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MTI Viewpoints

Insights shared by industry relative to healthcare and the advancement of medical technology.

Peter Justen is Founder and CEO of AmeriTrust Solutions. He works at the intersection of technology, data, and human-centered design to improve access to government programs and streamline administrative processes.

Hospitals and health systems make real-time decisions about patient care, including the use of medical devices, imaging, and digital therapeutics, based in part on expected coverage and reimbursement. In Medicaid, eligibility uncertainty at the point of care introduces risk into those decisions, risk that resurfaces later as denied claims, improper payments, or billing discrepancies. Closing that gap depends less on aggressive auditing and more on ensuring eligibility data is clean, verified, and available before care is delivered.

The Persistent Vulnerability in Medicaid

Medicaid covers more than 80 million Americans. Its scale creates operational challenges, but the persistent issue for providers is the reliability of the eligibility information that determines whether care will be reimbursed.

In fiscal year 2024, the Centers for Medicare and Medicaid Services reported an improper Medicaid payment rate of 5.09 percent, or roughly $31.1 billion. The majority of those payments were not tied to confirmed fraud, but to insufficient documentation or incomplete verification at enrollment. For device-intensive specialties, from imaging and implants to durable medical equipment, that translates directly into reimbursement risk.

The Government Accountability Office placed Medicaid on its High-Risk List in 2003, and it remains there today. Oversight has expanded, yet improper payment rates have remained relatively stable, pointing to a deeper issue that enforcement alone cannot resolve.

Fragmented Data, Fragmented Care Decisions

At the core of the problem is fragmentation. Eligibility determination depends on data spread across federal, state, and private systems, where income, household composition, identity, and residency are submitted through self-attestation and verified through delayed cross-checks.

This process introduces time gaps and inconsistencies. Applicants may submit incomplete information, verification often occurs weeks later, and documentation sometimes cannot be substantiated on review. By the time a patient presents for an MRI, an implant procedure, or enrollment in a remote monitoring program, coverage status may be unclear or unconfirmed.

Qualifying patients may face delays or denials, while unsupported data moves forward and produces improper payments identified only during audits, long after care is delivered. Nationally, hospitals deliver more than $40 billion in uncompensated care each year, a portion of which is tied to patients who are eligible for Medicaid but not successfully enrolled.

Where Eligibility Meets Device Use

When coverage is uncertain, providers typically respond in one of three ways: they delay care while documentation is gathered, they over-document defensively to protect against later denial, or they proceed and accept the risk of non-reimbursement.

Each response carries a cost. Delays push patients out of care pathways and can worsen outcomes. Defensive documentation consumes clinical staff capacity. Proceeding without confirmed coverage exposes the organization to write-offs and compliance issues.

The dynamic plays out across device categories. High-cost imaging, implantable devices, and remote monitoring or digital therapeutics all rest on the assumption that coverage is verifiable and stable. When it is not, decisions about which device to use are made on uncertain ground.

Why Detection Alone Cannot Keep Up

Detection has grown more sophisticated, using analytics and pattern recognition to flag anomalies in claims. These tools produce results, particularly in identifying high-risk billing patterns, but they largely operate after the fact, once a payment has already been authorized.

Recovery can be effective, with Medicaid Fraud Control Units returning roughly $4.60 for every dollar invested, but those recoveries arrive long after the device, procedure, or therapy has been delivered.

Detection also does not address the conditions that let errors into the system in the first place.

A Technology Shift Upstream

A more effective approach begins earlier. Rather than relying on self-attestation at intake, applications can arrive already populated with clean, verified data from trusted third-party sources. When that information is in place before care is delivered, clinical and billing teams act on a stable view of coverage, and reimbursement integrity follows.

Advances in healthcare IT make this practical. Data aggregation platforms, identity verification services, and authoritative federal and state data sources allow applications to be prepared with accurate information from the outset, rather than being reconstructed later through audits and appeals.

Several capabilities are central:

• Verified data prefill: Populating applications from trusted third-party sources reduces reliance on self-reported data and brings consistent, reconciled information into the record, including employment, income, identity, and household attributes.
• Authoritative data at the source: Drawing from authoritative systems before submission lets applicants confirm information rather than reconstruct it from memory, resolving discrepancies upstream of claim adjudication.
• Cross-system data coordination: Stronger interoperability between federal and state systems lets verified information flow directly into the application, reducing conflicting inputs and duplicated effort across agencies.
• Structured and auditable data capture: Standardized formats with clear data provenance make eligibility decisions defensible, reducing audit risk for device claims and downstream care.

These are not theoretical capabilities. Banking and credit underwriting already operate on pre-populated, verified application data. Healthcare can adopt similar standards as infrastructure for clinical decisions, not just enrollment.

A System Designed for Reimbursement Integrity

Reframing eligibility as a data quality issue changes how healthcare organizations invest. Instead of directing resources to audits and recovery, providers and payers can ensure applications arrive with verified information already in place, avoiding errors rather than correcting them later.

When applications enter the system pre-populated with clean, trusted data, caseworkers and clinical billing teams spend less time chasing documentation, and decisions about device use and reimbursement rest on stable ground.

Looking Ahead

Medicaid will continue to evolve, with rising regulatory complexity and scrutiny on program integrity. Technology will play a central role, but the nature of that role is shifting.

The next phase of modernization will not be defined by better detection tools applied after claims are submitted. It will be defined by how effectively the eligibility infrastructure ensures that applications, claims, and clinical decisions rest on the same verified data.

If eligibility is wrong, everything downstream, from care decisions to device utilization to reimbursement, operates on unstable ground. Strengthening eligibility at the source, with clean and verified third-party data, is how healthcare IT moves from reactive correction to proactive integrity, supporting better outcomes for patients, providers, and the innovators that serve them.

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