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Construction and Application of an Intelligent Response System for COVID-19 Voice Consultation in China: A Retrospective Study
Background: The outbreak of novel coronavirus disease 2019 (COVID-19) has led to tremendous individuals visit medical institutions for healthcare services. Public gatherings and close contact in clinics and emergency departments may increase the exposure and cross-infection of COVID-19. Objectives:...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8649760/ https://www.ncbi.nlm.nih.gov/pubmed/34888331 http://dx.doi.org/10.3389/fmed.2021.781781 |
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author | Shi, Jinming Gao, Jinghong Zhai, Yunkai Ye, Ming Lu, Yaoen He, Xianying Cui, Fangfang Ma, Qianqian Zhao, Jie |
author_facet | Shi, Jinming Gao, Jinghong Zhai, Yunkai Ye, Ming Lu, Yaoen He, Xianying Cui, Fangfang Ma, Qianqian Zhao, Jie |
author_sort | Shi, Jinming |
collection | PubMed |
description | Background: The outbreak of novel coronavirus disease 2019 (COVID-19) has led to tremendous individuals visit medical institutions for healthcare services. Public gatherings and close contact in clinics and emergency departments may increase the exposure and cross-infection of COVID-19. Objectives: The purpose of this study was to develop and deploy an intelligent response system for COVID-19 voice consultation, to provide suggestions of response measures based on actual information of users, and screen COVID-19 suspected cases. Methods: Based on the requirements analysis of business, user, and function, the physical architecture, system architecture, and core algorithms are designed and implemented. The system operation process is designed according to guidance documents of the National Health Commission and the actual experience of prevention, diagnosis and treatment of COVID-19. Both qualitative (system construction) and quantitative (system application) data from the real-world healthcare service of the system were retrospectively collected and analyzed. Results: The system realizes the functions, such as remote deployment and operations, fast operation procedure adjustment, and multi-dimensional statistical report capability. The performance of the machine-learning model used to develop the system is better than others, with the lowest Character Error Rate (CER) 8.13%. As of September 24, 2020, the system has received 12,264 times incoming calls and provided a total of 11,788 COVID-19-related consultation services for the public. Approximately 85.2% of the users are from Henan Province and followed by Beijing (2.5%). Of all the incoming calls, China Mobile contributes the largest proportion (66%), while China Unicom and China Telecom are accounted for 23% and 11%. For the time that users access the system, there is a peak period in the morning (08:00–10:00) and afternoon (14:00–16:00), respectively. Conclusions: The intelligent response system has achieved appreciable practical implementation effects. Our findings reveal that the provision of inquiry services through an intelligent voice consultation system may play a role in optimizing the allocation of healthcare resources, improving the efficiency of medical services, saving medical expenses, and protecting vulnerable groups. |
format | Online Article Text |
id | pubmed-8649760 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86497602021-12-08 Construction and Application of an Intelligent Response System for COVID-19 Voice Consultation in China: A Retrospective Study Shi, Jinming Gao, Jinghong Zhai, Yunkai Ye, Ming Lu, Yaoen He, Xianying Cui, Fangfang Ma, Qianqian Zhao, Jie Front Med (Lausanne) Medicine Background: The outbreak of novel coronavirus disease 2019 (COVID-19) has led to tremendous individuals visit medical institutions for healthcare services. Public gatherings and close contact in clinics and emergency departments may increase the exposure and cross-infection of COVID-19. Objectives: The purpose of this study was to develop and deploy an intelligent response system for COVID-19 voice consultation, to provide suggestions of response measures based on actual information of users, and screen COVID-19 suspected cases. Methods: Based on the requirements analysis of business, user, and function, the physical architecture, system architecture, and core algorithms are designed and implemented. The system operation process is designed according to guidance documents of the National Health Commission and the actual experience of prevention, diagnosis and treatment of COVID-19. Both qualitative (system construction) and quantitative (system application) data from the real-world healthcare service of the system were retrospectively collected and analyzed. Results: The system realizes the functions, such as remote deployment and operations, fast operation procedure adjustment, and multi-dimensional statistical report capability. The performance of the machine-learning model used to develop the system is better than others, with the lowest Character Error Rate (CER) 8.13%. As of September 24, 2020, the system has received 12,264 times incoming calls and provided a total of 11,788 COVID-19-related consultation services for the public. Approximately 85.2% of the users are from Henan Province and followed by Beijing (2.5%). Of all the incoming calls, China Mobile contributes the largest proportion (66%), while China Unicom and China Telecom are accounted for 23% and 11%. For the time that users access the system, there is a peak period in the morning (08:00–10:00) and afternoon (14:00–16:00), respectively. Conclusions: The intelligent response system has achieved appreciable practical implementation effects. Our findings reveal that the provision of inquiry services through an intelligent voice consultation system may play a role in optimizing the allocation of healthcare resources, improving the efficiency of medical services, saving medical expenses, and protecting vulnerable groups. Frontiers Media S.A. 2021-11-23 /pmc/articles/PMC8649760/ /pubmed/34888331 http://dx.doi.org/10.3389/fmed.2021.781781 Text en Copyright © 2021 Shi, Gao, Zhai, Ye, Lu, He, Cui, Ma and Zhao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Shi, Jinming Gao, Jinghong Zhai, Yunkai Ye, Ming Lu, Yaoen He, Xianying Cui, Fangfang Ma, Qianqian Zhao, Jie Construction and Application of an Intelligent Response System for COVID-19 Voice Consultation in China: A Retrospective Study |
title | Construction and Application of an Intelligent Response System for COVID-19 Voice Consultation in China: A Retrospective Study |
title_full | Construction and Application of an Intelligent Response System for COVID-19 Voice Consultation in China: A Retrospective Study |
title_fullStr | Construction and Application of an Intelligent Response System for COVID-19 Voice Consultation in China: A Retrospective Study |
title_full_unstemmed | Construction and Application of an Intelligent Response System for COVID-19 Voice Consultation in China: A Retrospective Study |
title_short | Construction and Application of an Intelligent Response System for COVID-19 Voice Consultation in China: A Retrospective Study |
title_sort | construction and application of an intelligent response system for covid-19 voice consultation in china: a retrospective study |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8649760/ https://www.ncbi.nlm.nih.gov/pubmed/34888331 http://dx.doi.org/10.3389/fmed.2021.781781 |
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