Chatbot as a telehealth intervention strategy in the COVID-19 pandemic: lessons learned from an action research approach
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFMG |
Texto Completo: | 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 |
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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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. PratesWagner Meira Jr.application/pdfinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/54564/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALChatbot as a Telehealth Intervention Strategy in the COVID-19 Pandemic Lessons Learned from an Action Research Approach.pdfChatbot as a Telehealth Intervention Strategy in the COVID-19 Pandemic Lessons Learned from an Action Research Approach.pdfapplication/pdf480671https://repositorio.ufmg.br/bitstream/1843/54564/2/Chatbot%20as%20a%20Telehealth%20Intervention%20Strategy%20in%20the%20COVID-19%20Pandemic%20Lessons%20Learned%20from%20an%20Action%20Research%20Approach.pdf707f1241cbdfab7c60763fec7680af94MD521843/545642023-06-05 21:31:51.095oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2023-06-06T00:31:51Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false |
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 |
format |
article |
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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info:eu-repo/semantics/openAccess |
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openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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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reponame:Repositório Institucional da UFMG instname:Universidade Federal de Minas Gerais (UFMG) instacron:UFMG |
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