Policy & Practice | Summer 2026

technology speaks

By Stephanie Bell

Automating Eligibility: Improving Accuracy and Consistency Through Data-Driven Decision Making

I n human services, eligibility deter minations are shaped by a complex interplay of policy, data, and human judgment—and the consequences are significant. Whether administering Medicaid, the Supplemental Nutrition Assistance Program (SNAP), or state based health coverage, agencies must deliver decisions that are both timely and accurate. Delays or errors can prevent eligible individuals from accessing care and expose states to compliance risks and financial penalties. For decades, eligibility systems have relied heavily on self-attestation, backed by documentation and manual review. This often results in increased processing times and variability. Documentation may be incomplete, data outdated, and policy interpreta tion inconsistent among staff, leading to different outcomes for similar cases. As caseloads grow and regula tions evolve, eligibility operations become more difficult to manage at scale. At the center of this challenge is verification—particularly income verification, one of the most complex and consequential aspects of eligi bility determination. Many agencies continue to rely on fragmented data sources and legacy tools that are costly to maintain and difficult to integrate, limiting their ability to achieve both accuracy and efficiency. Across states, a consistent pattern has emerged: improving accuracy depends in large part on improving the quality, timeliness, and accessibility of data. Automation plays a central role in this shift, enabling faster, more accurate, and more consistent

determinations. Yet adoption remains uneven. As of early 2025, only 14 states had automated more than half of Medicaid and CHIP (Children’s Health Insurance Program) eligibility determinations at the point of appli cation—a notable gap compared to health insurance marketplaces, where automation has long supported stream lined enrollment. 1 When implemented effectively, the impact of automation is clear. In some states, most applicants receive eligi bility decisions in minutes. In Virginia’s state-based marketplace, for example, more than 70 percent of Medicaid appli cants receive instant determinations without manual review. Automation also extends beyond electronic

verification—prompting applicants to resolve discrepancies, issuing reminders, flagging potential fraud, and streamlining updates across systems. Effective automation also relies on strong governance. Agencies need to translate policy into clear, testable rules, provide visibility into how eligi bility decisions are made, and operate across complex systems. These capabil ities are critical to ensuring consistent outcomes across programs such as Medicaid and SNAP. From Self-Attestation to Early Validation Self-attestation remains essen tial, particularly for information not

Illustration by Chris Campbell

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