Ohio’s Department of Medicaid runs its expansion population through state unemployment insurance wage records in a test batch during summer 2026. The results arrive within hours. Of the 712,000 adults enrolled in Medicaid expansion, approximately 480,000 show wages in the unemployment insurance database confirming employment meeting or exceeding the 80-hour monthly threshold. Another 85,000 are receiving Social Security disability benefits. Roughly 40,000 are already meeting work requirements through SNAP Employment and Training or TANF work participation. Before a single expansion adult has submitted a single document, before anyone has logged into a portal or called a help line, Ohio has verified compliance or exemption for approximately 85 percent of its expansion population.
Georgia takes a different path. The state’s Pathways to Coverage program, operational since 2023, initially required monthly online reporting through a web portal. Enrollment fell catastrophically short of projections, with only 5,573 members enrolled by September 2024 against an eligible population exceeding 300,000. The state pivoted to simplified annual reporting, but the original design philosophy, wait for individuals to come to the system rather than having the system go to the data, shaped early outcomes. Georgia spent more than twice as much on administrative costs as on actual healthcare in the program’s first year, according to the Government Accountability Office. Both states implemented work requirements. One invested in recognition infrastructure. One did not. The difference is not ideology but architecture.
Recognition systems are not philosophical abstractions but technical systems with specific components, design requirements, and integration challenges. The question facing every state is not whether to believe in recognition but whether to build the data infrastructure, verification channels, timing mechanisms, and exception handling systems that make recognition operational.
The most powerful recognition tool available to states is data they already possess. Every state maintains unemployment insurance wage records documenting quarterly earnings for workers covered by the UI system. Every state operates a new hire reporting database under federal mandate. Most states share data across public benefit programs including SNAP, TANF, and workforce development systems. Social Security Administration data identifies disability benefit recipients. Educational institution enrollment records document students. The principle underlying data matching is straightforward: verify first, then ask. Before requiring any individual to submit documentation, the system checks what it already knows.
Unemployment insurance wage records represent the richest data source. Employers report quarterly wages to state workforce agencies for every covered employee. These records capture approximately 94 percent of wage and salary employment, missing primarily agricultural workers, domestic employees, some religious organization employees, and self-employed individuals. For the covered population, wage records provide definitive evidence of employment. The limitation of UI wage data is temporal. Reports are filed quarterly, typically 30 days after the quarter ends, creating a lag between when work occurs and when documentation appears in state systems. A person working in January may not have their wages confirmed in state databases until May. Recognition systems must account for this lag by treating the absence of current-quarter data as absence of data rather than absence of work.
State new hire databases provide more timely data than quarterly wage reports. Federal law requires employers to report new hires within 20 days of hire date. These records confirm that someone has started employment even before their first quarterly wage report appears. Cross-program data sharing multiplies recognition capacity. A member already meeting SNAP work requirements is likely meeting Medicaid work requirements as well. TANF work participation records, workforce development program enrollment, and vocational rehabilitation case data all contain evidence of qualifying activities. States that build data sharing agreements across these programs can recognize compliance through channels the member never interacts with directly.
The technical requirements for effective matching are substantial but not unprecedented. Systems need secure data transfer protocols, standardized identifier matching, deduplication algorithms for records with minor discrepancies, and audit trails documenting match results. States that have modernized eligibility systems for the Affordable Care Act already possess much of this infrastructure. States operating legacy systems face larger investments but can leverage federal matching funds at the 90/10 rate for system modernization. Privacy and data governance present legitimate concerns that states must address proactively through data sharing agreements authorized under state law or federal program rules, navigating Computer Matching and Privacy Protection Act requirements while building matching infrastructure.
Data matching will not capture everyone. Gig economy workers, cash economy participants, people working for very small employers, and those engaged in qualifying activities other than formal employment may not appear in administrative databases. For these populations, self-reporting remains necessary. But the design of self-reporting systems determines whether they function as recognition tools or as compliance barriers. Arkansas’s 2018 implementation required monthly online reporting through a single web portal. Members who lacked internet access, who could not navigate the portal interface, or who did not know the reporting requirement existed lost coverage regardless of their work status. The portal-only design guaranteed that anyone unable to use that specific technology would fail verification.
Recognition-oriented self-reporting systems provide multiple channels precisely because no single channel reaches the entire population. Different people communicate through different means. Different circumstances make different channels accessible or inaccessible. Phone reporting with live assistance serves populations comfortable with verbal communication but struggling with written forms or digital interfaces. Mail-in options with adequate processing time serve populations with limited technology access, particularly in rural areas where broadband availability remains inconsistent. The key design element is processing time accepting postmark dates rather than receipt dates, providing return envelopes with pre-paid postage, and maintaining processing timelines that allow mailed submissions to prevent termination.
In-person verification through partner organizations serves populations that benefit from face-to-face assistance. Federally Qualified Health Centers, community action agencies, public libraries, and other trusted institutions can serve as verification access points where members submit documentation with staff assistance. This channel is particularly valuable for populations with limited English proficiency, cognitive impairments, or complex circumstances difficult to communicate through phone or portal interactions. Text-based check-ins serve as confirmation mechanisms for populations comfortable with mobile technology but who may not complete longer online forms.
Temporal flexibility represents the third architectural pillar. Work requirements assume steady monthly employment, but actual work patterns are highly variable for many expansion adults. Seasonal workers, gig economy participants, workers with fluctuating hours, and people moving between jobs may work well above requirements on an annual basis while falling short in specific months. Recognition systems account for this variability through annualization, allowing members to bank excess hours from high-earning months against shortfalls in low-earning months, good cause suspensions for temporary work interruptions due to illness, family emergency, or employer closure, and prospective verification accepting expected future work to maintain coverage during the gap between jobs.
Exception handling and human review represent the final architectural component. Automated systems produce errors. Data matching generates false negatives when records fail to match despite members actually working. Self-reporting channels produce false positives when members misunderstand questions or submit incorrect information. Recognition systems require mechanisms to identify and correct these errors before they cause coverage loss. Exception handling includes decision trees for verification pathways routing each member appropriately, escalation protocols defining what happens when standard processes do not resolve a case, feedback loops tracking outcomes to identify patterns indicating system dysfunction, and real-time dashboards monitoring recognition rates with key metrics showing percentage verified through automated data matching targeting 60 to 70 percent, percentage verified through self-reporting channels targeting 20 to 25 percent, percentage requiring exception handling targeting 5 to 10 percent, and percentage terminated after exhausting all pathways targeting under 5 percent.
Recognition is not magic, wishful thinking, or an unfunded mandate to be kind to people. It is engineering. Specific, identifiable technical investments produce specific, measurable recognition outcomes. States that invest in data matching infrastructure will automatically verify compliance for the majority of their expansion populations. States that provide multiple verification channels will capture compliance among populations that data matching misses. States that implement temporal flexibility will recognize compliance among variable workers whose annual effort exceeds requirements even when individual months fall short. States that build exception handling systems will identify and correct errors before they cause coverage loss. States that do none of these things will generate terminations, terminating working people alongside non-working people, producing coverage loss rates that far exceed actual non-compliance rates, spending more on downstream costs including emergency care, re-enrollment processing, appeals, and uncompensated hospital care than they would have spent on recognition infrastructure.