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The Impact of Artificial Intelligence on Waiting Time for Medical Care in an Urgent Care Service for COVID-19: Single-Center Prospective Study
BACKGROUND: To demonstrate the value of implementation of an artificial intelligence solution in health care service, a winning project of the Massachusetts Institute of Technology Hacking Medicine Brazil competition was implemented in an urgent care service for health care professionals at Hospital...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812142/ https://www.ncbi.nlm.nih.gov/pubmed/35103611 http://dx.doi.org/10.2196/29012 |
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author | Bin, Kaio Jia Melo, Adler Araujo Ribeiro da Rocha, José Guilherme Moraes Franco de Almeida, Renata Pivi Cobello Junior, Vilson Maia, Fernando Liebhart de Faria, Elizabeth Pereira, Antonio José Battistella, Linamara Rizzo Ono, Suzane Kioko |
author_facet | Bin, Kaio Jia Melo, Adler Araujo Ribeiro da Rocha, José Guilherme Moraes Franco de Almeida, Renata Pivi Cobello Junior, Vilson Maia, Fernando Liebhart de Faria, Elizabeth Pereira, Antonio José Battistella, Linamara Rizzo Ono, Suzane Kioko |
author_sort | Bin, Kaio Jia |
collection | PubMed |
description | BACKGROUND: To demonstrate the value of implementation of an artificial intelligence solution in health care service, a winning project of the Massachusetts Institute of Technology Hacking Medicine Brazil competition was implemented in an urgent care service for health care professionals at Hospital das Clínicas of the Faculdade de Medicina da Universidade de São Paulo during the COVID-19 pandemic. OBJECTIVE: The aim of this study was to determine the impact of implementation of the digital solution in the urgent care service, assessing the reduction of nonvalue-added activities and its effect on the nurses’ time required for screening and the waiting time for patients to receive medical care. METHODS: This was a single-center, comparative, prospective study designed according to the Public Health England guide “Evaluating Digital Products for Health.” A total of 38,042 visits were analyzed over 18 months to determine the impact of implementing the digital solution. Medical care registration, health screening, and waiting time for medical care were compared before and after implementation of the digital solution. RESULTS: The digital solution automated 92% of medical care registrations. The time for health screening increased by approximately 16% during the implementation and in the first 3 months after the implementation. The waiting time for medical care after automation with the digital solution was reduced by approximately 12 minutes compared with that required for visits without automation. The total time savings in the 12 months after implementation was estimated to be 2508 hours. CONCLUSIONS: The digital solution was able to reduce nonvalue-added activities, without a substantial impact on health screening, and further saved waiting time for medical care in an urgent care service in Brazil during the COVID-19 pandemic. |
format | Online Article Text |
id | pubmed-8812142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-88121422022-03-08 The Impact of Artificial Intelligence on Waiting Time for Medical Care in an Urgent Care Service for COVID-19: Single-Center Prospective Study Bin, Kaio Jia Melo, Adler Araujo Ribeiro da Rocha, José Guilherme Moraes Franco de Almeida, Renata Pivi Cobello Junior, Vilson Maia, Fernando Liebhart de Faria, Elizabeth Pereira, Antonio José Battistella, Linamara Rizzo Ono, Suzane Kioko JMIR Form Res Original Paper BACKGROUND: To demonstrate the value of implementation of an artificial intelligence solution in health care service, a winning project of the Massachusetts Institute of Technology Hacking Medicine Brazil competition was implemented in an urgent care service for health care professionals at Hospital das Clínicas of the Faculdade de Medicina da Universidade de São Paulo during the COVID-19 pandemic. OBJECTIVE: The aim of this study was to determine the impact of implementation of the digital solution in the urgent care service, assessing the reduction of nonvalue-added activities and its effect on the nurses’ time required for screening and the waiting time for patients to receive medical care. METHODS: This was a single-center, comparative, prospective study designed according to the Public Health England guide “Evaluating Digital Products for Health.” A total of 38,042 visits were analyzed over 18 months to determine the impact of implementing the digital solution. Medical care registration, health screening, and waiting time for medical care were compared before and after implementation of the digital solution. RESULTS: The digital solution automated 92% of medical care registrations. The time for health screening increased by approximately 16% during the implementation and in the first 3 months after the implementation. The waiting time for medical care after automation with the digital solution was reduced by approximately 12 minutes compared with that required for visits without automation. The total time savings in the 12 months after implementation was estimated to be 2508 hours. CONCLUSIONS: The digital solution was able to reduce nonvalue-added activities, without a substantial impact on health screening, and further saved waiting time for medical care in an urgent care service in Brazil during the COVID-19 pandemic. JMIR Publications 2022-02-01 /pmc/articles/PMC8812142/ /pubmed/35103611 http://dx.doi.org/10.2196/29012 Text en ©Kaio Jia Bin, Adler Araujo Ribeiro Melo, José Guilherme Moraes Franco da Rocha, Renata Pivi de Almeida, Vilson Cobello Junior, Fernando Liebhart Maia, Elizabeth de Faria, Antonio José Pereira, Linamara Rizzo Battistella, Suzane Kioko Ono. Originally published in JMIR Formative Research (https://formative.jmir.org), 01.02.2022. 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 JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Bin, Kaio Jia Melo, Adler Araujo Ribeiro da Rocha, José Guilherme Moraes Franco de Almeida, Renata Pivi Cobello Junior, Vilson Maia, Fernando Liebhart de Faria, Elizabeth Pereira, Antonio José Battistella, Linamara Rizzo Ono, Suzane Kioko The Impact of Artificial Intelligence on Waiting Time for Medical Care in an Urgent Care Service for COVID-19: Single-Center Prospective Study |
title | The Impact of Artificial Intelligence on Waiting Time for Medical Care in an Urgent Care Service for COVID-19: Single-Center Prospective Study |
title_full | The Impact of Artificial Intelligence on Waiting Time for Medical Care in an Urgent Care Service for COVID-19: Single-Center Prospective Study |
title_fullStr | The Impact of Artificial Intelligence on Waiting Time for Medical Care in an Urgent Care Service for COVID-19: Single-Center Prospective Study |
title_full_unstemmed | The Impact of Artificial Intelligence on Waiting Time for Medical Care in an Urgent Care Service for COVID-19: Single-Center Prospective Study |
title_short | The Impact of Artificial Intelligence on Waiting Time for Medical Care in an Urgent Care Service for COVID-19: Single-Center Prospective Study |
title_sort | impact of artificial intelligence on waiting time for medical care in an urgent care service for covid-19: single-center prospective study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812142/ https://www.ncbi.nlm.nih.gov/pubmed/35103611 http://dx.doi.org/10.2196/29012 |
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