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A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial

BACKGROUND: The data regarding the use of conversational agents in oncology are scarce. OBJECTIVE: The aim of this study was to verify whether an artificial conversational agent was able to provide answers to patients with breast cancer with a level of satisfaction similar to the answers given by a...

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Autores principales: Bibault, Jean-Emmanuel, Chaix, Benjamin, Guillemassé, Arthur, Cousin, Sophie, Escande, Alexandre, Perrin, Morgane, Pienkowski, Arthur, Delamon, Guillaume, Nectoux, Pierre, Brouard, Benoît
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6906616/
https://www.ncbi.nlm.nih.gov/pubmed/31774408
http://dx.doi.org/10.2196/15787
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author Bibault, Jean-Emmanuel
Chaix, Benjamin
Guillemassé, Arthur
Cousin, Sophie
Escande, Alexandre
Perrin, Morgane
Pienkowski, Arthur
Delamon, Guillaume
Nectoux, Pierre
Brouard, Benoît
author_facet Bibault, Jean-Emmanuel
Chaix, Benjamin
Guillemassé, Arthur
Cousin, Sophie
Escande, Alexandre
Perrin, Morgane
Pienkowski, Arthur
Delamon, Guillaume
Nectoux, Pierre
Brouard, Benoît
author_sort Bibault, Jean-Emmanuel
collection PubMed
description BACKGROUND: The data regarding the use of conversational agents in oncology are scarce. OBJECTIVE: The aim of this study was to verify whether an artificial conversational agent was able to provide answers to patients with breast cancer with a level of satisfaction similar to the answers given by a group of physicians. METHODS: This study is a blind, noninferiority randomized controlled trial that compared the information given by the chatbot, Vik, with that given by a multidisciplinary group of physicians to patients with breast cancer. Patients were women with breast cancer in treatment or in remission. The European Organisation for Research and Treatment of Cancer Quality of Life Group information questionnaire (EORTC QLQ-INFO25) was adapted and used to compare the quality of the information provided to patients by the physician or the chatbot. The primary outcome was to show that the answers given by the Vik chatbot to common questions asked by patients with breast cancer about their therapy management are at least as satisfying as answers given by a multidisciplinary medical committee by comparing the success rate in each group (defined by a score above 3). The secondary objective was to compare the average scores obtained by the chatbot and physicians for each INFO25 item. RESULTS: A total of 142 patients were included and randomized into two groups of 71. They were all female with a mean age of 42 years (SD 19). The success rates (as defined by a score >3) was 69% (49/71) in the chatbot group versus 64% (46/71) in the physicians group. The binomial test showed the noninferiority (P<.001) of the chatbot’s answers. CONCLUSIONS: This is the first study that assessed an artificial conversational agent used to inform patients with cancer. The EORTC INFO25 scores from the chatbot were found to be noninferior to the scores of the physicians. Artificial conversational agents may save patients with minor health concerns from a visit to the doctor. This could allow clinicians to spend more time to treat patients who need a consultation the most. TRIAL REGISTRATION: Clinicaltrials.gov NCT03556813, https://tinyurl.com/rgtlehq
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spelling pubmed-69066162019-12-23 A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial Bibault, Jean-Emmanuel Chaix, Benjamin Guillemassé, Arthur Cousin, Sophie Escande, Alexandre Perrin, Morgane Pienkowski, Arthur Delamon, Guillaume Nectoux, Pierre Brouard, Benoît J Med Internet Res Original Paper BACKGROUND: The data regarding the use of conversational agents in oncology are scarce. OBJECTIVE: The aim of this study was to verify whether an artificial conversational agent was able to provide answers to patients with breast cancer with a level of satisfaction similar to the answers given by a group of physicians. METHODS: This study is a blind, noninferiority randomized controlled trial that compared the information given by the chatbot, Vik, with that given by a multidisciplinary group of physicians to patients with breast cancer. Patients were women with breast cancer in treatment or in remission. The European Organisation for Research and Treatment of Cancer Quality of Life Group information questionnaire (EORTC QLQ-INFO25) was adapted and used to compare the quality of the information provided to patients by the physician or the chatbot. The primary outcome was to show that the answers given by the Vik chatbot to common questions asked by patients with breast cancer about their therapy management are at least as satisfying as answers given by a multidisciplinary medical committee by comparing the success rate in each group (defined by a score above 3). The secondary objective was to compare the average scores obtained by the chatbot and physicians for each INFO25 item. RESULTS: A total of 142 patients were included and randomized into two groups of 71. They were all female with a mean age of 42 years (SD 19). The success rates (as defined by a score >3) was 69% (49/71) in the chatbot group versus 64% (46/71) in the physicians group. The binomial test showed the noninferiority (P<.001) of the chatbot’s answers. CONCLUSIONS: This is the first study that assessed an artificial conversational agent used to inform patients with cancer. The EORTC INFO25 scores from the chatbot were found to be noninferior to the scores of the physicians. Artificial conversational agents may save patients with minor health concerns from a visit to the doctor. This could allow clinicians to spend more time to treat patients who need a consultation the most. TRIAL REGISTRATION: Clinicaltrials.gov NCT03556813, https://tinyurl.com/rgtlehq JMIR Publications 2019-11-27 /pmc/articles/PMC6906616/ /pubmed/31774408 http://dx.doi.org/10.2196/15787 Text en ©Jean-Emmanuel Bibault, Benjamin Chaix, Arthur Guillemassé, Sophie Cousin, Alexandre Escande, Morgane Perrin, Arthur Pienkowski, Guillaume Delamon, Pierre Nectoux, Benoît Brouard. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 27.11.2019. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Bibault, Jean-Emmanuel
Chaix, Benjamin
Guillemassé, Arthur
Cousin, Sophie
Escande, Alexandre
Perrin, Morgane
Pienkowski, Arthur
Delamon, Guillaume
Nectoux, Pierre
Brouard, Benoît
A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial
title A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial
title_full A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial
title_fullStr A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial
title_full_unstemmed A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial
title_short A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial
title_sort chatbot versus physicians to provide information for patients with breast cancer: blind, randomized controlled noninferiority trial
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6906616/
https://www.ncbi.nlm.nih.gov/pubmed/31774408
http://dx.doi.org/10.2196/15787
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