Dataset Information

Voice-Based Screening For SARS-CoV-2 Exposure In Cardiovascular Clinics

ABSTRACT: Abstract

Aims

Artificial intelligence (A.I) driven voice-based assistants may facilitate data capture in clinical care and trials; however, the feasibility and accuracy of using such devices in a healthcare environment are unknown. We explored the feasibility of using the Amazon Alexa (‘Alexa’) A.I. voice-assistant to screen for risk-factors or symptoms relating to SARS-CoV-2 exposure in quaternary care cardiovascular clinics.

Methods

We enrolled participants to be screened for signs and symptoms of SARS-CoV-2 exposure by a healthcare provider and then subsequently by the Alexa. Our primary outcome was interrater reliability of Alexa to healthcare provider screening using Cohen’s Kappa statistic. Participants rated the Alexa in a post-study survey (scale of 1 to 5 with 5 reflecting strongly agree). This study was approved by the McGill University Health Centre ethics board.

Results

We prospectively enrolled 215 participants. The mean age was 46 years (17.7 years standard deviation [SD]), 55% were female, and 31% were French speakers (others were English). In total, 645 screening questions were delivered by Alexa. The Alexa mis-identified one response. The simple and weighted Cohen’s kappa statistic between Alexa and healthcare provider screening was 0.989 (95% CI: 0.982, 0.997) and 0.992 (955 CI 0.985, 0.999) respectively. The participants gave an overall mean rating of 4.4 (out of 5, 0.9 SD).

Conclusion

Our study demonstrates the feasibility of an A.I. driven multilingual voice-based assistant to collect data in the context of SARS-CoV-2 exposure screening. Future studies integrating such devices in cardiovascular healthcare delivery and clinical trials are warranted.

Registration

https://clinicaltrials.gov/ct2/show/NCT04508972 Graphical Abstract Graphical Abstract

SUBMITTER: Sharma A

PROVIDER: S-EPMC8344943 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe COVID-19 pandemic has disrupted the health care system, limiting health care resources such as the availability of health care professionals, patient monitoring, contact tracing, and continuous surveillance. As a result of this significant burden, digital tools have become an important asset in increasing the efficiency of patient care delivery. Digital tools can help support health care institutions by tracking transmission of the virus, aiding in the screening process, and providing telemedicine support. However, digital health tools face challenges associated with barriers to accessibility, efficiency, and privacy-related ethical issues.ObjectiveThis paper describes the study design of an open-label, noninterventional, crossover, randomized controlled trial aimed at assessing whether interactive voice response systems can screen for SARS-CoV-2 in patients as accurately as standard screening done by people. The study aims to assess the concordance and interrater reliability of symptom screening done by Amazon Alexa compared to manual screening done by research coordinators. The perceived level of comfort of patients when interacting with voice response systems and their personal experience will also be evaluated.MethodsA total of 52 patients visiting the heart failure clinic at the Royal Victoria Hospital of the McGill University Health Center, in Montreal, Quebec, will be recruited. Patients will be randomly assigned to first be screened for symptoms of SARS-CoV-2 either digitally, by Amazon Alexa, or manually, by the research coordinator. Participants will subsequently be crossed over and screened either digitally or manually. The clinical setup includes an Amazon Echo Show, a tablet, and an uninterrupted power supply mounted on a mobile cart. The primary end point will be the interrater reliability on the accuracy of randomized screening data performed by Amazon Alexa versus research coordinators. The secondary end point will be the perceived level of comfort and app engagement of patients as assessed using 5-point Likert scales and binary mode responses.ResultsData collection started in May 2021 and is expected to be completed in fall 2022. Data analysis is expected to be completed in early 2023.ConclusionsThe use of voice-based assistants could improve the provision of health services and reduce the burden on health care personnel. Demonstrating a high interrater reliability between Amazon Alexa and health care coordinators may serve future digital tools to streamline the screening and delivery of care in the context of other conditions and clinical settings. The COVID-19 pandemic occurs during the first digital era using digital tools such as Amazon Alexa for disease screening, and it represents an opportunity to implement such technology in health care institutions in the long term.Trial registrationClinicalTrials.gov NCT04508972; https://clinicaltrials.gov/ct2/show/NCT04508972.International registered report identifier (irrid)DERR1-10.2196/41209.