JMIR Publications

ABSTRACT

Background:

The COVID-19 pandemic has disrupted the healthcare system, limiting healthcare resources such as the availability of healthcare professionals, patient monitoring, contact tracing, and continuous surveillance. As a result of this significant burden, digital tools can become an important asset in increasing efficiency of patient care delivery.

Objective:

This paper describes the study design of an open-label, non-interventional, cross-over, randomized controlled trial assessing whether voice-based screening can detect SARS-CoV-2 in patients as accurately and efficiently as screening by healthcare coordinators.

Methods:

A 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 then subsequently cross-over and be screened by either digitally or manually. The primary endpoint is the inter-rater reliability on the accuracy of randomized screening data performed by Amazon Alexa versus to standard healthcare coordinator. The secondary endpoint is the perceived level of comfort and app engagement of patients with 5-point Likert scales and binary mode responses.

Results:

The study is currently recruiting participants and the anticipated completion date is in September 2022.

Conclusions:

The use of voice-based assistants could improve the provision of health services and reduce the burden on healthcare personnel. Demonstrating a high inter-rater reliability between Amazon Alexa and healthcare coordinators may serve future digital-based tools to streamline screening and delivery of care in the context of other conditions and clinical settings. Clinical Trial: The trial is registered NCT04508972.