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慢性病如何影响患者与人工智能的交互:文献综述

On How Chronic Conditions Affect the Patient-AI Interaction: A Literature Review.

作者信息

Tahri Sqalli Mohammed, Al-Thani Dena

机构信息

Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha P.O. Box 34110, Qatar.

出版信息

Healthcare (Basel). 2020 Sep 1;8(3):313. doi: 10.3390/healthcare8030313.

Abstract

: Across the globe, managing chronic diseases has been recognized as a challenge for patients and healthcare providers. The state of the art in managing chronic conditions requires not only responding to the clinical needs of the patient, but also guaranteeing a comfortable state of wellbeing for them, despite living with the disease. This demands mutual effort between the patient and the physician in constantly collecting data, monitoring, and understanding the disease. The advent of artificial intelligence has made this process easier. However, studies have rarely attempted to analyze how the different artificial intelligence based health coaching systems are used to manage different types of chronic conditions. : Throughout this grounded theory literature review, we aim to provide an overview for the features that characterize artificial intelligence based health coaching systems used by patients with chronic diseases. : During our search and paper selection process process, we use three bibliographic libraries (PubMed, IEEE Xplore, and ACM Digital Library). Using the grounded theory, we extract overarching themes for the artificial intelligence based health coaching systems. These systems are then classified according to their role, platform, type of interaction with the patient, as well as targeted chronic conditions. Of 869 citations retrieved, 31 unique studies are included in this review. : The included studies assess 14 different chronic conditions. Common roles for AI-based health coaching systems are: developing adherence, informing, motivating, reminding, preventing, building a care network, and entertaining. Health coaching systems combine the aforementioned roles to cater to the needs of the patients. The combinations of these roles differ between multilateral, unilateral, opposing bilateral, complementing bilateral, one-role-missing, and the blurred role combinations. Clinical solutions and research related to artificial intelligence based health coaching systems are very limited. Clear guidelines to help develop artificial intelligence-based health coaching systems are still blurred. This grounded theory literature review attempted to shed the light on the research and development requirements for an effective health coaching system intended for patients with chronic conditions. Researchers are recommended to use this review to identify the most suitable role combination for an effective health coaching system development.

摘要

在全球范围内,管理慢性病对患者和医疗服务提供者而言都是一项挑战。管理慢性病的最新技术不仅需要满足患者的临床需求,还要确保他们在患病的情况下能保持舒适的健康状态。这需要患者和医生共同努力,持续收集数据、监测并了解病情。人工智能的出现使这一过程变得更加轻松。然而,很少有研究尝试分析不同的基于人工智能的健康指导系统如何用于管理不同类型的慢性病。

在本次扎根理论文献综述中,我们旨在概述慢性病患者使用的基于人工智能的健康指导系统的特征。

在搜索和论文筛选过程中,我们使用了三个文献数据库(PubMed、IEEE Xplore和ACM数字图书馆)。运用扎根理论,我们提取了基于人工智能的健康指导系统的总体主题。然后,这些系统根据其作用、平台、与患者的互动类型以及目标慢性病进行分类。在检索到的869篇文献中,本综述纳入了31项独特的研究。

纳入的研究评估了14种不同的慢性病。基于人工智能的健康指导系统的常见作用包括:提高依从性、提供信息、激励、提醒、预防、建立护理网络和娱乐。健康指导系统将上述作用结合起来以满足患者的需求。这些作用的组合在多边、单边、对立双边、互补双边、缺一项作用以及作用模糊的组合之间存在差异。与基于人工智能的健康指导系统相关的临床解决方案和研究非常有限。帮助开发基于人工智能的健康指导系统的明确指南仍然模糊不清。本次扎根理论文献综述试图阐明针对慢性病患者的有效健康指导系统的研发要求。建议研究人员利用本综述来确定有效健康指导系统开发中最合适的作用组合。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eeaf/7551169/f93f9f9fc040/healthcare-08-00313-g001.jpg

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