Policy & Practice | Summer 2026

criteria are applied consistently and reducing reliance on case-by-case interpretation. Automation ensures consistent application of rules when defined conditions are met, while preserving human oversight for complex cases. Rather than reducing staff involve ment, this approach makes that involvement more targeted, freeing eligibility workers from routine pro cessing so they can focus on cases that require judgment and context. Many platforms also support workload pri oritization by identifying higher-risk or more complex cases, allowing staff to use their time more effectively. consumers and caseworkers can understand and trust the outcome. Consistency in eligibility decisions is a key component of equity. When stan dardized rules and verified data reduce subjective variation, individuals in similar circumstances are more likely to receive consistent outcomes. Equity can also be intentionally built into the determination process. Documentation-heavy approaches, for example, can create barriers for indi viduals without stable housing, reliable records, or digital access. By reducing reliance on applicant-supplied docu mentation, modern systems help lower these barriers and improve access. Transparency is equally important. Automated systems must clearly explain how decisions are made and what actions applicants need to take, so both A Foundation for Better Outcomes

Transparency is equally important. Automated systems must clearly explain how decisions are made and what actions applicants need to take, so both consumers and caseworkers can understand and trust the outcome. Even with automation, data sources may still be incomplete or uneven across populations. Automated rules should be continuously reviewed to avoid reinforcing disparities. A balanced approach—combining stan dardized decisioning with human oversight—remains essential. The benefits of automation depend in large part on the quality of the under lying data and policy logic. Incomplete data or outdated rules can introduce new challenges, making ongoing main tenance and testing essential. For many states, modernization is a multiyear effort that requires more than simply layering automation onto existing processes. Agencies often need to rethink workflows—including how data enter the system, how deci sions are sequenced, and how work is distributed. Successful implementation also depends on investment in change management, staff training, and gover nance. Continuous monitoring—such as alerts when income or household circumstances change—can further improve accuracy over time. Automation is most effective when it is treated as an ongoing enhance ment rather than a one-time upgrade. It provides a solid foundation for more proactive, real-time program adminis tration—allowing agencies to respond more quickly, manage risk more effec tively, and deliver more consistent outcomes. Implementation Considerations

available through external sources. However, when electronic data are available and not used in real-time, discrepancies are often discovered after decisions are made, leading to rework, added administrative burden, and potential coverage disruptions. Agencies are increasingly shifting verification earlier in the process, moving from retrospective checks to real-time validation. Authoritative data sources—such as wage records and federal and state databases— enable real-time or near-real-time verification while maintaining trans parency. In some cases, consumers can also authorize access to private data sources to provide a more complete financial picture. Rather than relying on a single source, modern systems draw from a combination of federal, state, and commercial data sets, including those capturing nontraditional income such as gig work and self-employment. Many states prioritize low-cost, high reliability sources first, reserving specialized data for more complex cases. This approach improves first-pass accuracy—the share of applications processed correctly without follow up—by identifying and resolving discrepancies in income, household composition, or employment status at the point of application rather than weeks later. Early validation reduces rework, prevents unnecessary eli gibility reversals, and improves the overall experience for applicants and staff. The impact is signifi cant: The Centers for Medicare and Medicaid Services (CMS) reported that 15 percent of Medicaid and CHIP coverage losses in 2025 were due to procedural reasons, underscoring the importance of resolving issues upstream. 2 Consistency and Workforce Efficiency Variability remains a persistent challenge in eligibility determination. Similar cases may be processed differ ently depending on workload, timing, or policy interpretation. Rules-based decision engines address this by trans lating policy into structured, testable logic, helping ensure eligibility

Stephanie Bell is the Medicaid Innovation and Policy Strategist for Vimo.

Reference Notes 1. https://www.kff.org/state category/medicaid-chip/

application-and-enrollment-systems/ 2. https://www.medicaid.gov/resources-for states/downloads/eligib-oper-and-enrol snap-jan2026.pdf

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