AI for Underpayment Detection: How Healthcare Organizations Can Find Missed Revenue

Underpayments are a common source of revenue leakage in healthcare. A claim may be processed and paid, yet the provider may receive less than the amount expected under the applicable contract, fee schedule, or reimbursement methodology.

AI and revenue cycle analytics can help healthcare organizations identify potential underpayments by comparing payment data with expected reimbursement and surfacing patterns for review. A payment variance is a signal to investigate, not proof that a payer made an error. Contract, claim, and remittance context still matter.

Adonis Intelligence supports underpayment detection and analysis through dashboards and specialized reports. In a published customer example, a gastroenterology group used historical claims and reimbursement data to identify nearly $500,000 in underpaid claims in five months, then used the findings in payer discussions. Read the case study

What is an underpayment in healthcare?

A contractual underpayment occurs when a payer reimburses a provider for less than the amount expected under the applicable contract, fee schedule, or reimbursement methodology.

For example, if the expected reimbursement for a service is $1,000 and the payer pays $800, the $200 difference may be a potential underpayment. It is not automatically an error. The difference may reflect legitimate factors such as:

• Contractual adjustments
• Patient responsibility
• Secondary insurance
• Benefit coverage
• Bundling or other payment methodology
• Claim- or service-line adjustments
• Payer-specific reimbursement rules

That is why underpayment detection requires more context than simply comparing the payment with the billed charge.

Why are healthcare underpayments difficult to detect?

A paid claim is not necessarily a correctly paid claim. Healthcare organizations handle claims across multiple payers, plans, contracts, specialties, providers, and locations. The rules and adjustments that affect reimbursement can vary across those situations.

Teams may use contractual variance reports, manual claim reviews, payment audits, payer-specific reports, or retrospective sampling to find potential issues. These methods can uncover opportunities, but they can also make it difficult to see recurring patterns across a large claim population, especially when each individual variance is relatively small.

How can AI help detect potential underpayments?

AI and analytics can help organize payment information and reveal variances or patterns that warrant review. A useful process includes four steps:

1. Compare payment with expected reimbursement

Where the necessary contract or fee-schedule information is available, compare what was paid with the expected reimbursement for the claim or service. The amount billed is not, by itself, the correct benchmark for payment.

2. Look for patterns across claims

A single variance may be isolated. Reviewing multiple claims can show whether potential issues cluster around a payer, plan, service, provider, or other claim segment.

3. Validate the payment context

Before calling a variance an underpayment, review the applicable contract or fee schedule alongside claim and remittance details. This helps distinguish a potential payer error from a legitimate adjustment or other payment rule.

4. Decide what action to take

After validation, the provider's revenue cycle team can determine the appropriate next step, such as further research, payer outreach, a contract discussion, or a claim dispute when warranted. Detection and prioritization are not the same as automatically recovering the money. The right action depends on the claim and payer context.

What data is useful for underpayment analysis?

The data needed depends on the organization's contracts, systems, and analysis workflow. Commonly relevant information includes:

• Claims and service-line details
• Payment and remittance information
• Contract terms or fee schedules
• Payer and plan
• Procedure codes and relevant modifiers
• Adjustment details
• Provider and location
• Historical reimbursement

The goal is to understand what was billed, what was expected, what was paid, and what explains the difference. Data availability and configuration can affect the analysis.

What can Adonis help organizations see?

Adonis Intelligence supports underpayment detection and analysis through reporting and visibility into payment and reimbursement data. A public Adonis case study describes a review of historical claims and reimbursement data using dashboards and an Underpayments report. The analysis identified nearly $500,000 in underpaid claims within the first five months of 2024, showing which payers and claim segments were involved. The customer then used the findings to engage payers and recover owed dollars. Read the underpayment case study

A separate public case study reports that Allied Digestive Health increased remittance on underpaid claims by 1% with greater visibility into underpayments data. This is a customer-specific result, not a guaranteed outcome. Read the Allied Digestive Health case study

These examples show how underpayment detection and reporting can help teams quantify potential issues and focus payer discussions. Results depend on each organization's data, contracts, workflows, and implementation.

AI vs. traditional underpayment detection

Traditional workflows often rely on predefined rules, manual audits, and sampling. AI and analytics can complement those methods by helping teams examine larger sets of payment data and find patterns to investigate.

Neither approach removes the need to validate a variance against the relevant contract and claim details. The practical value comes from making potential issues easier to find and giving RCM teams clearer evidence for their next step.

How can healthcare organizations get started?

1. Identify where payment differences are occurring

Review historical reimbursement by payer, service, specialty, or location to see where potential variances are concentrated.

2. Document the current process

Map the reports, rules, audits, and manual reviews the team already uses to identify and validate payment differences.

3. Connect analysis to a follow-up process

Define who reviews a potential underpayment, what evidence they need, and how the organization decides whether to contact or dispute with a payer.

4. Track outcomes

Useful measures can include potential dollars identified, dollars validated, dollars recovered, recovery rate, time from payment to identification, and staff time spent on review. Define each measure consistently so teams can distinguish amounts flagged for review from amounts confirmed or recovered.

The role of AI in revenue recovery

The revenue cycle does not necessarily end when a claim is paid. A payment may still need to be reviewed to determine whether it matches the applicable reimbursement terms.

AI and analytics can help teams find potential payment variances and understand where further investigation may be useful. With the right claim, payment, and contract context, revenue cycle teams can move from asking only whether a claim was paid to asking whether it was paid as expected.

Frequently asked questions

Can AI detect healthcare underpayments?

AI and analytics can help identify potential payment variances and patterns across claims. Those findings should be validated against the applicable contract, fee schedule, claim, and remittance details before an organization treats them as confirmed underpayments.

What is the difference between an underpayment and a denial?

A denial means the payer did not reimburse the claim or service as submitted. An underpayment means a claim was paid, but the reimbursement may be less than expected under the applicable contract or reimbursement methodology.

Is every payment variance an underpayment?

No. A variance can result from legitimate contractual, benefit, patient-responsibility, or other payment adjustments. The circumstances of the claim and the applicable reimbursement terms determine whether it is an underpayment.

How does Adonis support underpayment detection?

Adonis Intelligence supports underpayment detection and analysis through reporting and visibility into payment and reimbursement data. In a published case study, a gastroenterology group used historical claims and reimbursement data and an Underpayments report to identify nearly $500,000 in underpaid claims within five months. See the case study

What data is useful for underpayment analysis?

Claims, payment and remittance information, contract or fee-schedule data, payer and plan details, and historical reimbursement can all be relevant. The required data depends on the organization's reimbursement arrangements and implementation.

What should organizations measure?

Track potential dollars identified, dollars confirmed, dollars recovered, recovery rate, time to identification, and staff time spent on review. Keep identified, validated, and recovered amounts separate so the metrics reflect different stages of the process.