Understanding Twitter Telehealth Communication during the COVID-19 Pandemic using Hetero-Functional Graph Theory

Abstract

The active COVID-19 pandemic continues to impose a significant burden on healthcare delivery systems around the world. In the United States, temporary reimbursement policy amendments forged significant changes to telehealth adoption, seemingly overnight. Policy changes now allow beneficiaries in all areas of the country to receive telehealth services, including in their home. Healthcare delivery systems and providers have had to adjust to meet the need for telehealth expansion. Significant funding poured into telehealth research, technology, and the healthcare delivery landscape, yet it is unclear how temporary these policies will be. Significant telehealth adoption casts strong implications for healthcare delivery in smart cities, which are in a position to leverage other technologies on top of the telehealth delivery model. With such a significant change to the healthcare delivery landscape, we sought to understand the public discourse around the telehealth topic through the Twitter social media platform. In this paper, we seek to characterize telehealth related Twitter communication patterns. We show that while classic Twitter social network analysis does not conceptually characterize the communication, hetero-functional graph theory analysis instead provides deeper insights about the dynamic communication behavior of telehealth in social media.