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

Sharma, Abhinav; Oulousian, Emily; Ni, Jiayi; Lopes, Renato; Cheng, Matthew Pellan; Label, Julie; Henriques, Filipe; Lighter, Claudia; Giannetti, Nadia; Avram, Robert

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