Medicaid Eligibility Reassessment and AI Implementation: What to Know

By early 2027, Medicaid eligibility for millions of beneficiaries across 44 states will undergo reassessment in compliance with new federal regulations.

The One Big Beautiful Bill Act (H.R.1) mandates that adults covered under the Affordable Care Act must adhere to work requirements or qualify for exemptions, while states must perform eligibility checks biannually. This heightened scrutiny, coupled with deadlines and staffing limitations, is leading many states to explore artificial intelligence (AI) as a solution. Currently, six states have implemented AI tools, and another 21 are considering their adoption.

AI Implementation and Challenges

According to an article in the JAMA Health Forum by Michelle Mello, JD, PhD, and Himaja Nagireddy, MS, AI can streamline eligibility verification if utilized wisely. However, they caution that improperly applied AI could worsen outcomes for beneficiaries. They recommend three strategies to mitigate these risks. First, deploying AI incrementally aligns it with the task complexity. For example, AI works well with structured data, like county unemployment statistics. By integrating data sources with predefined rules, AI can flag questionable cases for human review, optimizing resource allocation for intricate tasks like determining 'medical frailty.'

Fostering Competitive AI Vendors

Fostering competition among AI vendors is the second recommended strategy. Presently, 30 states continue with existing vendors to meet immediate deadlines, despite previous errors in Medicaid eligibility algorithms having led to Federal Trade Commission complaints. Introducing competitive environments through federal sandboxes and online comparison platforms could enhance vendor reliability. Additionally, shared state evaluations and regular error reporting can ensure accountability and performance improvements.

Balancing Coverage Risks

The third strategy emphasizes correcting skewed incentives within AI-driven eligibility criteria. With federal penalties set for error rates above 3% by 2029, states face pressure to enforce strict work requirements, risking wrongful disenrollments. Mello and Nagireddy advocate for clear state-determined thresholds to prevent eligible beneficiaries from losing coverage.

Practical Implications for Insurance Professionals

For insurance professionals, the AI-driven shift in Medicaid screening holds significant implications. Agents and brokers must stay informed about how AI tools could alter eligibility criteria and affect client coverage. Understanding the evolving landscape will help in advising clients effectively and in adjusting compliance strategies. Furthermore, insurers may explore similar AI innovations to streamline their operations while being vigilant about potential errors.

In closing, while the reassessment of Medicaid eligibility presents challenges, it also offers opportunities for technological advancements. If handled correctly, these innovations can improve program efficiency and accuracy, benefiting both the state systems and the millions they serve.