Sarah Chen became Medicaid Director seven months ago. Her predecessor had spent eighteen months building a compliance-oriented work requirement system: an online portal, automated termination processing, a modest call center, and standard appeal procedures. The system was nearly complete. It would meet the December 2026 deadline. It would also, based on every available projection, terminate between 15 and 25 percent of the state’s 380,000 expansion adults in the first year, the majority of whom would be working or exempt. Director Chen inherited a system designed to catch non-compliance and a timeline that left perhaps ten months to pivot toward recognition. She could not start over. She did not have the budget, the legislative authority, or the time to build a complete recognition infrastructure from scratch. What she could do was triage: identify the highest-impact recognition investments, sequence them against the remaining months, and build as much recognition capacity as the constraints allowed.
Her first call was to the state’s workforce development agency asking how quickly they can share unemployment insurance wage data with the eligibility system. The answer: four months to establish data sharing agreements, build secure transfer protocols, and validate matching algorithms. That single investment would automatically verify compliance for an estimated 60 percent of expansion adults before they were required to submit a single document. Her second call was to the state’s largest MCOs asking what navigation capacity they can deploy. The MCOs, acutely aware that risk adjustment degradation from mass terminations would cost them far more than navigation investment, committed to funding 150 community health workers across the state by implementation date. Her third decision was to add phone and mail channels to the online portal, not because she believed phone and mail were optimal verification methods, but because the alternative was terminating every person who could not use a website.
The first four months of recognition infrastructure development must focus on investments that provide the largest reduction in wrongful termination risk with the shortest implementation timelines. Data matching agreements represent the single highest-return investment available to any state. Establishing formal agreements between the Medicaid agency and the state workforce agency for unemployment insurance wage data, new hire reporting data, and employer information provides the foundation for automated verification. Most states already share some data between these agencies for other program purposes. The incremental effort involves extending existing agreements to cover work requirement verification, establishing data transfer schedules aligned with verification timelines, and validating matching algorithms against known compliance data.
The technical work is straightforward for states with modern eligibility systems: define the data elements needed including Social Security number, quarterly wages, employer identifiers, hire dates, establish secure transfer protocols typically SFTP or API-based, build matching logic that accounts for name variations and identifier discrepancies, and test against historical data to estimate match rates. Cross-program data agreements with SNAP, TANF, and workforce development programs provide additional verification capacity. Members already meeting work requirements under SNAP Employment and Training or TANF work participation programs are almost certainly meeting Medicaid work requirements. Social Security Administration data sharing for disability benefit identification enables automatic exemption for SSI and SSDI recipients. These data feeds already exist for Medicaid eligibility determination purposes. Extending them to work requirement exemption processing requires policy direction and system configuration rather than new infrastructure.
Adding phone and mail channels to online portal systems requires modest technology investment and more substantial staffing investment. A phone verification line requires trained staff who can walk members through verification questions, identify potential exemptions, and document responses. Mail processing requires intake procedures, data entry capacity, and processing timelines fast enough that mailed submissions prevent termination. In-person verification through existing community organizations including FQHCs, libraries, and social service offices requires partnership agreements and minimal technology support.
Outreach infrastructure must be established before requirements activate. Members need to know about the requirements, understand what they need to do, and know where to get help. States that activated work requirements without adequate outreach, as Arkansas did, found that one-third of affected members had never heard of the requirements. Outreach campaigns through mail, phone, text, provider offices, MCO communications, and community organizations must begin months before the compliance deadline. Provider notification and preparation for attestation pathways should begin during Phase 1 even though attestation systems may not be fully operational until later phases. CBO partnership agreements with trusted intermediary organizations should be formalized during this phase. Identifying organizations that serve homeless populations, people with serious mental illness, people in recovery from substance use disorders, domestic violence survivors, and other exemption-eligible groups, and establishing memoranda of understanding that authorize these organizations to submit documentation on behalf of their clients, builds the intermediary network before it is needed.
States building recognition infrastructure face several strategic choices that affect cost, timeline, and effectiveness. The build-versus-buy decision for verification systems depends on state technology capacity and timeline. States with modern, modular eligibility systems can build data matching and verification features incrementally. States with legacy systems face longer development timelines and higher risk of integration failure. State-operated versus MCO-delegated navigation represents a structural choice with significant implications. State-operated navigation ensures consistency across the entire expansion population but requires the state to build a workforce it has never previously employed. MCO-delegated navigation leverages existing MCO community health worker infrastructure and aligns financial incentives since MCOs lose money when members are wrongly terminated but creates variation across MCOs.
Centralized versus distributed exemption processing affects both accuracy and accessibility. Centralized processing through a single state unit ensures consistent application of exemption criteria but creates bottlenecks when volume exceeds capacity. Distributed processing through MCOs, provider organizations, and community intermediaries increases capacity and accessibility but risks inconsistent decisions. Investment sequencing when resources are constrained requires ruthless prioritization. A state that can fund only three of five recognition components should invest in data matching, phone and mail channels, and human review before termination. These three investments prevent more wrongful terminations per dollar than navigation workforce, provider attestation infrastructure, or advanced analytics.
States implementing recognition systems need performance benchmarks that measure whether recognition is actually occurring. The recognition rate measures the percentage of working expansion adults correctly identified as compliant without requiring individual documentation submission. A well-functioning recognition system should achieve a 60 to 70 percent automated recognition rate through data matching alone, rising to 85 to 90 percent when self-reporting channels and exemption identification are included. The false negative rate measures the percentage of compliant people incorrectly classified as non-compliant. Arkansas achieved a false negative rate of approximately 85 percent. A well-functioning recognition system should achieve a false negative rate below 15 percent.
The exemption capture rate measures the percentage of exemption-eligible members who actually receive exemptions. If administrative data analysis suggests that 50,000 members likely qualify for medical exemptions but only 20,000 receive them, the exemption capture rate is 40 percent and the system is failing to recognize 30,000 members’ exemption eligibility. Time-to-recognition measures the number of days between when a qualifying activity occurs and when the system recognizes that activity as compliance. A member who starts a new job on January 15 should have their compliance recognized within 30 to 60 days, not 120 to 180 days. The churn rate measures the frequency of termination-and-re-enrollment cycling. A churn rate above 10 percent indicates that the verification system is generating false terminations at a rate that creates significant administrative cost and member harm. The target should be a churn rate below 5 percent.
Ten months is not enough time to build perfect recognition infrastructure. No timeline is enough for perfection. But ten months is enough time to build adequate recognition infrastructure if investment begins immediately and priorities are clear. The challenge is not knowing what to build but deciding to build it. Every state Medicaid director in the country understands the choice between compliance and recognition systems. The evidence is clear. The design principles are established. The economic case overwhelmingly favors recognition. The question is whether states will make the investments the evidence demands or whether political incentives, budget constraints, and institutional inertia will produce compliance systems that replicate Arkansas’s results.