Automation vs Manual Review in Underpayment Recovery: How to Choose the Right Strategy

Every RCM leader knows denials are a problem. What most organizations underestimate is how much silent damage underpayments create. Claims that are “paid” but paid incorrectly or below contracted rates are often treated as resolved, even though they represent pure margin leakage.

As payer contracts become more complex and margins tighten, the way you detect and recover underpayments is no longer a back-office detail. It is a strategic decision. Should you lean on automation, invest in a team of expert analysts, or pursue a hybrid approach that blends both?

This article breaks down how to evaluate automation versus manual review for underpayment recovery, how each impacts cash flow and staffing, and how to design a practical hybrid model that works in real-world RCM operations.

Why Underpayments Deserve Their Own Strategy, Not Just More AR Work

Many organizations treat underpayments as a subset of general accounts receivable follow up. That approach typically fails, because underpayment recovery requires different skills and tools than standard AR or denial follow up.

Underpayments are often the result of contract misapplication, incorrect pricing, missed modifiers, misconfigured fee schedules, or payer policy inconsistencies. These issues are structurally different from a denial for “no coverage” or “timely filing.” As a result, they demand a specialized operating model.

Why this matters:

• Revenue at stake: It is common for 1 to 3 percent of net revenue to be tied up in chronic underpayments. For a 50 million dollar practice or a mid-size hospital, that can mean hundreds of thousands of dollars per year.

• Hidden nature: Unlike denials, underpayments do not always generate obvious work queues. Many “paid” claims never receive a second look.

• Contract risk: Failure to systematically monitor payer compliance weakens your negotiating position and can mask chronic under-reimbursement for high-value services.

Operational example: A multi-specialty group with 400k claims annually spot-checks EOBs. The billing team identifies obvious zero pays and a few short-pays but has no systematic way to compare each payment against contracted rates. Even a 1.5 percent underpayment rate on net collectible charges can mean over 750k dollars in annual leakage, with no formal visibility into where the losses occur or which payers drive them.

Before deciding on automation or manual review, leadership should first treat underpayment recovery as its own workflow with dedicated metrics, staffing, and technology decisions.

Manual Underpayment Review: Where Human Expertise Still Wins

Manual underpayment review is the traditional model. An analyst (or team) evaluates EOBs and remits, compares payment to allowed amounts, verifies contract terms, and determines whether a variance exists. Although labor intensive, manual work remains essential in several scenarios.

Strengths of manual review

• Handling complex, nuanced cases: Human reviewers can interpret ambiguous contract language, understand context around medical necessity, and recognize patterns that are difficult to encode into rules, such as payer-specific quirks or unannounced policy changes.

• High quality appeals and provider-level arguments: Strong analysts draft targeted appeals, cite specific contract sections, and reference clinical documentation or coding guidelines. These tailored appeals often drive higher recovery rates for disputed or high-dollar cases.

• Root cause discovery: Skilled staff can connect the dots between front-end eligibility issues, coding patterns, and back-end payment variances. They are often the first to identify systemic problems in registration, charge capture, or contract configuration.

Weaknesses and risks of manual review

• Limited scalability: As volume grows, manual review becomes a bottleneck. Analysts are forced to choose between depth and breadth: either focus only on high-balance claims or accept that many low and mid-dollar underpayments will never be reviewed.

• Variability and knowledge loss: Expertise lives in individual analysts. Turnover, burnout, or reassignments can materially degrade performance and institutional knowledge.

• High cost per recovered dollar: In a fully manual model, each additional dollar of recovered revenue typically requires additional FTE hours. The marginal cost of recovery can approach or exceed the value of small-balance underpayments.

When manual review is appropriate:

• Low-volume, high-complexity environments (for example, specialized surgical lines, transplant programs, or small but highly specialized practices).

• Escalated disputes where a payer has rejected automated or template-based appeals.

• Pilot phases where an organization is still learning payer behavior and defining its underpayment taxonomy and workflows.

In short, manual review excels at complex, ambiguous work that requires judgment. It is inefficient as the primary mechanism for high-volume detection.

Automation for Underpayment Detection: What It Can and Cannot Do

Automation refers to rule-based engines, contract modeling tools, or AI-enabled platforms that systematically compare expected reimbursement to actual payment. Properly configured, these tools can review every adjudicated claim and flag discrepancies in near real time.

