Testing the Acceptability and Usability of an AI-Enabled COVID-19 Diagnostic Tool Among Diverse Adult Populations in the United States. (January 2023)

• Record Type: Journal Article
• Authors: Schilling, Josh; Moeller, F. Gerard; Peterson, Rachele; Beltz, Brandon; Joshi, Deepti; Gartner, Danielle; Vang, Jee; Jain, Praduman
• Is Part Of: Quality management in health care. Volume 32(2023)Supplement 1
• Page Start: S35
• Page End: S44
• Publication Date: 2023-01
• Subjects: acceptability -- COVID-19 -- testing -- usability -- web-based screening; Medical care -- Quality control -- Periodicals; Total quality management -- Periodicals; Health services administration -- Periodicals; 362.1068
• DOI: 10.1097/QMH.0000000000000396
• Languages: English
• ISSNs: 1063-8628

Abstract

Abstract : Background and Objectives: Although at-home coronavirus disease-2019 (COVID-19) testing offers several benefits in a relatively cost-effective and less risky manner, evidence suggests that at-home COVID-19 test kits have a high rate of false negatives. One way to improve the accuracy and acceptance of COVID-19 screening is to combine existing at-home physical test kits with an easily accessible, electronic, self-diagnostic tool. The objective of the current study was to test the acceptability and usability of an artificial intelligence (AI)-enabled COVID-19 testing tool that combines a web-based symptom diagnostic screening survey and a physical at-home test kit to test differences across adults from varying races, ages, genders, educational, and income levels in the United States. Methods: A total of 822 people from Richmond, Virginia, were included in the study. Data were collected from employees and patients of Virginia Commonwealth University Health Center as well as the surrounding community in June through October 2021. Data were weighted to reflect the demographic distribution of patients in United States. Descriptive statistics and repeated independent t tests were run to evaluate the differences in the acceptability and usability of an AI-enabled COVID-19 testing tool. Results: Across all participants, there was a reasonable degree of acceptability and usability of the AI-enabled COVID-19 testing tool that included a physical test kit and symptom screening website. The AI-enabled COVID-19 testing tool demonstrated overall good acceptability and usability across race, age, gender, and educational background. Notably, participants preferred both components of the AI-enabled COVID-19 testing tool to the in-clinic testing. Conclusion: Overall, these findings suggest that our AI-enabled COVID-19 testing approach has great potential to improve the quality of remote COVID testing at low cost and high accessibility for diverse demographic populations in the United States.