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Voice-assisted Artificial Intelligence-enabled Screening for Severe Acute Respiratory Syndrome Coronavirus 2 Exposure in Cardiovascular Clinics: Primary Results of the VOICE-COVID-19-II Randomized Trial
BACKGROUND: Voice-assisted artificial intelligence–based systems may streamline clinical care among patients with heart failure (HF) and caregivers; however, randomized clinical trials are needed. We evaluated the potential for Amazon Alexa (Alexa), a voice-assisted artificial intelligence–based sys...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10201196/ https://www.ncbi.nlm.nih.gov/pubmed/37224994 http://dx.doi.org/10.1016/j.cardfail.2023.05.004 |
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author | Sharma, ABHINAV MARQUES, PEDRO ZHANG, GUANG OULOUSIAN, EMILY CHUNG, SEOK HOON GANNI, ELIE LOPES, RENATO D. RAZAGHIZAD, AMIR AVRAM, ROBERT |
author_facet | Sharma, ABHINAV MARQUES, PEDRO ZHANG, GUANG OULOUSIAN, EMILY CHUNG, SEOK HOON GANNI, ELIE LOPES, RENATO D. RAZAGHIZAD, AMIR AVRAM, ROBERT |
author_sort | Sharma, ABHINAV |
collection | PubMed |
description | BACKGROUND: Voice-assisted artificial intelligence–based systems may streamline clinical care among patients with heart failure (HF) and caregivers; however, randomized clinical trials are needed. We evaluated the potential for Amazon Alexa (Alexa), a voice-assisted artificial intelligence–based system, to conduct screening for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a HF clinic. METHODS AND RESULTS: We enrolled 52 participants (patients and caregivers) from a HF clinic who were randomly assigned with a subsequent cross-over to receive a SARS-CoV-2 screening questionnaire via Alexa or health care personnel. The primary outcome was overall response concordance, as measured by the percentage of agreement and unweighted kappa scores between groups. A postscreening survey evaluated comfort with using the artificial intelligence–based device. In total, 36 participants (69%) were male, the median age was 51 years (range 34–65 years) years and 36 (69%) were English speaking. Twenty-one participants (40%) were patients with HF. For the primary outcome, there were no statistical differences between the groups: Alexa–research coordinator group 96.9% agreement and unweighted kappa score of 0.92 (95% confidence interval 0.84–1.00) vs research coordinator–Alexa group 98.5% agreement and unweighted kappa score of 0.95 (95% confidence interval 0.88-1.00) (P value for all comparisons > .05). Overall, 87% of participants rated their screening experience as good or outstanding. CONCLUSIONS: Alexa demonstrated comparable performance to a health care professional for SARS-CoV-2 screening in a group of patients with HF and caregivers and may represent an attractive approach to symptom screening in this population. Future studies evaluating such technologies for other uses among patients with HF and caregivers are warranted. NCT04508972 |
format | Online Article Text |
id | pubmed-10201196 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102011962023-05-22 Voice-assisted Artificial Intelligence-enabled Screening for Severe Acute Respiratory Syndrome Coronavirus 2 Exposure in Cardiovascular Clinics: Primary Results of the VOICE-COVID-19-II Randomized Trial Sharma, ABHINAV MARQUES, PEDRO ZHANG, GUANG OULOUSIAN, EMILY CHUNG, SEOK HOON GANNI, ELIE LOPES, RENATO D. RAZAGHIZAD, AMIR AVRAM, ROBERT J Card Fail Brief Report BACKGROUND: Voice-assisted artificial intelligence–based systems may streamline clinical care among patients with heart failure (HF) and caregivers; however, randomized clinical trials are needed. We evaluated the potential for Amazon Alexa (Alexa), a voice-assisted artificial intelligence–based system, to conduct screening for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a HF clinic. METHODS AND RESULTS: We enrolled 52 participants (patients and caregivers) from a HF clinic who were randomly assigned with a subsequent cross-over to receive a SARS-CoV-2 screening questionnaire via Alexa or health care personnel. The primary outcome was overall response concordance, as measured by the percentage of agreement and unweighted kappa scores between groups. A postscreening survey evaluated comfort with using the artificial intelligence–based device. In total, 36 participants (69%) were male, the median age was 51 years (range 34–65 years) years and 36 (69%) were English speaking. Twenty-one participants (40%) were patients with HF. For the primary outcome, there were no statistical differences between the groups: Alexa–research coordinator group 96.9% agreement and unweighted kappa score of 0.92 (95% confidence interval 0.84–1.00) vs research coordinator–Alexa group 98.5% agreement and unweighted kappa score of 0.95 (95% confidence interval 0.88-1.00) (P value for all comparisons > .05). Overall, 87% of participants rated their screening experience as good or outstanding. CONCLUSIONS: Alexa demonstrated comparable performance to a health care professional for SARS-CoV-2 screening in a group of patients with HF and caregivers and may represent an attractive approach to symptom screening in this population. Future studies evaluating such technologies for other uses among patients with HF and caregivers are warranted. NCT04508972 Elsevier Inc. 2023-05-22 /pmc/articles/PMC10201196/ /pubmed/37224994 http://dx.doi.org/10.1016/j.cardfail.2023.05.004 Text en © 2023 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Brief Report Sharma, ABHINAV MARQUES, PEDRO ZHANG, GUANG OULOUSIAN, EMILY CHUNG, SEOK HOON GANNI, ELIE LOPES, RENATO D. RAZAGHIZAD, AMIR AVRAM, ROBERT Voice-assisted Artificial Intelligence-enabled Screening for Severe Acute Respiratory Syndrome Coronavirus 2 Exposure in Cardiovascular Clinics: Primary Results of the VOICE-COVID-19-II Randomized Trial |
title | Voice-assisted Artificial Intelligence-enabled Screening for Severe Acute Respiratory Syndrome Coronavirus 2 Exposure in Cardiovascular Clinics: Primary Results of the VOICE-COVID-19-II Randomized Trial |
title_full | Voice-assisted Artificial Intelligence-enabled Screening for Severe Acute Respiratory Syndrome Coronavirus 2 Exposure in Cardiovascular Clinics: Primary Results of the VOICE-COVID-19-II Randomized Trial |
title_fullStr | Voice-assisted Artificial Intelligence-enabled Screening for Severe Acute Respiratory Syndrome Coronavirus 2 Exposure in Cardiovascular Clinics: Primary Results of the VOICE-COVID-19-II Randomized Trial |
title_full_unstemmed | Voice-assisted Artificial Intelligence-enabled Screening for Severe Acute Respiratory Syndrome Coronavirus 2 Exposure in Cardiovascular Clinics: Primary Results of the VOICE-COVID-19-II Randomized Trial |
title_short | Voice-assisted Artificial Intelligence-enabled Screening for Severe Acute Respiratory Syndrome Coronavirus 2 Exposure in Cardiovascular Clinics: Primary Results of the VOICE-COVID-19-II Randomized Trial |
title_sort | voice-assisted artificial intelligence-enabled screening for severe acute respiratory syndrome coronavirus 2 exposure in cardiovascular clinics: primary results of the voice-covid-19-ii randomized trial |
topic | Brief Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10201196/ https://www.ncbi.nlm.nih.gov/pubmed/37224994 http://dx.doi.org/10.1016/j.cardfail.2023.05.004 |
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