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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.

Publication ,  Journal Article
Sharma, A; Marques, P; Zhang, G; Oulousian, E; Chung, SH; Ganni, E; Lopes, RD; Razaghizad, A; Avram, R
Published in: J Card Fail
October 2023

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.

Duke Scholars

Published In

J Card Fail

DOI

EISSN

1532-8414

Publication Date

October 2023

Volume

29

Issue

10

Start / End Page

1456 / 1460

Location

United States

Related Subject Headings

  • Cardiovascular System & Hematology
  • 3202 Clinical sciences
  • 3201 Cardiovascular medicine and haematology
  • 1110 Nursing
  • 1103 Clinical Sciences
  • 1102 Cardiorespiratory Medicine and Haematology
 

Citation

APA
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Sharma, A., Marques, P., Zhang, G., Oulousian, E., Chung, S. H., Ganni, E., … Avram, R. (2023). 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. J Card Fail, 29(10), 1456–1460. https://doi.org/10.1016/j.cardfail.2023.05.004
Sharma, Abhinav, Pedro Marques, Guang Zhang, Emily Oulousian, Seok Hoon Chung, Elie Ganni, Renato D. Lopes, Amir Razaghizad, and Robert Avram. “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.J Card Fail 29, no. 10 (October 2023): 1456–60. https://doi.org/10.1016/j.cardfail.2023.05.004.
Sharma A, Marques P, Zhang G, Oulousian E, Chung SH, Ganni E, Lopes RD, Razaghizad A, Avram R. 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. J Card Fail. 2023 Oct;29(10):1456–1460.
Journal cover image

Published In

J Card Fail

DOI

EISSN

1532-8414

Publication Date

October 2023

Volume

29

Issue

10

Start / End Page

1456 / 1460

Location

United States

Related Subject Headings

  • Cardiovascular System & Hematology
  • 3202 Clinical sciences
  • 3201 Cardiovascular medicine and haematology
  • 1110 Nursing
  • 1103 Clinical Sciences
  • 1102 Cardiorespiratory Medicine and Haematology