Coalition Forms AI Tiger Team
The Coalition for Health AI (CHAI) has launched an unprecedented fast-tracked initiative to revolutionize how artificial intelligence supports Medicaid work requirements implementation. This priority “tiger team” represents a groundbreaking approach to integrating AI technology with healthcare policy administration, specifically targeting the community engagement requirements established by the One Big Beautiful Bill Act (OBBBA).
Brian Anderson, CEO of the Coalition for Health AI, emphasizes the transformative potential of this initiative. “I strongly believe that AI can support the new eligibility determinations from the OBBBA,” Anderson stated during his interview with Fierce Healthcare. The urgency of this project reflects the tight timeline facing states and federal agencies as they prepare for comprehensive Medicaid system changes.
Rapid Response and Industry Interest
Within just one week of launching the tiger team recruitment, Anderson received responses from approximately 150 organizations, demonstrating significant industry interest in AI-driven Medicaid solutions. This overwhelming response includes participation from technology vendors, state public health officials, and employees from the Centers for Medicare and Medicaid Services (CMS).
The term “tiger team” specifically indicates high-priority tasks operating under compressed timelines, according to Forbes business analysis. This designation underscores the critical nature of implementing AI solutions before the December 31, 2026 deadline when new work requirements take effect.
Understanding OBBBA Work Requirements
Community Engagement Mandate
The Medicaid work requirement policy mandates that able-bodied adults between ages 19 and 64 must complete 80 hours monthly of qualifying activities to maintain Medicaid eligibility. These activities include traditional employment, volunteering, education, and other community engagement initiatives.
This policy represents the primary driver of healthcare savings projected by the OBBBA, with estimates suggesting $1 trillion in reduced healthcare spending over 10 years. The magnitude of these projected savings highlights why efficient, accurate implementation becomes crucial for both fiscal responsibility and beneficiary access to care.
Impact on Medicaid Population
According to KFF estimates, the Medicaid provisions within OBBBA could affect over 10.3 million Americans currently enrolled in Medicaid. This massive population impact necessitates sophisticated technological solutions to handle the increased administrative complexity while maintaining accuracy in eligibility determinations.
The scale of this change particularly affects the Medicaid expansion population of over 20 million people, requiring comprehensive overhauls of existing eligibility and verification systems across all participating states.
AI Implementation Strategy
Dual-Purpose AI Framework
CHAI’s tiger team focuses on developing AI best practices for two critical applications:
- Application Assistance: AI tools to help individuals complete complex Medicaid applications more accurately and efficiently
- Eligibility Adjudication: AI systems to assist in determining whether applicants meet new community engagement requirements
Anderson explains the efficiency rationale: “We really appreciated this was likely a space where AI could create efficiencies and help people complete their applications, but also make the appropriate adjudication determination, and trying to do that in a way that doesn’t create increased burden for states.”
Human-AI Collaboration Model
The proposed AI framework mirrors successful implementations in commercial insurance claims adjudication, where AI handles initial reviews while maintaining human oversight for complex cases. “At an adjudication level, there’s been lots of media coverage that has shown that commercial payers do involve some kind of AI in the initial review of things that need to be adjudicated, but there’s always a human in the loop,” Anderson noted.
This approach ensures that when AI systems encounter ambiguous cases or recommend denials, human reviewers make final determinations, balancing efficiency with accuracy and fairness.
Timeline and Federal Support
Accelerated Development Schedule
The tiger team operates under an accelerated timeline, aiming to develop best practice frameworks within six months and finalize them within one year. This compressed schedule represents a significant departure from CHAI’s typical community review process, which usually involves extensive public comment periods.
“This is a little bit more of a nuanced area where we may not need as much rigor as one of our more formal best practices,” Anderson explained. “This may be an opportunity to try to do something faster.”
Federal Funding and Support
States implementing the new requirements will have access to $200 million in grants for FY 2026, with funding allocation based partly on the proportion of affected individuals within each state’s Medicaid population. Additionally, CMS receives $200 million specifically for implementing these new requirements at the federal level.
The Department of Health and Human Services (HHS) must release comprehensive guidance on community engagement requirements by June 1, 2026, aligning closely with CHAI’s development timeline for AI best practices.
Technology Solutions and Best Practices
Existing AI Solutions
Anderson identifies several existing digital solutions already available in the commercial marketplace that could be adapted for Medicaid applications. “I would look to some of the commercial payers that support Medicaid. I think that they’ve been some of the ones that have been using that technology,” he suggested.
