to trained people, and the practice is still responsible for every claim submitted under its NPI.
The term AI covers a wide range of software, from rule engines to machine learning models to tools that draft text. This guide sticks to what these tools do in a billing office today, task by task, and where each one falls short.
What traditional billing looks like
In a traditional workflow, a front desk staff member checks eligibility in a payer portal or the clearinghouse for each patient, often the morning of the visit. A biller enters or reviews charges and relies on the clearinghouse's edits to catch errors. Payments are posted from ERAs, with exceptions handled by hand. Follow-up runs from an aging report sorted by date or balance, and denials are worked in the order they arrive.
This works when volume is manageable and staff are experienced. It breaks down when a few people are responsible for thousands of claims, because routine checks get skipped and follow-up tends to favor the oldest or largest balances rather than the ones closest to a deadline.
What AI-assisted tools do in RCM today
Eligibility verification (270/271)
The HIPAA standard eligibility inquiry is the X12 270 transaction, and the payer's response is the 271. AI-assisted tools send 270 inquiries in batches for upcoming appointments, read the 271 responses, and flag patients whose coverage is inactive, whose plan changed, or whose benefits show a high deductible or a different primary payer. Staff get a short exception list instead of checking every patient one by one.
Limits: 271 responses vary by payer and are sometimes incomplete. Behavioral health or imaging benefits may be managed by a separate company that the 271 does not show, and some details (visit limits, specific service coverage) still need a portal lookup or a call.
Claim scrubbing against NCCI and payer edits
Before submission, scrubbing software checks each claim against National Correct Coding Initiative (NCCI) procedure-to-procedure edits and medically unlikely edits (MUEs), modifier rules, diagnosis-to-procedure logic, and payer-specific rules. It flags a missing modifier 25 on an E/M billed with a minor procedure, units that exceed an MUE, or a diagnosis that does not support a service under a Local Coverage Determination.
Limits: a scrubber checks the claim, not the chart. It cannot tell whether the note supports the E/M level or whether a modifier 59 is justified. Rule sets also need updating when CMS releases quarterly NCCI updates or a payer changes a policy.
Denial categorization by CARC and RARC
Every denial on an 835 carries a group code (CO, PR, OA or PI), a Claim Adjustment Reason Code (CARC) and often a Remittance Advice Remark Code (RARC). AI-assisted tools read these codes, group denials by root cause (eligibility, authorization, coding, bundling, timely filing, missing information) and route them to the right worklist. That makes patterns visible: if CO-197 authorization denials rise for one payer, the team sees it within days.
Limits: the same CARC can mean different things. CO-16 (claim lacks information) depends entirely on the remark code, and payers do not always apply codes consistently. Writing an appeal still means reading the record, the payer's policy and the contract.
A/R prioritization
Instead of working an aging report top to bottom, prioritization tools rank open claims by balance, age, timely filing deadline, payer behavior and the type of action needed. A $90 claim that hits a 90-day filing limit next week can rise above a larger claim that has months left.
Limits: the ranking is a suggestion. A biller who knows a payer is reprocessing a batch of claims, or that a patient called about a balance, should override it.
835 posting and underpayment detection
Payment posting tools read 835 electronic remittance files, post matched payments and adjustments, and reconcile them to EFT deposits. With your contracted fee schedules loaded, they compare each allowed amount to the expected rate and flag lines that were paid below contract.
Limits: underpayment detection is only as good as the fee schedules loaded into it. Recoupments, offsets and provider-level adjustments in the 835 still need a person to trace them to the original claim.
Side-by-side comparison
| Task | Traditional approach | AI-assisted approach | What people still do |
|---|---|---|---|
| Eligibility | Portal checks one patient at a time | Batch 270/271 with exception flags | Resolve flagged accounts, call payers when needed |
| Claim review | Clearinghouse edits, manual spot checks | Scrub against NCCI, MUE and payer rules | Check documentation, decide coding and modifiers |
| Denials | Worked in order received | Grouped by CARC/RARC and root cause | Correct claims, write appeals, fix the upstream step |
| A/R follow-up | Aging report sorted by date or balance | Ranked by deadline, balance and payer | Call payers, decide on escalation or write-off |
| Payment posting | Manual posting of exceptions | 835 posting with underpayment flags | Review flags, trace recoupments, dispute underpayments |
Where AI falls short in billing
- It cannot read intent from a chart. Coding depends on what the provider documented and why. Tools can suggest codes, but a certified coder or experienced biller needs to confirm them.
- Payer rules change faster than rule sets. A new prior authorization requirement or a revised medical policy may not be in the rules engine on the day it takes effect.
- Text-generating tools can be wrong with confidence. A drafted appeal letter can cite a policy that does not apply. Anything sent to a payer should be checked by a person.
- Edge cases are common. Coordination of benefits, workers' compensation, out-of-network claims and retro eligibility changes rarely follow a clean rule.
- Accountability stays with people. Payers and auditors hold the practice responsible for what it bills, regardless of which software flagged or missed an error.
Why human review still matters
AI makes the queue shorter and better sorted. It does not decide what to do with what is in the queue. The decisions that move revenue (coding a complex visit, appealing a medical necessity denial with the right records, recognizing that a payer is misapplying a contract) depend on experience and context. A good AI-assisted workflow puts the software on the checking and the people on the deciding, and makes it clear who signed off on each action.
When you evaluate a vendor that uses AI, ask specific questions:
- Which tasks does the software perform, and which does a person review before anything is submitted
- Whose software is it, and is it covered under the vendor's Business Associate Agreement
- Does the tool work inside your EHR and PM system, or does your data have to move somewhere else
- How are payer rule changes added, and how quickly
- What happens when the tool and the biller disagree
Be careful with any vendor that says AI eliminates denials or runs billing without human review. Neither claim holds up in practice.
What results to expect from AI-assisted billing
AI-assisted checking can help reduce preventable errors, such as missing modifiers, inactive coverage and missed filing deadlines, because more claims get checked before they go out. It does not change payer behavior, fix documentation gaps or speed up a payer's processing time. Measure results with the same metrics you would use for any billing vendor: clean claim rate, first-pass denial rate, days in A/R and net collection rate. Our guide on improving your clean claim rate covers how to track these.
How SFA can help
SFA's experienced billing team works with SFA Intelligence, our technology-supported workflow powered by the RCM Employee platform, which we license. It helps with 270/271 eligibility checks, claim scrubbing against NCCI and payer edits, denial categorization, A/R prioritization and 835 underpayment flags, while our billers review and own the claim decisions through our revenue cycle management, denial management and eligibility verification services. If you want to see how this would apply to your practice, start with a free practice audit.
Frequently asked questions
Can AI submit claims without a biller reviewing them?
Some software can technically send claims automatically, but submitting without review shifts risk onto the practice. A sound workflow uses software to check claims and flag problems, with a trained biller or coder reviewing flagged claims and coding decisions before submission.
Does AI-powered RCM mean fewer denials?
It can help reduce preventable denials, such as inactive coverage, missing modifiers and NCCI conflicts, because more claims are checked before they go out. It cannot prevent denials caused by documentation gaps, payer policy changes or coverage the payer decides not to pay.
What is a 270/271 transaction?
The 270 is the HIPAA standard electronic eligibility inquiry sent to a payer, and the 271 is the payer's response. The 271 shows whether coverage is active and often includes plan details, copays, coinsurance and deductible information.
Member of the SFA Solutions revenue cycle team.