• Timothy J Judson, Anobel Y Odisho, Aaron B Neinstein, Jessica Chao, Aimee Williams, Christopher Miller, Tim Moriarty, Nathaniel Gleason, Gina Intinarelli, and Ralph Gonzales.
• Department of Medicine, University of California San Francisco, San Francisco, California.
• J Am Med Inform Assoc. 2020 Jun 1; 27 (6): 860-866.

ObjectiveTo rapidly deploy a digital patient-facing self-triage and self-scheduling tool in a large academic health system to address the COVID-19 pandemic.Materials And MethodsWe created a patient portal-based COVID-19 self-triage and self-scheduling tool and made it available to all primary care patients at the University of California, San Francisco Health, a large academic health system. Asymptomatic patients were asked about exposure history and were then provided relevant information. Symptomatic patients were triaged into 1 of 4 categories-emergent, urgent, nonurgent, or self-care-and then connected with the appropriate level of care via direct scheduling or telephone hotline.ResultsThis self-triage and self-scheduling tool was designed and implemented in under 2 weeks. During the first 16 days of use, it was completed 1129 times by 950 unique patients. Of completed sessions, 315 (28%) were by asymptomatic patients, and 814 (72%) were by symptomatic patients. Symptomatic patient triage dispositions were as follows: 193 emergent (24%), 193 urgent (24%), 99 nonurgent (12%), 329 self-care (40%). Sensitivity for detecting emergency-level care was 87.5% (95% CI 61.7-98.5%).DiscussionThis self-triage and self-scheduling tool has been widely used by patients and is being rapidly expanded to other populations and health systems. The tool has recommended emergency-level care with high sensitivity, and decreased triage time for patients with less severe illness. The data suggests it also prevents unnecessary triage messages, phone calls, and in-person visits.ConclusionPatient self-triage tools integrated into electronic health record systems have the potential to greatly improve triage efficiency and prevent unnecessary visits during the COVID-19 pandemic.© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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