Core capabilities of automation in underpayment recovery

• Contract modeling and rate comparison: Systems can ingest payer contracts, fee schedules, and reimbursement rules, then compute the expected allowed amount for each CPT, DRG, or revenue code. Variances beyond configured thresholds are flagged automatically.

• Pattern recognition: Analytics engines can spot recurring payer behavior. For example, a payer that consistently underpays a subset of high-cost imaging codes by a fixed percentage or always misapplies a particular modifier.

• Workflow automation: Underpayment flags can feed directly into work queues, create appeal tasks, or generate template appeals, reducing manual triage time and accelerating recovery.

Benefits for cash flow and operations

• Coverage of 100 percent of claims: Instead of sampling, automation checks every paid claim, which significantly increases visibility into true underpayment rates and opportunities.

• Faster identification: Many organizations move from discovering issues months later to identifying variances within days of adjudication. Faster detection shortens the appeal cycle and reduces timely filing risk.

• Labor leverage: Analysts can focus on the most material or complex variances. This allows RCM leaders to maintain or even reduce staffing while increasing total recovered dollars.

Limitations that leaders must plan for

• Rule configuration and maintenance: Contract terms change, new codes appear, and payers update policies. Without disciplined governance and maintenance, automated rules become stale and inaccurate.

• Difficulty with ambiguity: Automation struggles with “soft” issues such as vague clinical policy application, nuanced medical necessity disputes, or inconsistent payer interpretation of coding guidelines.

• False positives and noise: Poorly tuned thresholds can generate large volumes of low-value variances, overwhelming staff and reducing trust in the system.

Example metric view: After implementing a contract modeling engine, a regional health system increases underpayment identification by 70 percent but initially recovers only 30 percent of flagged dollars because queues are flooded with low-dollar mismatches and no prioritization rules have been configured. Without careful workflow design, automation can shift the problem from “no visibility” to “too much unstructured visibility.”

Designing a Hybrid Underpayment Model: A Practical Operating Framework

For most independent groups, hospital RCM teams, and billing companies, the best approach is not “automation or manual.” It is a hybrid model that assigns the right type of work to the right resource: engines do the detection at scale, humans handle judgment-heavy resolution and continuous improvement.

A practical hybrid framework can be organized into four layers:

1. Automated detection and categorization

Automation evaluates all payments, compares them to expected allowed amounts, and then categorizes variances by type, payer, code, and dollar value. Examples of variance categories include:

• Unit-level rate variances (contract price vs paid rate).

• Bundling or unbundling discrepancies.

• Incorrect application of modifiers.

• Site-of-service or place-of-service discrepancies.

Key KPI: percentage of adjudicated claims that undergo automated contract comparison and the total variance dollars identified per month.

2. Risk-based prioritization rules

Not every underpayment is worth the same operational investment. Build a simple scoring model that ranks variances by:

• Financial impact: dollar value of the variance and frequency across claims.

• Payer behavior: historical response to appeals, timely filing windows, and likelihood of future recurrence.

• Complexity: whether the issue is rule-based (for example, mispriced allowable) or dispute-based (for example, clinical denial masquerading as a rate issue).

High-score items route to experienced analysts. Low-score, highly standardized issues might be handled via automated or semi-automated appeals, depending on payer regulations and organizational risk tolerance.

3. Manual investigation and resolution

Analysts focus on:

• Confirming the variance in complex scenarios, including reviewing contracts, payer bulletins, and clinical documentation.

• Drafting targeted appeals, including citation of contract provisions, coding references, and calculation of expected reimbursement.

• Collaborating with coding, CDIs, and contracting to adjust internal practices when the root cause lies inside the organization.

This layer is where human expertise is critical. The goal is not just to win individual appeals but to identify patterns that can be addressed at scale.

4. Continuous feedback into rules and contracting

Insights from manual work should regularly feed back into:

• Automation rules: If analysts repeatedly resolve the same type of underpayment using the same logic, that resolution pattern is a candidate for rules-based automation.

• Contract negotiations: Aggregated data on payer underpayment frequency, average variance per claim, and appeal success rates provide leverage during contract renewal discussions.

• Front and mid-cycle corrections: If underpayments often result from coding, documentation, or prior authorization issues, those upstream workflows should be redesigned.

