Delivering Healthcare through Teleconsultations: Implication on Offline Healthcare Disparity
50 Pages Posted: 19 Jun 2017 Last revised: 9 Nov 2020
Date Written: June 1, 2017
Teleconsultations allow patients to search, receive, and pay for medical consultations virtually. With its remote diagnosis and treatment capability, teleconsultations are proposed as a potential solution to a longstanding social problem, geographic disparities in healthcare. While it sounds promising, it is unclear whether teleconsultations actually mobilize healthcare access to underserved regions because there might be unforeseen frictions that suppress the virtual flow of healthcare. To understand the role of teleconsultations on geographic healthcare disparity, we first empirically investigate whether teleconsultations generate a virtual flow of healthcare to mitigate resource disparity. Second, we examine social, information, and geography frictions on the virtual healthcare flow. To this end, we curate unique longitudinal data that capture regional offline health resources and various regional characteristics and match them with teleconsultation instances over ten years (2006 ~ 2015). Our Exponential Random Graph Model analysis provides encouraging empirical evidence that teleconsultations connect physicians in resourceful regions and patients in underserved areas, a desirable direction that can alleviate geographic healthcare disparity. We find that social and information frictions, such as cultural and linguistic differences and limited media coverage, suppress the supposedly free flow of teleconsultations across regions. Further, while teleconsultation is anticipated to spark long-distance healthcare, we find that teleconsultations are less likely as regions become farther apart. We examine two plausible mechanisms that may contribute to the observed geography friction: (1) low information bandwidth of a teleconsultation channel, and (2) financial constraint of patients in underserved areas. Supplementary analyses using granular data (fees, physician ranks, and illness types) provide corroborating evidence to the proposed mechanisms.
Keywords: telemedicine, healthcare disparity, Exponential Random Graph Model
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