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新冠疫情公共卫生应对中的聊天机器人应用案例。

Chatbot use cases in the Covid-19 public health response.

机构信息

Management Information System Department, University of Georgia, Athens, Georgia, USA.

出版信息

J Am Med Inform Assoc. 2022 Apr 13;29(5):1000-1010. doi: 10.1093/jamia/ocac014.


DOI:10.1093/jamia/ocac014
PMID:35137107
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8903403/
Abstract

OBJECTIVE: To identify chatbot use cases deployed for public health response activities during the Covid-19 pandemic. MATERIAL AND METHODS: We searched PubMed/MEDLINE, Web of Knowledge, and Google Scholar in October 2020 and performed a follow-up search in July 2021. We screened articles based on their abstracts and keywords in their text, reviewed potentially relevant articles, and screened their references to (a) assess whether the article met inclusion criteria and (b) identify additional articles. Chatbots, their use cases, and chatbot design characteristics were extracted from the articles and information from other sources and by accessing those chatbots that were publicly accessible. RESULTS: Our search returned 3334 articles, 61 articles met our inclusion criteria, and 61 chatbots deployed in 30 countries were identified. We categorized chatbots based on their public health response use case(s) and design. Six categories of public health response use cases emerged comprising 15 distinct use cases: risk assessment, information dissemination, surveillance, post-Covid eligibility screening, distributed coordination, and vaccine scheduler. Design-wise, chatbots were relatively simple, implemented using decision-tree structures and predetermined response options, and focused on a narrow set of simple tasks, presumably due to need for quick deployment. CONCLUSION: Chatbots' scalability, wide accessibility, ease of use, and fast information dissemination provide complementary functionality that augments public health workers in public health response activities, addressing capacity constraints, social distancing requirements, and misinformation. Additional use cases, more sophisticated chatbot designs, and opportunities for synergies in chatbot development should be explored.

摘要

目的:确定在新冠疫情期间用于公共卫生应对活动的聊天机器人应用案例。

材料和方法:我们于 2020 年 10 月在 PubMed/MEDLINE、Web of Knowledge 和 Google Scholar 进行了检索,并于 2021 年 7 月进行了后续检索。我们根据文章的摘要和正文关键词对文章进行了筛选,对潜在相关文章进行了审查,并对这些文章的参考文献进行了筛选,以评估这些文章是否符合纳入标准,以及确定是否有其他文章。从文章和其他来源中提取了聊天机器人及其使用案例以及聊天机器人设计特征,并通过访问那些公开可用的聊天机器人获取信息。

结果:我们的检索返回了 3334 篇文章,其中 61 篇符合纳入标准,确定了 30 个国家部署的 61 个聊天机器人。我们根据公共卫生应对使用案例和设计对聊天机器人进行了分类。公共卫生应对使用案例分为六类,包括 15 个不同的使用案例:风险评估、信息传播、监测、新冠后资格筛查、分布式协调和疫苗安排。从设计角度来看,聊天机器人相对简单,采用决策树结构和预定的响应选项实现,专注于一组简单的任务,这可能是由于需要快速部署。

结论:聊天机器人的可扩展性、广泛的可及性、易用性和快速信息传播提供了补充功能,增强了公共卫生工作者在公共卫生应对活动中的能力,解决了能力限制、社会距离要求和错误信息问题。应该探索更多的使用案例、更复杂的聊天机器人设计以及在聊天机器人开发方面的协同机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/772b/9006697/7f5a2e01552a/ocac014f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/772b/9006697/2324f1fdb948/ocac014f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/772b/9006697/b7342f295562/ocac014f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/772b/9006697/7f5a2e01552a/ocac014f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/772b/9006697/2324f1fdb948/ocac014f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/772b/9006697/b7342f295562/ocac014f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/772b/9006697/7f5a2e01552a/ocac014f3.jpg

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本文引用的文献

[1]
Health Chatbots for Fighting COVID-19: a Scoping Review.

Acta Inform Med. 2020-12

[2]
The relationship between fever rate and telework implementation as a social distancing measure against the COVID-19 pandemic in Japan.

Public Health. 2021-3

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J Med Internet Res. 2020-10-6

[4]
Syndromic Surveillance Insights from a Symptom Assessment App Before and During COVID-19 Measures in Germany and the United Kingdom: Results From Repeated Cross-Sectional Analyses.

JMIR Mhealth Uhealth. 2020-10-9

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Digital Response During the COVID-19 Pandemic in Saudi Arabia.

J Med Internet Res. 2020-9-1

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J Med Internet Res. 2020-8-7

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Implementation of a digital chatbot to screen health system employees during the COVID-19 pandemic.

J Am Med Inform Assoc. 2020-7-1

[9]
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Cien Saude Colet. 2020-6

[10]
Early SNS-Based Monitoring System for the COVID-19 Outbreak in Japan: A Population-Level Observational Study.

J Epidemiol. 2020-5-30

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