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Building and implementing a contactless clinical trial protocol for patients with COVID-19: A Korean perspective

Cited 1 time in Web of Science Cited 3 time in Scopus
Authors

Bae, Ye Seul; Sung, Sumi; Lee, Jungeun; Lee, Hyeonji; Chie, Eui Kyu

Issue Date
2022-09
Publisher
Frontiers Media S.A.
Citation
Frontiers in Medicine, Vol.9, p. 975243
Abstract
IntroductionTo effectively manage patients with coronavirus disease 2019 (COVID-19) while minimizing contact between medical staff, clinical trial protocol that facilitates contactless patient management was designed to predict deterioration of disease condition and monitor mental health status. MethodsThrough consultation with infectious disease specialists and psychiatrists, this study identified main clinical indicators related to respiratory and non-respiratory outcomes, and mental health. Telehealth devices that could collect relevant data indicators were explored. The following three modes were identified: wearable devices, video calls, and online questionnaires. Clinical trial protocol was implemented to patients confirmed with COVID-19 infection and admitted to Seongnam residential treatment centers between September 1, 2021 and December 30, 2021. Data were collected from wearable devices, video calls, online questionnaires, and from electronic health records. Participant satisfaction was assessed through an online survey at the time of discharge. ResultsIn total, 120 asymptomatic and mildly symptomatic COVID-19 patients participated in this trial. Seven types of physiological and life log data were collected from 87 patients using wearable devices, video and audio recordings, and online mental health-related questionnaire. Most participants were satisfied with the overall trial process, but perceived difficulties in using telehealth devices. ConclusionThis trial collected simultaneously generated multimodal patient data using various telehealth devices in a contactless setting for COVID-19 patients. Data collected in this study will be used to build a remote patient management system based on the prediction algorithms.
ISSN
2296-858X
URI
https://hdl.handle.net/10371/186176
DOI
https://doi.org/10.3389/fmed.2022.975243
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