Need for an Artificial Intelligence-based Diabetes Care Management System in India and the United States.

IF 1.5 Q3 HEALTH POLICY & SERVICES Health Services Research and Managerial Epidemiology Pub Date : 2024-08-28 eCollection Date: 2024-01-01 DOI:10.1177/23333928241275292
Veena Mayya, Rajesh N V P S Kandala, Varadraj Gurupur, Christian King, Giang T Vu, Thomas T H Wan
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Abstract

Objective: Diabetes mellitus is an important chronic disease that is prevalent around the world. Different countries and diverse cultures use varying approaches to dealing with this chronic condition. Also, with the advancement of computation and automated decision-making, many tools and technologies are now available to patients suffering from this disease. In this work, the investigators attempt to analyze approaches taken towards managing this illness in India and the United States.

Methods: In this work, the investigators have used available literature and data to compare the use of artificial intelligence in diabetes management.

Findings: The article provides key insights to comparison of diabetes management in terms of the nature of the healthcare system, availability, electronic health records, cultural factors, data privacy, affordability, and other important variables. Interestingly, variables such as quality of electronic health records, and cultural factors are key impediments in implementing an efficiency-driven management system for dealing with this chronic disease.

Conclusion: The article adds to the body of knowledge associated with the management of this disease, establishing a critical need for using artificial intelligence in diabetes care management.

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印度和美国对基于人工智能的糖尿病护理管理系统的需求。
目的:糖尿病是一种重要的慢性疾病,在世界各地普遍存在。不同的国家和不同的文化采用不同的方法来应对这种慢性疾病。此外,随着计算和自动决策技术的发展,现在有许多工具和技术可供糖尿病患者使用。在这项研究中,研究人员试图分析印度和美国管理这种疾病的方法:在这项工作中,研究人员利用现有的文献和数据,比较了人工智能在糖尿病管理中的应用:文章从医疗保健系统的性质、可用性、电子健康记录、文化因素、数据隐私、可负担性和其他重要变量等方面,对糖尿病管理的比较提供了重要见解。有趣的是,电子健康记录的质量和文化因素等变量是实施以效率为导向的慢性病管理系统的主要障碍:这篇文章丰富了与糖尿病管理相关的知识,确定了在糖尿病护理管理中使用人工智能的关键需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.60
自引率
6.20%
发文量
32
审稿时长
12 weeks
期刊最新文献
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