A Methodology for Using Large Language Models to Create User-Friendly Applications for Medicaid Redetermination and Other Social Services

Following the unwinding of Medicaid's 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].Given that Medicaid redetermination follows eligibility rules similar to those in clinical trials, we thought LLMs might help with Medicaid redetermination, as well.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.

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Publication Details

Journal
International Journal of Public Health
Published
2024-08-16
DOI
https://doi.org/10.3389/ijph.2024.1607317
Citations
1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.12
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article

A Methodology for Using Large Language Models to Create User-Friendly Applications for Medicaid Redetermination and Other Social Services

Mayana Pereira, Juan Lavista Ferres, William B. Weeks, Sumanth Ratna et al.
1 citations
International Journal of Public Health
Artificial Intelligence in Healthcare and Education
0.12
article

A Methodology for Using Large Language Models to Create User-Friendly Applications for Medicaid Redetermination and Other Social Services

Mayana Pereira, Juan Lavista Ferres, William B. Weeks, Sumanth Ratna, Aneesh Chopra
article en
1 citations

Abstract

Following the unwinding of Medicaid's 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].Given that Medicaid redetermination follows eligibility rules similar to those in clinical trials, we thought LLMs might help with Medicaid redetermination, as well.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.

International Journal of Public HealthVol. 69
Microsoft (United States) (US), Yale University (US), Microsoft Research (United Kingdom) (GB)
Reduced inequalities
Openalex Percentile: Top 49%
Artificial Intelligence in Healthcare and Education
0.12
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