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一种为医院选择理想移动健康应用程序的大规模群体决策方法。

A large-scale group decision making method to select the ideal mobile health application for the hospital.

作者信息

Zhang Xumin, Meng Fanyong

机构信息

School of Business, Central South University, Changsha, 410083 China.

出版信息

Appl Intell (Dordr). 2022;52(14):15844-15864. doi: 10.1007/s10489-022-03273-1. Epub 2022 Mar 18.

Abstract

Mobile health, which is not limited by time and space, can effectively alleviate the imbalance of medical resources. Currently, more and more hospitals begin to pay attention to online medical care and actively expand their mobile channels. Among of which, the cooperation with the third-party platform is an effective way to expand the online services of most hospitals. With the increasing number of mobile health applications (mHealth apps), it is difficult to select the ideal application. Most of the existing studies on mHealth app selection are conducted from the perspective of users who have health needs, which is insufficient. The views of multiple stakeholders should be taken into account. mHealth app selection can be regarded as a large-scale group decision making (LSGDM) problem. In this paper, a hybrid LSGDM method is proposed to select the mHealth app with the highest user satisfaction. First, the weights of criteria are obtained based on quality function deployment and 2-additive measure. Furthermore, a consensus model that considers cooperative and non-cooperative behaviors of decision makers is applied to select the ideal mHealth app. Finally, an illustrative example is implemented to exhibit the utility and validity of the proposed model.

摘要

移动医疗不受时间和空间限制,能够有效缓解医疗资源不均衡的问题。目前,越来越多的医院开始重视在线医疗,并积极拓展其移动渠道。其中,与第三方平台合作是多数医院拓展在线服务的有效途径。随着移动健康应用程序(mHealth应用)数量的不断增加,选择理想的应用变得困难。现有的关于mHealth应用选择的研究大多是从有健康需求的用户角度进行的,这是不够的。应该考虑多个利益相关者的观点。mHealth应用选择可被视为一个大规模群体决策(LSGDM)问题。本文提出了一种混合LSGDM方法来选择用户满意度最高的mHealth应用。首先,基于质量功能展开和2-加性测度获得准则权重。此外,应用一种考虑决策者合作和非合作行为的共识模型来选择理想的mHealth应用。最后,通过一个实例展示了所提模型的实用性和有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d26/8931594/6ea102a1f29c/10489_2022_3273_Fig1_HTML.jpg

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