Chatbot as a telehealth intervention strategy in the COVID-19 pandemic: lessons learned from an action research approach

Detalhes bibliográficos
Autor(a) principal: Bruno Azevedo Chagas
Data de Publicação: 2021
Outros Autores: Antonio L. Ribeiro, Kícila Ferreguetti, Thiago C. Ferreira, Milena S. Marcolino, Leonardo B. Ribeiro, Adriana Silvina Pagano, Zilma S. N. Reis, Raquel O. Prates, Wagner Meira Jr.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: https://doi.org/10.19153/cleiej.24.3.6
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https://orcid.org/0000-0002-3150-3503
https://orcid.org/0000-0001-6374-9295
https://orcid.org/0000-0002-7128-4974
https://orcid.org/0000-0002-2614-2723
Resumo: The COVID-19 pandemic and the need for social distancing have created a demand for new and innovative solutions in healthcare systems worldwide. One of the strategies that have been implemented are chatbots, which can be helpful in providing reliable health information and preventing people from seeking assistance in healthcare centers and being unnecessarily exposed to the virus. In this context, although a high number of chatbots have been implemented worldwide, little has been discussed about the process and challenges in developing and implementing this technology. This paper reports on an action research, which designed a novel chatbot as a prompt response to the COVID-19 pandemic. The chatbot is intended to be a first layer of interaction with the public, performing triage of patients and providing information about COVID-19 on a large scale and without human contact. Our contribution is twofold: (i) we reflected on the development process and discussed lessons learned and recommendations to support a multidisciplinary development and evolution process of the chatbot; and (ii) we identified some interactive and technological features that can be used as a reference framework for this kind of technology. These contributions can be useful to other researchers and multidisciplinary teams facing similar challenges.
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spelling 2023-06-05T23:05:25Z2023-06-05T23:05:25Z2021-12-13243117https://doi.org/10.19153/cleiej.24.3.60717-5000http://hdl.handle.net/1843/54564https://orcid.org/0000-0002-8571-3135https://orcid.org/0000-0002-0364-3584https://orcid.org/0000-0002-1919-0073https://orcid.org/0000-0003-4278-3771https://orcid.org/0000-0001-9035-0722https://orcid.org/0000-0002-3150-3503https://orcid.org/0000-0001-6374-9295https://orcid.org/0000-0002-7128-4974https://orcid.org/0000-0002-2614-2723The COVID-19 pandemic and the need for social distancing have created a demand for new and innovative solutions in healthcare systems worldwide. One of the strategies that have been implemented are chatbots, which can be helpful in providing reliable health information and preventing people from seeking assistance in healthcare centers and being unnecessarily exposed to the virus. In this context, although a high number of chatbots have been implemented worldwide, little has been discussed about the process and challenges in developing and implementing this technology. This paper reports on an action research, which designed a novel chatbot as a prompt response to the COVID-19 pandemic. The chatbot is intended to be a first layer of interaction with the public, performing triage of patients and providing information about COVID-19 on a large scale and without human contact. Our contribution is twofold: (i) we reflected on the development process and discussed lessons learned and recommendations to support a multidisciplinary development and evolution process of the chatbot; and (ii) we identified some interactive and technological features that can be used as a reference framework for this kind of technology. These contributions can be useful to other researchers and multidisciplinary teams facing similar challenges.engUniversidade Federal de Minas GeraisUFMGBrasilFALE - FACULDADE DE LETRASICX - DEPARTAMENTO DE ESTATÍSTICAMED - DEPARTAMENTO DE CLÍNICA MÉDICAMED - DEPARTAMENTO DE GINECOLOGIA OBSTETRÍCIACLEI Electronic JournalCiência da ComputaçãoSaúde ColetivaChatbotTelehealthCOVID pandemicAction ResearchChatbot as a telehealth intervention strategy in the COVID-19 pandemic: lessons learned from an action research approachinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleBruno Azevedo ChagasAntonio L. RibeiroKícila FerreguettiThiago C. FerreiraMilena S. MarcolinoLeonardo B. RibeiroAdriana Silvina PaganoZilma S. N. ReisRaquel O. 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dc.title.pt_BR.fl_str_mv Chatbot as a telehealth intervention strategy in the COVID-19 pandemic: lessons learned from an action research approach
title Chatbot as a telehealth intervention strategy in the COVID-19 pandemic: lessons learned from an action research approach
spellingShingle Chatbot as a telehealth intervention strategy in the COVID-19 pandemic: lessons learned from an action research approach
Bruno Azevedo Chagas
Chatbot
Telehealth
COVID pandemic
Action Research
Ciência da Computação
Saúde Coletiva
title_short Chatbot as a telehealth intervention strategy in the COVID-19 pandemic: lessons learned from an action research approach
title_full Chatbot as a telehealth intervention strategy in the COVID-19 pandemic: lessons learned from an action research approach
title_fullStr Chatbot as a telehealth intervention strategy in the COVID-19 pandemic: lessons learned from an action research approach
title_full_unstemmed Chatbot as a telehealth intervention strategy in the COVID-19 pandemic: lessons learned from an action research approach
title_sort Chatbot as a telehealth intervention strategy in the COVID-19 pandemic: lessons learned from an action research approach
author Bruno Azevedo Chagas
author_facet Bruno Azevedo Chagas
Antonio L. Ribeiro
Kícila Ferreguetti
Thiago C. Ferreira
Milena S. Marcolino
Leonardo B. Ribeiro
Adriana Silvina Pagano
Zilma S. N. Reis
Raquel O. Prates
Wagner Meira Jr.
