Explore how artificial intelligence (AI), advanced analytics, and modern digital platforms are driving Medicaid modernization, enabling states to improve efficiency, strengthen compliance, and deliver better health outcomes. This session highlights real-world strategies for applying AI across claims, clinical workflows, system transformation, and population health—while ensuring responsible, human-centered implementation.
What you'll learn:
- How policy and regulatory pressures are accelerating Medicaid transformation
Understand how CMS interoperability mandates, prior authorization reforms, and emerging legislation are driving the need for AI-powered, API-first Medicaid systems. - How AI is improving Medicaid operations and efficiency
Explore real-world use cases across claims processing, system modernization (MES/DDI), clinical workflows, and call center operations to reduce administrative burden and improve accuracy. - How to apply AI responsibly in healthcare
Learn how states are adopting human-in-the-loop AI models, governance frameworks, and initiatives like the Safe AI and Medicaid Alliance (SAMA) to ensure transparency, compliance, and trust. - How data and analytics enable better population health outcomes
Discover how integrated data models and social determinants of health (SDOH) insights support predictive analytics, care coordination, and equitable healthcare delivery.
Participants
- Eliza Baldwin, Vice President, Sales, Acentra Health
- Daniel Hallenbeck, Vice President, Strategy, Acentra Health
- Sean Harrison, Chief AI & Analytics Officer, Acentra Health
- Ben Novinger, Director, Health Services Research, Acentra Health
Read the transcript
Note: This is a polished transcript of the full session and is not intended to be a verbatim record.
Opening and policy context
[Approx. 00:00] The session opens by introducing the urgency of modernizing Medicaid with AI, analytics, and digital platforms, highlighting how emerging technologies are reshaping healthcare delivery and operations.
[Approx. 02:30] Speakers outline the session’s goal: to demonstrate how AI-driven strategies can improve cost efficiency, workforce productivity, and health equity across Medicaid programs.
Policy drivers and modernization urgency
[Approx. 07:00] The panel discusses key policy drivers, including CMS interoperability rules, prior authorization reforms, and T-MSIS reporting requirements, which are pushing states toward real-time data exchange and API-first architectures.
[Approx. 10:00] New legislation such as OB3 introduces workforce requirements and increased oversight, adding pressure on Medicaid systems already facing budget constraints and staffing shortages.
Legislative impact and AI opportunities
[Approx. 10:30–12:00] The discussion highlights Medicaid work requirements and the $50 billion rural health transformation program, showing how AI can automate verification, improve access, and enhance care delivery in underserved areas.
Evolution of AI and emerging capabilities
[Approx. 12:00–16:00] Panelists explore the rapid advancement of generative AI and agentic AI, describing how these technologies enable multi-step reasoning, workflow automation, and improved decision-making in Medicaid systems.
[Approx. 17:00–20:00] Frontier AI models demonstrate advanced problem-solving capabilities, signaling a shift toward intelligent, adaptive systems that can handle complex healthcare operations.
Human-AI collaboration and workforce transformation
[Approx. 21:00] A key theme is human-AI collaboration, where AI automates repetitive tasks while clinicians and staff maintain oversight and decision-making authority—ensuring trust and accountability.
[Approx. 23:00] AI is framed as a transformational force comparable to foundational innovations, requiring leaders to rethink how Medicaid systems operate and deliver care.
Core AI use cases in Medicaid
[Approx. 25:00] Five major areas of AI application are identified:
- Medicaid Enterprise System (MES/DDI) modernization
- Claims processing and fiscal operations
- Clinical decision support and utilization management
- Population health and data analytics
- Governance through SAMA
System modernization and operational efficiency
[Approx. 26:30–29:00] AI is transforming system modernization projects by automating requirements mapping, documentation, testing, and compliance—reducing timelines and implementation risk.
[Approx. 29:00–32:00] In claims processing, AI improves accuracy and efficiency through automation, real-time validation, and transparent audit trails, enhancing provider and member experience.
Clinical workflows and care delivery
[Approx. 32:30–35:00] AI supports clinical decision-making by summarizing medical records, surfacing evidence, and drafting communications—while maintaining clinician control (“human in the lead”).
Data integration and population health analytics
[Approx. 35:00–38:00] The session introduces a whole person, whole population analytics model, integrating claims data with social determinants of health to generate actionable insights and improve care outcomes.
Governance, trust, and responsible AI
[Approx. 39:00–42:00] The Safe AI and Medicaid Alliance (SAMA) is presented as a collaborative framework for ensuring safe, ethical, and compliant AI adoption across states and organizations.
Closing takeaways and future outlook
[Approx. 43:00–45:00] The session concludes that AI represents a turning point for Medicaid, enabling faster modernization, improved efficiency, and better health outcomes. With policy mandates like OB3 and increasing fiscal pressure, states must act now to adopt AI-driven, data-enabled strategies while ensuring strong governance and collaboration through initiatives like SAMA.
Practical implementation and safe AI adoption
[Approx. 45:00–50:00] In Q&A, speakers highlight how states are operationalizing AI through low-risk, high-impact use cases, including document generation, call center support, and workflow automation. Emphasis is placed on aligning AI use with state-specific policies, risk tolerance, and compliance requirements, reinforcing a “start practical, scale responsibly” approach.
Collaboration, real-world use, and next steps
[Approx. 50:00–55:00] The discussion reinforces the importance of cross-state collaboration and shared learning, with SAMA providing tools, frameworks, and use case libraries to accelerate adoption. Speakers also highlight real-world AI usage as an iterative, collaborative process—helping teams think, refine strategy, and improve decision-making—underscoring that successful implementation depends on both technology and how people use it.