Medicaid Algorithmic Unwinding Economics

Vivek Yadav; Amit Nandal

doi:10.36948/ijfmr.2026.v08i02.72557

Medicaid Algorithmic Unwinding Economics

Author(s)	Vivek Yadav, Amit Nandal
Country	United States
Abstract	Recent federal budget constraints and subsequent cuts in administrative funding have led to an increased application of machine learning based automation technology in the state Medicaid eligibility redetermination and disenrollment ("unwinding") processes. The states that are using frequent eligibility reviews with limited human resources are using algorithmic systems not only for the purpose of streamlining eligibility verification but also for reducing operational costs and accelerating decision-making. These systems have the potential for efficiency and scalability but also carry the risk of errors in classification, bias, and systemic misjudgement, particularly for the most vulnerable populations, such as those experiencing unstable housing, irregular employment, limited digital access, and inconsistent documentation. For example, errors in disenrollment resulting from algorithmic determinations may result in a sudden loss of health benefits, delayed treatments, and a reliance on emergency services for most of the care. This paper examines the economic consequences of algorithmic Medicaid unwinding and its downstream financial impact on safety net hospitals. More precisely, it proposes a policy, relevant analytical framework that measures how mistaken disenrollments magnify uncompensated emergency room use, weaken hospital operating margins, and transfer the financial load from state Medicaid programs to publicly funded healthcare institutions. The research, by associating automated eligibility errors with hospital, level cost increase, emergency department overcrowding, and uncompensated care growth, presents algorithmic unwinding as a systemic economic risk instead of just an administrative optimization strategy. The main point of this paper is the creation of a detailed economic impact model linking decisions on automation at the state level to hospital financial sustainability, public health equity, and healthcare system stability. Additionally, the article offers policy, related insight into algorithmic governance, highlighting how essential human, in, the, loop oversight, transparency, auditing mechanisms, and equity, aware system design are to preventing the shifting of costs onto safety, net providers and to ensuring the protection of vulnerable patient populations.
Keywords	Medicaid, algorithmic eligibility, machine learning, safety-net hospitals, uncompensated care, healthcare economics.
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-26
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.72557

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Medicaid Algorithmic Unwinding Economics

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