How AI Can Help States Sustain Medicaid Coverage Amid Proposed Federal Funding Cuts

Introduction: Navigating Medicaid Funding Uncertainty with Innovation
‍
Recent federal proposals have called for a reduction in the enhanced matching rate for Medicaid expansion populations under the Affordable Care Act (ACA) from 90% to a lower rate. This shift would place significant financial pressure on states, potentially forcing them to scale back services or find new funding sources to maintain coverage for millions of low-income adults. States are now tasked with an urgent challenge: how to maintain high levels of care and coverage with reduced federal support.

Artificial Intelligence (AI) offers a practical, solution-oriented path forward. With its capacity to analyze large datasets, streamline administrative processes, and target high-cost areas, AI can help states increase operational efficiency, reduce costs, and ensure eligible individuals are enrolled in the right programs. From predictive analytics to intelligent automation, states that invest in AI are better positioned to weather funding changes while protecting patient outcomes and financial sustainability.

This article explores how states can use AI to optimize Medicaid operations through three key areas: high-utilizer identification, enrollment streamlining, and real-world applications. Each section highlights the process, technology, and people involved, along with real-world data and case studies to demonstrate measurable outcomes.

1: Identifying and Managing High-Utilizers with AI

Process:
High-utilizers make up a small fraction of the Medicaid population but account for a disproportionately high percentage of costs. These individuals frequently use emergency departments (EDs), inpatient care, and other high-cost services, often due to unmanaged chronic conditions or lack of coordinated care. AI algorithms can analyze electronic health records (EHRs), claims data, and social determinants of health to identify these high-cost users.

Product:
Platforms like Health Catalyst’s Data Operating System (DOS) enable healthcare providers to consolidate data across care settings and apply predictive modeling to flag high-risk patients. These systems can provide insights into utilization patterns, risk scores, and gaps in care, allowing providers to intervene early.

People:
Care managers and community health workers (CHWs) are integral to acting on AI-driven insights. Once high-utilizers are identified, these professionals can offer personalized outreach, connect patients with primary care services, and help address non-medical drivers of health such as housing or transportation.

Benefits and Results:
A study from the Agency for Healthcare Research and Quality (AHRQ) found that targeted care management can reduce ED visits by 30% and inpatient admissions by 20%. UnityPoint Health reported significant reductions in unnecessary utilization using AI-enabled care management tools.

2: Streamlining Medicaid Enrollment and Renewals with AI

Process:
Enrollment and renewal for Medicaid are often bottlenecked by manual paperwork, outdated systems, and complex eligibility verification. AI can improve these processes through automation and predictive analytics.

Product:
Solutions like Firstsource’s Eligibility and Enrollment Services use machine learning to verify Medicaid eligibility, pre-populate application forms, and identify missing documentation. These systems can also monitor beneficiaries’ renewal deadlines and flag those at risk of losing coverage due to procedural errors.

People:
Eligibility specialists and Medicaid navigators benefit from AI-powered tools by spending less time on data entry and more time supporting applicants. This allows for a more personalized, responsive enrollment experience.

Benefits and Results:
States using AI to streamline enrollment have seen significant administrative savings. For example, one state that implemented automation in its eligibility process reduced processing times by 40% and increased completed applications by 25%. Fortuna Health’s Medicaid technology also helped hospitals lower uninsured patient rates and improve patient retention.

3: Real-World Applications and Case Studies

UnityPoint Health:
Used the Health Catalyst DOS platform to identify and engage high-risk patients. Results included fewer avoidable hospitalizations and improved care coordination. The organization reported a positive return on investment by reducing unnecessary utilization.

Southwestern Health Resources:
Partnered with ClosedLoop.ai to develop a predictive model that flagged patients at high risk for preventable ED visits. The intervention helped reduce ED overcrowding and avoid costly admissions.

Fortuna Health:
Deployed AI to screen for Medicaid eligibility and support timely enrollment. The platform helped hospital systems reduce uninsured rates and secure funding for ongoing patient care.

TeleTracking and NHS Trust (UK example):
Implemented AI-based bed management to streamline patient flow, resulting in reduced ED wait times and improved operational efficiency. Although a UK example, the model offers valuable insights into how AI can optimize healthcare operations.

Conclusion: A Smarter Path to Medicaid Sustainability
As the federal government considers reducing the ACA expansion matching rate, states must prepare to manage Medicaid programs with fewer resources. Cutting services or narrowing eligibility would be short-sighted and harmful. Instead, leveraging artificial intelligence provides a smarter, scalable way to navigate this transition.

By identifying high-utilizers, streamlining enrollment, and optimizing resource allocation, AI helps states maintain access to care and control costs. The real-world examples discussed in this article show that AI can deliver measurable improvements in both efficiency and outcomes.

Healthcare leaders and policymakers should act now by investing in AI-driven tools and fostering hospital-FQHC partnerships. The stakes are high, but the path forward is clear.

If you're a Medicaid decision-maker or healthcare administrator, now is the time to explore how AI can future-proof your operations. Let’s build a more efficient, equitable Medicaid system together—even in the face of federal funding changes.

References:

• AHRQ: Reducing Avoidable Hospital Utilization
• Health Catalyst: Data Operating System Case Studies
• Firstsource: Eligibility & Enrollment Services
• ClosedLoop.ai: Predictive Modeling for Healthcare
• Fortuna Health: Medicaid Enrollment Technology
• TeleTracking: Hospital Flow Optimization

‍