Following the unwinding of Medicaid\u2019s continuous enrollment provision, states must redetermine Medicaid eligibility, creating uncertainty about coverage [1 (opens in new tab)<\/span><\/a>] and the widespread administrative removal of beneficiaries from rolls [2 (opens in new tab)<\/span><\/a>].<\/p>\n Existing research demonstrates that Large Language Models (LLMs) can automate clinical trial eligibility query extraction [3 (opens in new tab)<\/span><\/a>], generation [4 (opens in new tab)<\/span><\/a>], and classification [5 (opens in new tab)<\/span><\/a>]. Given that Medicaid redetermination follows eligibility rules similar to those in clinical trials, we thought LLMs might help with Medicaid redetermination, as well.<\/p>\n Therefore, using the State of Washington, South Carolina, and North Dakota as examples, we applied LLMs to extract Medicaid rules from publicly available documents and transform those rules into a web application that could allow users to determine whether they are eligible for Medicaid. This paper describes the methodology we used.<\/p>\n","protected":false},"excerpt":{"rendered":" Background Following the unwinding of Medicaid\u2019s continuous enrollment provision, states must redetermine Medicaid eligibility, creating uncertainty about coverage [1] and the widespread administrative removal of beneficiaries from rolls [2]. Existing research demonstrates that Large Language Models (LLMs) can automate clinical trial eligibility query extraction [3], generation [4], and classification [5]. 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