Complex healthcare policies are often difficult to implement, but the next major test for state Medicaid agencies may also become a real-world experiment in artificial intelligence. Beginning Jan. 1, adults receiving Medicaid through the Affordable Care Act’s expansion will generally be required to complete at least 80 hours each month of work, education, community engagement or other qualifying activities—or meet an exemption—to retain coverage under the Budget Reconciliation Act of 2025, known as HR 1.
The rule creates a large administrative challenge for both government agencies and the people they serve. States must determine whether enrollees are meeting the monthly requirement, identify people who qualify for exemptions and provide opportunities to correct incomplete or missing records. A Special Communication published Aug. 7 in JAMA Health Forum argues that carefully designed artificial intelligence systems could help Medicaid agencies perform these tasks while reducing the paperwork burden that has historically caused eligible people to lose coverage.
“Medicaid work requirements introduce administrative complexities into an already very complex program,” said Beth McGinty, professor of population health sciences at Weill Cornell Medicine and co-founding director of the Cornell Health Policy Center. Applying for Medicaid and remaining enrolled already varies widely by state, with confusing forms, changing eligibility rules and multiple documentation requirements. Adding a work or community-engagement standard could create another point at which people lose insurance, even when they are working or legally exempt.
The central concern is not necessarily that enrollees will fail to meet the requirement, but that they will be unable to prove that they meet it. The law directs states to use existing government databases to verify eligibility whenever possible. Yet payroll records, tax information and data from other public programs may be incomplete, delayed or stored in systems that cannot easily communicate with one another. When automated verification fails, the responsibility may shift to individuals, who could be asked to submit pay stubs, exemption forms or other records within strict deadlines.
That problem has precedent. The authors point to earlier research from Arkansas, where some Medicaid recipients lost coverage after facing difficulty documenting compliance with a similar work requirement. Such outcomes are often described as procedural or administrative losses rather than deliberate cancellations: people may qualify under the policy but fail to complete a complex sequence of notices, forms and verification steps. For people with unstable housing, disabilities, limited internet access, irregular employment or demanding caregiving responsibilities, even a technically simple request can become a significant barrier.
Artificial intelligence could help by connecting information that agencies already possess. Yongkang Zhang, Fei Wang, William Schpero and John Ayanian, the authors of the Special Communication with McGinty, propose systems that could link Medicaid enrollment records with payroll and tax data or with participation records from other public programs. In technical terms, such systems would use data integration and record-matching methods to compare information across databases, while algorithms could flag likely matches, identify missing fields and route uncertain cases to human reviewers. If implemented accurately, the approach could verify employment or an exemption without repeatedly asking enrollees for documents.
AI could also be deployed at the front end of the Medicaid system, where applicants and beneficiaries interact with online portals. A digital assistant could explain the work requirement in plain language, answer questions about qualifying activities and identify which documents a person may need. More advanced tools could analyze where users abandon applications, which questions generate repeated errors and which parts of a website prompt people to seek help. That information would allow agencies to redesign confusing forms before those difficulties translate into coverage losses.
Around one-quarter of state Medicaid programs already use AI chatbots for consumer assistance, according to McGinty. Expanding these systems could provide round-the-clock guidance during a policy rollout likely to generate a surge in calls and online inquiries. AI tools could also analyze anonymized call-center transcripts, help-desk messages and website activity to detect emerging problems in near real time. If many people in a state suddenly ask how to document seasonal work, report caregiving or claim a disability-related exemption, administrators could adjust outreach materials and staff training instead of waiting for formal complaints or enrollment data to reveal the problem.
The technology, however, would not eliminate the risks created by the policy. States differ substantially in their information-technology infrastructure, data standards and capacity to develop or supervise AI systems. Poorly designed data matching could incorrectly classify a person as noncompliant, while outdated records could trigger unnecessary requests for documentation. Automated language systems may also misunderstand users with limited English proficiency, disabilities or unusual employment arrangements. Because Medicaid data contains sensitive health, financial and demographic information, agencies would need strong privacy safeguards, access controls, audit trails and procedures for correcting erroneous records.
The researchers emphasize that AI must remain an assistive technology rather than the final decision-maker. Historical data can contain racial, economic and geographic disparities, and algorithms trained on those records may reproduce them at scale. Human staff would need to review ambiguous cases, explain adverse decisions and provide accessible appeals. “This cannot be a ‘hand it over to the bots’ solution,” McGinty said, warning that continuous monitoring and human oversight will be essential. Federal assistance may also be necessary to help lower-capacity states build secure systems. If those safeguards are put in place, AI could do more than speed up administration: it could help governments identify where policy design itself is causing people to fall through the cracks.
Subject of Research: Artificial intelligence applications for implementing Medicaid work requirements and reducing administrative barriers to coverage.
News Publication Date: 7-Aug-2026
Web References: https://jamanetwork.com/journals/jama-health-forum/fullarticle/2852210
References: JAMA Health Forum Special Communication; Weill Cornell Medicine; Cornell Health Policy Center.
Keywords: Medicaid, Medicaid work requirements, artificial intelligence, health policy, health insurance, data analysis, healthcare technology, public health, administrative burden, Affordable Care Act.
