Healthcare providers rely on expected coverage and reimbursement when making real-time decisions about patient care, including the use of medical devices. However, in Medicaid, uncertainty regarding patient eligibility at the point of care introduces risk, which later manifests as denied claims or billing discrepancies.
Addressing Medicaid’s Ongoing Challenge
Medicaid provides coverage for over 80 million Americans, and its vast scale presents significant operational hurdles. A persistent concern for healthcare providers is the reliability of eligibility data, which directly impacts reimbursement for care.
In the 2024 fiscal year, the Centers for Medicare and Medicaid Services reported an improper payment rate of 5.09 percent in Medicaid, totaling approximately $31.1 billion. Most of these payments were not due to fraud but rather resulted from inadequate documentation or incomplete verification during enrollment.
Since 2003, the Government Accountability Office has listed Medicaid on its High-Risk List, and it remains there today. Despite increased oversight, improper payment rates have persisted, indicating a deeper issue that cannot be resolved through enforcement alone.
Fragmented Data Causes Delays
The core issue lies in the fragmentation of data. Eligibility determination relies on information scattered across federal, state, and private systems, leading to delays and inconsistencies.
Applicants may provide incomplete information, and verification often occurs weeks later. In some cases, documentation cannot be verified during review. As a result, when a patient requires an MRI or an implant procedure, their coverage status may be unclear.
Eligible patients may experience delays or denials, while unverified data progresses through the system, leading to improper payments identified only during audits. Annually, hospitals provide over $40 billion in uncompensated care, some of which is attributed to eligible patients who were not successfully enrolled.
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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.
Device Coverage Uncertainty
This situation affects various 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.
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.
Prefill Applications With Trusted Data
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.
