Scaling MRD Reimbursement Requires More Than Standard Billing Workflows

In the first article in this series, we explored why MRD testing reimbursement strategy must be built into market access planning early. This article focuses on the revenue cycle management capabilities laboratories need to scale MRD testing while maintaining reimbursement visibility, operational control, and financial performance. The third article expands the conversation to the care team, examining how oncology pharmacists can help connect MRD-driven clinical decision-making to documentation, access, and reimbursement readiness.

How Can Laboratories Scale MRD Testing Reimbursement?

As MRD testing volume grows, reimbursement operations become harder to manage manually. For clinical laboratories, each patient may require multiple tests over time, and each test may be subject to different coverage, authorization, frequency, documentation, or payment rules.

For laboratories, the operational question is clear: how do you scale MRD testing without adding excessive manual work, having to confront missing payment issues, or losing visibility into payor behavior?

Payors may reimburse MRD testing in different ways depending on the contract, coverage policy, testing sequence, and patient scenario.

That level of variability requires reimbursement analytics, configurable workflows, automation, AI-enabled policy support, and clear operational visibility.

Monitor Expected Payment to Catch Reimbursement Issues Early

One of the most important disciplines in MRD testing reimbursement is comparing expected reimbursement to actual allowed amount.

Without expected payment monitoring, laboratories may not quickly identify underpayment, incorrect fee schedule loading, contract implementation issues, or overpayment risk. Reimbursement analytics can help teams identify variances between expected and actual allowable reimbursement before those issues result in significant financial leakage.

For out-of-network reimbursement or arrangements without a clearly defined contracted rate, historical reimbursement data can also establish expected payment benchmarks by payor, plan, and test type. Laboratories can also compare reimbursement rates to market benchmarks to better understand how individual payor rates align with the broader market.

Detect Payor Policy and Reimbursement Shifts Early

MRD testing reimbursement is highly dependent on payor policy and interpretation. A payor shift from favorable to more restrictive coverage can quickly increase investigational, medical necessity, or non-covered denials.

By monitoring denial patterns, reimbursement trends, and payor policy and behavior changes, laboratories can identify emerging issues earlier and engage market access or payor relations teams before the financial impact becomes material.

Reimbursement and appeals analytics can also show where appeals are successful, where additional evidence is needed, and where broader payor engagement may be more effective than continuing to address claims individually.

Prevent Documentation Gaps Before Claims Go Out

Incomplete documentation from ordering providers remains one of the most common operational challenges in MRD testing.

Specimens may arrive with incomplete orders or insufficient clinical information to support prior authorization or medical necessity. If key clinical information is missing, reimbursement can be delayed or denied.

Client error analysis helps laboratories move from reactive follow-up to proactive improvement. By identifying ordering providers who frequently submit incomplete information, laboratory RCM teams can target provider education where it will have the greatest impact.

Rather than resolving documentation issues one claim at a time, laboratories can build common requirements into requisitions, onboarding materials, provider education, and intake workflows.

Configure Billing Workflows Around MRD Testing Reimbursement Models

MRD testing does not always fit traditional laboratory billing models. Reimbursement arrangements may include:

• Fee-for-service reimbursement
• Bundled payments
• Flat-rate arrangements over a defined testing period
• Single covered test models

Each model creates different operational requirements. Some subsequent tests may need to be submitted with a zero-dollar charge for tracking purposes. Others may need to be suppressed entirely. Frequency limitations may also require historical testing data to determine whether a claim should be submitted.

These variations make configurable billing technology essential. Laboratories need workflows that can manage procedure code combinations, frequency limits, bundled arrangements, zero-dollar billing, claim suppression, and reimbursement logic across payors and contracts.

Scale Prior Authorization and Appeals with AI-Enabled RCM

Prior authorization is one of the most labor-intensive areas of MRD testing reimbursement. Requirements can vary by payor, CPT code, patient plan, indication, and available documentation, making prior authorization automation an important opportunity for laboratories managing growing test volumes.

AI-enabled RCM can help teams apply payor-specific policy intelligence more consistently and manage specialized work at scale. AI-enabled tools integrated within your RCM platform can help reimbursement teams:

• Interpret payor-specific policies
• Determine whether prior authorization may be required
• Identify documentation needs
• Compare available records against policy criteria

When the available documentation appears to satisfy the patient’s plan requirements, the workflow can identify the case as having a higher likelihood of approval. If key information is missing, it can flag those gaps before submission. This type of prior authorization automation can help reimbursement teams focus manual review on cases requiring clinical judgment or additional documentation.

The same policy and documentation intelligence can also support appeals by helping teams draft tailored responses that reflect the patient’s plan policy, available clinical documentation, and supporting evidence. AI-enabled revenue cycle management tools can help teams scale appeal preparation work while keeping reimbursement specialists responsible for review and final decisions.

AI does not replace reimbursement expertise or guarantee outcomes. It helps teams scale specialized work that would be difficult to manage manually at high volume.

Make Patient Financial Clarity Part of the Workflow

MRD testing also requires clear patient engagement and financial transparency. Patients increasingly expect to understand coverage and potential out-of-pocket responsibility before testing proceeds.

Real-time eligibility verification, cost estimation, digital communication, virtual assistants, and IVR can support a more transparent patient experience while helping reimbursement teams reduce confusion and manual follow-up.

The Path Forward: Building a Scalable Revenue Cycle for MRD Testing

MRD testing offers significant clinical value but creates substantial operational complexity. Laboratories that rely on manual workflows alone may struggle to keep pace with payor variation, authorization requirements, appeal volume, documentation gaps, and specialized reimbursement models.

To scale effectively, laboratories need connected intelligence across:

• Reimbursement analytics and expected payment monitoring
• Payor behavior tracking
• Provider documentation improvement
• Configurable billing workflows
• AI-enabled prior authorization support
• Data-driven appeal strategies
• Patient financial transparency

As MRD testing expands, operational excellence will be essential to sustainable reimbursement. The laboratories best positioned for success will be those that combine advanced diagnostics with equally sophisticated reimbursement operations.

Next in the series: Blog three brings the care team into the conversation, exploring how oncology pharmacists can help connect MRD-driven clinical decision-making to documentation, access, and reimbursement readiness.

See how XiFin Empower RCM, a purpose-built RCM platform for laboratories, helps laboratories improve reimbursement visibility, automate complex workflows, support AI-enabled prior authorization and appeals, and create a more transparent patient financial experience.