These existing solutions provide a foundation for rapid deployment, requiring adaptation rather than complete development from scratch. The tiger team will likely build upon CHAI’s existing workgroup on AI for prior authorization, which has already established frameworks for automated approvals and human-flagged reviews.
Data Integration and Training
Implementing AI for Medicaid work requirements will require new training datasets specific to community engagement verification. Anderson noted that existing workflows could be “repurposed” with “a new kind of training set of data that you would need to take these models through.”
This approach leverages proven AI adjudication technologies while customizing them for the unique requirements of work verification and community engagement tracking.
State Infrastructure Challenges
Comprehensive System Overhauls
According to the National Association of State Health Policy (NASHP) analysis, states face significant challenges implementing these requirements. “New Medicaid community engagement requirements will require comprehensive changes to eligibility and verification systems for the over 20 million people in the Medicaid expansion population.”
States must establish new data-sharing arrangements and infrastructure across multiple state programs to accurately identify individuals who meet requirements, qualify for exemptions, or are successfully completing monthly community engagement activities.
Information Technology Investments
NASHP’s analysis indicates that states will likely need to invest in new information technology infrastructure to support these complex verification and tracking requirements. This presents both challenges and opportunities for AI implementation, as states can integrate AI solutions into their infrastructure upgrades rather than retrofitting existing systems.
Industry Collaboration and CMS Partnership
Multi-Stakeholder Approach
The tiger team includes diverse stakeholders representing the entire healthcare ecosystem: technology vendors, state public health officials, and federal CMS employees. This comprehensive representation ensures that developed frameworks address real-world implementation challenges across different organizational contexts.
Anderson recently participated in discussions about AI for prior authorization at the CMS Quality Conference, engaging in fireside chats with CMS Administrator Mehmet Oz, M.D., DOGE Acting Administrator and CMS Advisor Amy Gleason, and representatives from Epic Systems and Patients for Patient Safety US.
Federal Agency Participation
While CHAI has not made formal commitments to CMS regarding these best practices, Anderson actively encourages federal participation. “We welcome the administration to participate, obviously having public sector officials, in this case, the regulators at the CMS level, participating in an industry-led effort to try to understand how we can best use AI in this space would be enormously helpful.”
Historical Context and Lessons
Post-Pandemic Medicaid Redeterminations
Anderson’s interest in AI for Medicaid administration originated during the 2023 post-pandemic Medicaid redeterminations. States, prevented from disenrolling individuals during the COVID-19 public health emergency, faced massive administrative challenges when continuous enrollment provisions ended.
“Some application forms were around 10 to 15 pages, and we found lots of people were not accurately completing those applications,” Anderson recalled. “Part of the reason was just incomplete forms, other was insufficient information.”
Successful AI Implementations
During the redetermination period, several states successfully implemented AI and digital tools to navigate the renewal process. These early successes provide proof-of-concept evidence that AI can effectively support complex Medicaid administrative processes while improving both efficiency and accuracy.
Future Impact on Healthcare
Guiding Principles for AI Implementation
Anderson emphasizes that CHAI’s fundamental principle remains using AI to help individuals, even within the context of potentially restrictive policy changes. “This seems like a good opportunity to see if we can help, if we can develop a framework that would inform an ecosystem to develop a set of apps that, ideally, where AI is helping people.”
Balancing Efficiency and Accessibility
The AI implementation aims to create efficiencies that benefit both administrators and beneficiaries. Even when applications require human review, AI can provide data summarization and analysis to help reviewers make more informed decisions more quickly.
“Hopefully, the kinds of solutions that are out there enable that human to get to the source data, to be able to make an ultimate determination,” Anderson explained. “So there’s additional efficiencies that can still happen downstream, even once a human gets into the loop.”
Working Within Legal Frameworks
Anderson acknowledges the sensitive nature of this work while emphasizing the importance of working within established legal structures. “This is obviously a very sensitive and important space that affects millions of Americans… As it relates to the bill, I mean, it is the law and we need to work within the framework of the laws made by our officials.”
The tiger team’s work represents an opportunity to leverage technology for humanitarian purposes within policy constraints, potentially reducing administrative burdens while maintaining program integrity and supporting beneficiary access to necessary healthcare services.
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