Complex healthcare policies are often challenging to implement. That's a reality states across the country will deal with as new Medicaid work requirements get rolled out next year.
The new federal rule, part of the Budget Reconciliation Act of 2025 (HR 1), requires adults enrolled under the Affordable Care Act (ACA) expansion of Medicaid eligibility to complete at least 80 hours per month of qualifying work or community engagement activities or meet exemption criteria to maintain their coverage starting Jan. 1.
A new Special Communication published Aug. 7 in JAMA Health Forum suggests that artificial intelligence may enhance implementation of this policy by helping state Medicaid agencies keep eligible individuals enrolled in Medicaid. Going forward, the use of AI can help states and local governments roll out and improve other healthcare policies, too.
"Medicaid work requirements introduce administrative complexities into an already very complex program," said Dr. Beth McGinty , professor of population health sciences at Weill Cornell and co-founding director of the Cornell Health Policy Center. "It's already very complicated to figure out how to apply for Medicaid and stay enrolled. It differs by state. Forms are confusing, and this new requirement creates additional administrative complexity for Medicaid agencies and the people who are trying to get or stay enrolled."
Taking the Burden Off Enrollees
The biggest concern is that people who are working or qualify for exemptions to the rule may still lose coverage because they have difficulty submitting documentation. The law requires states to use existing databases to verify eligibility whenever possible, but in many cases states may not be able to verify compliance or exemption status based on the information that's available to them. In that case, enrollees will need to submit documentation themselves. The Special Communication cited previous research finding that some eligible Arkansas enrollees – when presented with a similar work requirement – lost coverage due to documentation difficulties.
Dr. McGinty and her co-authors Drs. Yongkang Zhang , Fei Wang and William Schpero , as well as Dr. John Ayanian of the University of Michigan, suggest states can use AI tools to take better advantage of information they already have.
"For example, states can use AI to link Medicaid enrollment records with payroll and tax data or enrollment data for other public programs," said Dr. Schpero, an associate professor in population health sciences at Weill Cornell Medicine and associate center director of the Cornell Health Policy Center . "This would allow agencies to verify if someone is working or qualifies for an exemption without asking the enrollee to fill out more forms or submit more documentation."
AI tools embedded in online Medicaid application portals could also be used to figure out where enrollees and applicants are having the most difficulty navigating the process, he said.
Today, about a quarter of state Medicaid programs are already using AI chatbots for consumer assistance, said Dr. McGinty, which could be extended to help with implementation of the new work requirements. AI-powered digital assistants could explain requirements, answer questions and help applicants better understand the documentation they need to provide.
Dr. Schpero said AI could also help agencies learn where bottlenecks are in real time by analyzing call center transcripts, help desk messages and activity on Medicaid websites.
"Using AI to provide real-time learning for state Medicaid programs has promise," Dr. Schpero said. "For example, AI could mine anonymized call center transcripts to understand week to week what challenges enrollees are facing in trying to comply with work requirements, and state could adapt their outreach and assistance programs accordingly."
Of course, there are some caveats. States and jurisdictions have different levels of AI maturity and IT infrastructure, Dr. McGinty said. The authors argue that federal assistance will be vital for lower-capacity states to implement this approach.
Finally, like with use of other AI tools, there will have to be a human component for it to be successful, Dr. McGinty said.
"There are biases baked into our data that AI implementation will 100% reproduce here, and so having a human in the loop and really careful monitoring and oversight of AI is needed," she said. "This cannot be a 'hand it over to the bots' solution."
