AI-driven innovation in medicaid: enhancing access, cost efficiency, and population health management
- URL: http://arxiv.org/abs/2410.21284v1
- Date: Fri, 11 Oct 2024 07:14:42 GMT
- Title: AI-driven innovation in medicaid: enhancing access, cost efficiency, and population health management
- Authors: Balaji Shesharao Ingole, Vishnu Ramineni, Manjunatha Sughaturu Krishnappa, Vivekananda Jayaram,
- Abstract summary: 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.
- Score: 1.4802369202548666
- License:
- Abstract: 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.
Related papers
- Artificial Intelligence for Public Health Surveillance in Africa: Applications and Opportunities [0.0]
This paper investigates the applications of AI in public health surveillance across the continent.
Our paper highlights AI's potential to enhance disease monitoring and health outcomes.
Key barriers to the widespread adoption of AI in African public health systems have been identified.
arXiv Detail & Related papers (2024-08-05T15:48:51Z) - AI-Driven Healthcare: A Survey on Ensuring Fairness and Mitigating Bias [2.398440840890111]
AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions.
These advancements also introduce substantial ethical and fairness challenges.
These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups.
arXiv Detail & Related papers (2024-07-29T02:39:17Z) - What We Know So Far: Artificial Intelligence in African Healthcare [0.0]
Artificial intelligence (AI) applied to healthcare has the potential to transform healthcare in Africa.
This paper reviews the current state of how AI Algorithms can be used to improve diagnostics, treatment, and disease monitoring.
There is a need for a well-coordinated effort by the governments, private sector, healthcare providers, and international organizations to create sustainable AI solutions.
arXiv Detail & Related papers (2023-05-10T19:27:40Z) - The Design and Implementation of a National AI Platform for Public
Healthcare in Italy: Implications for Semantics and Interoperability [62.997667081978825]
The Italian National Health Service is adopting Artificial Intelligence through its technical agencies.
Such a vast programme requires special care in formalising the knowledge domain.
Questions have been raised about the impact that AI could have on patients, practitioners, and health systems.
arXiv Detail & Related papers (2023-04-24T08:00:02Z) - Ensuring Trustworthy Medical Artificial Intelligence through Ethical and
Philosophical Principles [4.705984758887425]
AI-based computer-assisted diagnosis and treatment tools can democratize healthcare by matching the clinical level or surpassing clinical experts.
The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care.
integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability.
arXiv Detail & Related papers (2023-04-23T04:14:18Z) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI.
We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
arXiv Detail & Related papers (2022-05-19T17:34:18Z) - MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence
using Federated Evaluation [110.31526448744096]
We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data.
We are building MedPerf, an open framework for benchmarking machine learning in the medical domain.
arXiv Detail & Related papers (2021-09-29T18:09:41Z) - Edge Intelligence for Empowering IoT-based Healthcare Systems [42.909808437026136]
This article highlights the benefits of edge intelligent technology, along with AI in smart healthcare systems.
A novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems.
arXiv Detail & Related papers (2021-03-22T19:35:06Z) - Reinforcement Learning with Efficient Active Feature Acquisition [59.91808801541007]
In real-life, information acquisition might correspond to performing a medical test on a patient.
We propose a model-based reinforcement learning framework that learns an active feature acquisition policy.
Key to the success is a novel sequential variational auto-encoder that learns high-quality representations from partially observed states.
arXiv Detail & Related papers (2020-11-02T08:46:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.