author_role author
author2 Antonio L. Ribeiro
Kícila Ferreguetti
Thiago C. Ferreira
Milena S. Marcolino
Leonardo B. Ribeiro
Adriana Silvina Pagano
Zilma S. N. Reis
Raquel O. Prates
Wagner Meira Jr.
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Bruno Azevedo Chagas
Antonio L. Ribeiro
Kícila Ferreguetti
Thiago C. Ferreira
Milena S. Marcolino
Leonardo B. Ribeiro
Adriana Silvina Pagano
Zilma S. N. Reis
Raquel O. Prates
Wagner Meira Jr.
dc.subject.por.fl_str_mv Chatbot
Telehealth
COVID pandemic
Action Research
topic Chatbot
Telehealth
COVID pandemic
Action Research
Ciência da Computação
Saúde Coletiva
dc.subject.other.pt_BR.fl_str_mv Ciência da Computação
Saúde Coletiva
description The COVID-19 pandemic and the need for social distancing have created a demand for new and innovative solutions in healthcare systems worldwide. One of the strategies that have been implemented are chatbots, which can be helpful in providing reliable health information and preventing people from seeking assistance in healthcare centers and being unnecessarily exposed to the virus. In this context, although a high number of chatbots have been implemented worldwide, little has been discussed about the process and challenges in developing and implementing this technology. This paper reports on an action research, which designed a novel chatbot as a prompt response to the COVID-19 pandemic. The chatbot is intended to be a first layer of interaction with the public, performing triage of patients and providing information about COVID-19 on a large scale and without human contact. Our contribution is twofold: (i) we reflected on the development process and discussed lessons learned and recommendations to support a multidisciplinary development and evolution process of the chatbot; and (ii) we identified some interactive and technological features that can be used as a reference framework for this kind of technology. These contributions can be useful to other researchers and multidisciplinary teams facing similar challenges.
publishDate 2021
dc.date.issued.fl_str_mv 2021-12-13
dc.date.accessioned.fl_str_mv 2023-06-05T23:05:25Z
dc.date.available.fl_str_mv 2023-06-05T23:05:25Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1843/54564
dc.identifier.doi.pt_BR.fl_str_mv https://doi.org/10.19153/cleiej.24.3.6
dc.identifier.issn.pt_BR.fl_str_mv 0717-5000
dc.identifier.orcid.pt_BR.fl_str_mv https://orcid.org/0000-0002-8571-3135
https://orcid.org/0000-0002-0364-3584
https://orcid.org/0000-0002-1919-0073
https://orcid.org/0000-0003-4278-3771
https://orcid.org/0000-0001-9035-0722
https://orcid.org/0000-0002-3150-3503
https://orcid.org/0000-0001-6374-9295
https://orcid.org/0000-0002-7128-4974
https://orcid.org/0000-0002-2614-2723
url https://doi.org/10.19153/cleiej.24.3.6
http://hdl.handle.net/1843/54564
https://orcid.org/0000-0002-8571-3135
https://orcid.org/0000-0002-0364-3584
https://orcid.org/0000-0002-1919-0073
https://orcid.org/0000-0003-4278-3771
https://orcid.org/0000-0001-9035-0722
https://orcid.org/0000-0002-3150-3503
https://orcid.org/0000-0001-6374-9295
https://orcid.org/0000-0002-7128-4974
https://orcid.org/0000-0002-2614-2723
identifier_str_mv 0717-5000
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv CLEI Electronic Journal
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dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.publisher.initials.fl_str_mv UFMG
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv FALE - FACULDADE DE LETRAS
ICX - DEPARTAMENTO DE ESTATÍSTICA
MED - DEPARTAMENTO DE CLÍNICA MÉDICA
MED - DEPARTAMENTO DE GINECOLOGIA OBSTETRÍCIA
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
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