Hospitals and health systems base real-time decisions about patient care—including medical devices, imaging, and digital therapeutics—on Medicaid coverage. Uncertainty at the point of care introduces risks that often reappear later as denied claims or billing errors. The fix may not lie in stricter audits but in ensuring eligibility data is accurate, verified, and available before care begins.
Medicaid serves over 80 million Americans, creating operational challenges at scale. 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. Most errors stemmed not from fraud but from missing documentation or incomplete verification during enrollment. For device-dependent specialties, imaging, implants, or durable medical equipment, this directly increases reimbursement risks.
The Government Accountability Office has flagged Medicaid as a high-risk program since 2003, yet improper payment rates have remained steady. Expanded oversight has not resolved the core issue: fragmented data. Eligibility checks depend on information scattered across federal, state, and private systems, where applicants self-report income, household details, identity, and residency. Verification often happens weeks later, leaving gaps that cannot be addressed when a patient needs an MRI, implant, or remote monitoring.
When coverage is unclear, providers face three choices: delay care while gathering documentation, over-document defensively, or proceed and accept non-reimbursement risks. Each option carries costs. Delays disrupt care pathways and worsen outcomes. Excessive documentation strains clinical staff. Proceeding without confirmed coverage exposes organizations to write-offs and compliance problems.
High-stakes risks for device-dependent care
This challenge affects all device categories. High-cost imaging, implantable devices, and digital therapeutics rely on the assumption that coverage is verifiable and stable. Without that certainty, decisions about which device to use are made on shaky ground.
Detection tools have advanced, using analytics and pattern recognition to spot anomalies in claims. These systems recover funds, Medicaid Fraud Control Units return about $4.60 for every dollar spent, but they act after payments are approved. Recovery does not address the conditions that allowed errors to enter the system initially.
A more effective strategy starts earlier. Instead of depending on self-attestation at intake, applications could arrive pre-filled with verified data from trusted third-party sources. With accurate information in place before care delivery, clinical and billing teams can act on a stable view of coverage, reducing reimbursement risk from the outset.
Healthcare IT advancements make this feasible. Data aggregation platforms, identity verification services, and official federal and state databases allow applications to be prepared with precise information upfront. Key capabilities include:
- Verified data prefill: Populating forms from trusted sources cuts reliance on self-reported data, ensuring consistent, reconciled details on employment, income, identity, and household attributes.
- Authoritative data at submission: Drawing from official systems before filing lets applicants confirm details rather than reconstruct them, resolving discrepancies before claim adjudication.
- Cross-system data coordination: Stronger interoperability between federal and state systems enables verified information to flow directly into applications, reducing conflicting inputs and redundant efforts.
- Structured and auditable data capture: Standardized formats with clear data provenance make eligibility decisions defensible, lowering audit risks for device claims and subsequent care.
How banking’s data model could fix Medicaid
These solutions are not theoretical. Banking and credit underwriting already operate on pre-populated, verified application data. Healthcare could adopt similar standards, shifting from reactive corrections to proactive integrity in clinical decisions.
Medicaid’s future will involve rising regulatory demands and closer scrutiny of program integrity. The next phase of modernization will depend on how well eligibility infrastructure ensures applications, claims, and clinical decisions rely on the same verified data.
Peter Justen, founder and CEO of AmeriTrust Solutions, argues the core issue is not oversight but the absence of real-time, verified eligibility data at the point of care. His work focuses on connecting administrative processes with clinical decisions by ensuring eligibility determinations match the reliability of credit underwriting in banking. Currently, providers must decide, such as whether to proceed with an implant or delay an MRI, without confidence in coverage status. This uncertainty, he says, is avoidable with infrastructure treating eligibility as a data integrity issue, not a compliance problem.
Justen cites the $40 billion in uncompensated care hospitals provide annually as a direct result of eligibility gaps. Many cases involve Medicaid-qualified patients facing enrollment barriers, delayed verification, missing paperwork, or fragmented data across systems. For device-heavy specialties, where reimbursement depends on prior authorization and coverage confirmation, the stakes are higher. An MRI, pacemaker implant, or remote monitoring device may proceed only to be denied later, leaving providers with unpaid bills and patients with disrupted care. The outcome is a cycle of inefficiency: providers over-document to mitigate risk, clinical staff divert time from patient care to paperwork, and payers spend resources recovering funds that should never have been at risk.
Real-time verification: The missing infrastructure
Technology exists to break this cycle, Justen says, but adoption remains inconsistent. Data aggregation platforms can pull verified employment, income, and identity records from federal and state sources, such as the Social Security Administration, state unemployment databases, or digital identity providers, before an application is submitted. This eliminates reliance on self-reported data. Cross-system interoperability could also allow Medicaid agencies to share eligibility statuses in real time with providers, ensuring coverage is confirmed before a procedure begins.
Justen points to banking as a model: credit applications are rarely approved based on handwritten forms or memory. Instead, they use pre-verified, structured data, pay stubs, tax filings, or bank statements, before decisions are made. Healthcare could apply the same rigor. For example, a hospital scheduling an MRI could pull a patient’s Medicaid eligibility directly from a state’s verified database instead of relying on a paper application submitted weeks earlier.