RCM leaders can treat this hybrid model as an operating blueprint, then adjust the depth of each layer based on staffing, volume, and technology maturity.

Sizing the Right Mix: How Practice Size and Claim Volume Should Guide Your Decision

There is no one-size-fits-all answer to how much automation versus manual capacity you need. The right mix depends on claim volume, payer diversity, service mix, and available capital for technology.

Small independent practices and low-volume specialties

For organizations with modest monthly claim counts and a limited payer mix, a highly disciplined manual process may be sufficient, particularly if they operate on a narrow budget.

• Focus: Target high-dollar claims and high-risk payers. Build simple spreadsheets or lightweight tools to track repeated variances.

• Risks: Without at least semi-automated reports, it is easy to underestimate the total underpayment footprint. Sampling bias can skew decisions.

• Practical step: Start with structured manual audits by payer and code set, then consider targeted automation later if trends justify the spend.

Mid-size groups, multi-specialty practices, and regional hospitals

Once monthly claim volume reaches tens or hundreds of thousands, fully manual detection is no longer realistic.

• Focus: Implement contract modeling or rules-based automation for the top payers and highest revenue generating service lines first, then expand coverage.

• Staffing: Maintain a dedicated underpayment team that manages complex cases, oversees rule tuning, and partners with contracting.

• KPIs: track recovered underpayment dollars per FTE and underpayment identification as a percentage of net revenue.

Health systems and high-volume billing companies

At very large scale, automation is no longer optional. It is the only way to achieve complete visibility across millions of claims.

• Focus: Integrate contract models into your core billing platform or layer specialized recovery technology over your existing RCM stack. Standardize escalation criteria and appeal templates across entities.

• Governance: Establish a formal underpayment steering committee that includes contracting, compliance, RCM operations, and IT.

• Risk: Large organizations face higher reputational and compliance risk if automated rules or appeals are not properly validated. Strong quality controls are critical.

Across all sizes, leaders should ask a simple question: “What is the smallest combination of technology and talent that gives us adequate coverage and control over underpayments without overwhelming staff or budgets?”

Key KPIs and Reporting Practices to Keep Underpayments Under Control

Whether you rely more heavily on manual review or automation, your underpayment program will fail without disciplined measurement. Executive reports should separate underpayments from broader AR and denial metrics.

Foundational KPIs

• Underpayments as a percentage of net revenue: Total identified underpayment variance divided by net revenue, by month and by payer.

• Recovery rate: Dollars recovered divided by total dollars identified, segmented by variance type and payer.

• Time to resolution: Average days from variance identification to final resolution or write-off.

• Staff productivity: Recovered underpayment dollars per FTE per month.

Operational reporting practices

• Break out underpayment trends by payer and service line so that contracting and service line leaders can take ownership.

• Track a separate queue for aged underpayments that are at risk of falling outside appeal windows.

• Monitor automation quality with periodic back-testing: sample claims with no identified variance and verify that the system is not missing critical patterns.

Over time, your goal should shift from recovering underpayments after the fact to reducing their occurrence through contract clarity, payer accountability, and upstream workflow improvements.

Putting It All Together: How to Move From Ad Hoc Recovery to a Strategic Program

Underpayment recovery is no longer a “nice to have” back-office activity. It is directly tied to margin protection, payer relationships, and the credibility of your revenue cycle function. The decision is not simply between automation and manual review. The real question is how to combine them into a repeatable, measurable operating model.

For most organizations, the practical path looks like this:

• Quantify your current underpayment exposure using structured audits of a defined payer and service line set.

• Design a basic hybrid framework, with automation handling detection and humans handling complex resolution and continuous learning.

• Prioritize investments based on revenue impact, not generic technology features. Focus first where payer behavior and volume create the largest opportunity.

• Build the feedback loop between underpayment analytics, contracting, coding, and front-end workflows so that the same issue is not fought claim by claim indefinitely.

If you do not have the internal capacity to stand up this kind of program, collaboration with experienced RCM partners can accelerate progress and reduce risk. One of our trusted partners, Quest National Services, specializes in full-service medical billing and revenue cycle support for organizations that need better control over denials, underpayments, and payer performance.

If your organization is ready to reduce hidden leakage, strengthen payer compliance, and build a sustainable underpayment strategy, start by assessing your current process gaps and technology readiness. For a deeper discussion about your specific environment and potential next steps, contact our team.