Underpayment Detection: Stop Silent Revenue Loss

Why underpayment detection matters for outpatient clinics

Picture this, your schedule is full, therapists are booked, and yet month end reports show revenue that feels a little light. No obvious crisis, just a slow drip that never quite matches the work your team is doing. That quiet gap is often where underpayments live.

Underpayment detection is about protecting access, throughput, and staff workload as much as it is about dollars. Industry analyses often suggest that providers can lose roughly one to three percent of net patient revenue when underpaid claims slip through routine workflows, and that is before you factor in the time your team spends untangling explanations of benefits that never quite add up. At the same time, the Centers for Medicare and Medicaid Services has reported an improper payment rate of around seven and two thirds percent in Medicare fee for service spending, a category that includes both overpayments and underpayments and that translates into tens of billions of dollars.

If you run an outpatient therapy clinic, that scale matters. Underpayments shrink the resources you have for staff, space, and technology. They can also push teams into a constant firefight, where everyone is reacting to shortfalls rather than working from a clear view of what payers should have paid in the first place. Underpayment detection offers a more disciplined path, one that aligns naturally with automation, structured intake, and unified communication tools such as the ones described across the Solum Health Solutions and How it works pages.

What is underpayment detection

In plain terms, underpayment detection is the process of identifying claims that were paid less than what your contracts or fee schedules say you are owed. It compares three signals for each claim or line, what you billed, what the payer allowed under the contract, and what actually arrived in the electronic remittance. Whenever the paid amount falls short of the contractual or allowed amount, the system treats the gap as a potential underpayment.

It is useful to think of this as a specific strand in your revenue integrity work, separate from but closely related to topics like claim denial management and automated claims filing. Denial work focuses on claims that were not paid at all. Underpayment detection looks for claims that look fine at a glance, they are in the paid bucket, but that do not match the veracity of the contract terms you negotiated.

For outpatient therapy practices, this often involves visits that repeat over months, with varying units, modifiers, and authorization rules. The more complex that mix becomes, the harder it is for humans to spot small discrepancies. Underpayment detection provides a systematic, repeatable way to surface them.

How underpayment detection works in practice

Even though the phrase sounds technical, the workflow behind underpayment detection follows a straightforward sequence. In most clinics or multi site groups, it includes five core moves.

• First, centralize your data. Bring payer contracts, fee schedules, and remittance data into a shared environment so you are not trying to reconcile one claim at a time from scattered documents. This is where a unified view of communication and documentation, similar to the call and message consolidation described in the Solum call text email consolidation entry, becomes a practical foundation.
• Second, normalize and map. Contract language is full of idiosyncrasy. One payer may describe a rate by code and modifier, another by revenue category. Underpayment detection tools translate those different formats into a consistent model so each paid claim can be compared accurately to the expected amount.
• Third, calculate expected payment. For each claim line, the system looks at the agreed rate, any relevant modifiers, units, and authorization status. It then calculates what payment should have been, given the rules.
• Fourth, compare and flag. The actual payment from the remittance advice is compared to that expected figure. If the paid amount falls below the threshold you set, for example any gap larger than a few dollars or a set percentage, the claim is flagged as a possible underpayment.
• Fifth, route and track. Flagged claims move into a work queue for staff to review, correct, or appeal. Over time, patterns in those queues show where specific payers, codes, or sites are at a crossroads and need contract clarification, better documentation, or different workflows.

When this process is wired into your broader pre visit and intake flows, such as the approaches outlined in the Solum glossary article on automating pre visit workflows, you move from a reactive posture to one that treats payment accuracy as a routine part of operations. Solum Health consistently presents its role as a unified inbox and AI intake automation layer for outpatient facilities, specialty ready, integrated with EHR and practice management systems, and built to show measurable time savings in front office work. Underpayment detection fits that same zeitgeist of parsimony and precision.

Steps to adopt underpayment detection in your clinic

If you want to bring underpayment detection into your therapy practice this quarter, you can frame it as a contained project rather than a nebulous aspiration. A practical sequence might look like this.

• Define the problem in your own setting. Start with a simple question, how often do we audit payments today, and what percentage of claims are checked against contracts. Even an informal answer will highlight whether you are relying mostly on trust or on structured review.
• Prioritize payers and services. Focus on the portion of your revenue that matters most. Many clinics start with the largest commercial payer or with a subset of high volume evaluation and treatment codes. You do not need a labyrinthine scope to get value.
• Gather contracts and fee schedules. Pull the current versions, check that rates, modifiers, and effective dates are clear, and note any known quirks. This is where the juxtaposition between what was negotiated and what staff think the rules are often becomes visible.
• Choose or configure a detection tool. Some organizations extend existing billing platforms, others lean on AI supported tools similar in spirit to the AI powered front office described across the Solum home experience. The important part is that the tool can ingest remittances reliably and can represent contract rules with enough detail.
• Define thresholds and workflows. Decide what counts as a work item. For example, you might review any gap above a fixed dollar amount, or any pattern of repeated small variances for the same code. Map who will review flags, how they will document findings, and how corrected claims will be sent. The Solum Glossary and real world narratives in Case Studies can help you shape realistic thresholds.
• Integrate with intake and claims workflows. Make sure underpayment detection does not sit off to the side. It should connect to the same claims stream that your automated claims filing process uses and to the contact routes described in the Solum Blog when that content discusses revenue cycle improvements.
• Measure and refine. Track recovered dollars, staff time per underpayment worked, and recurring root causes. Over a few cycles you will see where to tighten documentation, contract terms, or system configuration. That feedback loop is where underpayment detection shifts from a quixotic exercise to a dependable revenue integrity habit.

