Salaar Liaqat
University of Toronto, Toronto, Ontario, Canada
Remote patient monitoring is becoming increasingly instrumental to healthcare delivery but can substantially hamper the interpersonal communication that underlies standard clinical practice. In this work, we explore the benefits imparted to patients, clinicians, and researchers by an asynchronous messaging feature within a platform called COVIDFree@Home. We created COVIDFree@Home to assist the healthcare system in a large metropolitan city in North America during the COVID-19 pandemic. Clinicians used COVIDFree@Home to monitor the self-reported symptoms and vital signs of over 350 COVID-19 patients post-infection. Using thematic analysis of user-initiated messages, we found the messaging feature helped maintain protocol adherence while allowing patients to ask questions about their health and clinicians to convey empathetic care. This feedback cycle also led to higher quality data for hospitalization prediction, as the revisions significantly improved the AUROC of a machine learning model trained on demographic variables, vital signs data, and self-reported symptoms from 0.53 to 0.59.
doi.org/10.1145/3613904.3642630
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)