# AI-driven innovation in medicaid: enhancing access, cost efficiency, and population health management

arXiv: https://arxiv.org/abs/2410.21284 · HTML: https://mlsanity.com/paper/2410.21284/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 17 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Built on by (most cited first, 1 of 1)

- [Advancements In Heart Disease Prediction: A Machine Learning Approach For Early Detection And Risk Assessment](https://mlsanity.com/paper/2410.14738/) (2024)

## Abstract (arXiv)

The U.S. Medicaid program is experiencing critical challenges that include rapidly increasing healthcare costs, uneven care accessibility, and the challenge associated with addressing a varied set of population health needs. This paper investigates the transformative potential of Artificial Intelligence (AI) in reshaping Medicaid by streamlining operations, improving patient results, and lowering costs. We delve into the pivotal role of AI in predictive analytics, care coordination, the detection of fraud, and personalized medicine. By leveraging insights from advanced data models and addressing challenges particular to Medicaid, we put forward AI-driven solutions that prioritize equitable care and improved public health outcomes. This study underscores the urgency of integrating AI into Medicaid to not only improve operational effectiveness but also to create a more accessible and equitable healthcare system for all beneficiaries.