Common causes of underpayments that detection can surface

Once you begin looking systematically, certain sources of underpayment appear over and over.

You may find contract configuration gaps, for instance, a new rate went into effect on a given date but the billing system kept using the prior value.

You may see inconsistent application of modifiers that change reimbursement, especially in therapy settings where place of service and supervision rules vary.

Another frequent source is authorization and visit limit misalignment. The payer may have reduced the allowed number of units or visits, but staff continued scheduling at the prior level, which leads to partial payments.

Documentation gaps also play a role. If the clinical record does not fully support the level of service that was billed, payers sometimes downcode rather than deny outright. This is the quieter cousin of full denial and it often goes unnoticed without structured review.

Finally, pure processing errors do occur, sometimes on the payer side, sometimes in clearinghouse steps. Underpayment detection does not assume bad intent, it simply notices when the numbers lack internal consistency and invites a closer look.

Across all of these, the goal is not to chase every last dollar at any cost. It is to find a responsible balance between effort and recovery so that your team can focus on care delivery without leaving significant contracted revenue uncollected.

Pitfalls to avoid

There are a few predictable traps that can undercut underpayment detection programs.

One is trying to do everything manually. Occasional spot checks by a single biller will not keep pace with the volume of claims in a busy outpatient practice.

Another is ignoring staff workload. If you generate an enormous queue of underpayment flags with no clear criteria or triage, the project will feel like an impossible burden and will quietly stall.

A third pitfall is treating the work as purely financial. When findings are not shared with scheduling, intake, and documentation teams, the same issues recur. Tying underpayment insights back into pre visit workflows, as described in the Solum content on automating pre visit workflows, is what turns detection into prevention.

Finally, some groups overlook the broader policy context. Public sources such as the Centers for Medicare and Medicaid Services and independent analysis from KFF can help you understand how improper payments, including underpayments, fit into the wider regulatory and funding landscape, which can be helpful when you need to explain the work to senior leaders.

Frequently asked questions about underpayment detection

What is underpayment detection in healthcare billing

Underpayment detection is the structured process of comparing what a payer actually paid on a claim with what your contracts and fee schedules say they should have paid. When the paid amount falls short of that expected figure, the claim is flagged so staff can investigate, correct, or appeal.

How is underpayment detection different from denial management

Denial management focuses on claims that were not paid at all, usually because the payer rejected or denied them for specific reasons. Underpayment detection looks at claims that were paid, often without obvious issues, and checks whether the amount aligns with contracted rates. Both are important, but they address different parts of the revenue cycle.

Does underpayment detection only matter for large hospitals

No. Outpatient therapy clinics, multi site practices, and smaller specialty groups can all experience meaningful underpayments, especially when they handle high visit volume with complex authorization and coding rules. Even a modest percentage of underpaid claims can have a visible impact on margins in these settings.

What data do we need to start underpayment detection

You need access to current payer contracts or fee schedules, detailed electronic remittance information, and a reliable mapping between your billing codes and those contract terms. Many clinics also benefit from connecting underpayment analysis with broader EHR and practice management data so they can see patterns across sites, providers, and visit types.

How often should we review underpayment findings

At a minimum, most organizations benefit from monthly review of underpayment patterns so they can spot trends before they become entrenched. High volume practices, or those in the middle of major payer or documentation changes, may choose weekly reviews until the environment stabilizes.

Action plan for clinic leaders

If you are a practice administrator or medical director in an outpatient clinic, you do not need a perfect system on day one. Start with one payer and a focused group of services, bring contracts and remittances into a single view, and define clear thresholds for what you will review. Connect that work to the same automation and unified communication stack you use for scheduling, intake, and follow up, the same direction you see reflected in Solum Health content about an AI powered front office and unified inbox for outpatient care.

From there, let results guide refinement. When you see recurring underpayments linked to specific codes, documentation patterns, or sites, you will know exactly where to invest training and process changes. Underpayment detection, handled with care and a bit of serendipity in how you combine human judgment with automation, becomes less of a chore and more of a quiet guardian for the financial side of your clinical mission.